Selective visual scaling of time-scale processes facilitates broadband learning of isometric force frequency tracking.

Science.gov (United States)

King, Adam C; Newell, Karl M

2015-10-01

The experiment investigated the effect of selectively augmenting faster time scales of visual feedback information on the learning and transfer of continuous isometric force tracking tasks to test the generality of the self-organization of 1/f properties of force output. Three experimental groups tracked an irregular target pattern either under a standard fixed gain condition or with selectively enhancement in the visual feedback display of intermediate (4-8 Hz) or high (8-12 Hz) frequency components of the force output. All groups reduced tracking error over practice, with the error lowest in the intermediate scaling condition followed by the high scaling and fixed gain conditions, respectively. Selective visual scaling induced persistent changes across the frequency spectrum, with the strongest effect in the intermediate scaling condition and positive transfer to novel feedback displays. The findings reveal an interdependence of the timescales in the learning and transfer of isometric force output frequency structures consistent with 1/f process models of the time scales of motor output variability.

Does learning or instinct shape habitat selection?

Directory of Open Access Journals (Sweden)

Scott E Nielsen

Full Text Available Habitat selection is an important behavioural process widely studied for its population-level effects. Models of habitat selection are, however, often fit without a mechanistic consideration. Here, we investigated whether patterns in habitat selection result from instinct or learning for a population of grizzly bears (Ursus arctos in Alberta, Canada. We found that habitat selection and relatedness were positively correlated in female bears during the fall season, with a trend in the spring, but not during any season for males. This suggests that habitat selection is a learned behaviour because males do not participate in parental care: a genetically predetermined behaviour (instinct would have resulted in habitat selection and relatedness correlations for both sexes. Geographic distance and home range overlap among animals did not alter correlations indicating that dispersal and spatial autocorrelation had little effect on the observed trends. These results suggest that habitat selection in grizzly bears are partly learned from their mothers, which could have implications for the translocation of wildlife to novel environments.

Joint Feature Selection and Classification for Multilabel Learning.

Science.gov (United States)

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

Learning strategies during fear conditioning

OpenAIRE

Carpenter, Russ E.; Summers, Cliff H.

2009-01-01

This paper describes a model of fear learning, in which subjects have an option of behavioral responses to impending social defeat. The model generates two types of learning: social avoidance and classical conditioning, dependent upon 1) escape from or 2) social subordination to an aggressor. We hypothesized that social stress provides the impetus as well as the necessary information to stimulate dichotomous goal-oriented learning. Specialized tanks were constructed to subject rainbow trout t...

Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA – A statistical learning approach

Directory of Open Access Journals (Sweden)

R. Jegadeeshwaran

2015-03-01

Full Text Available In automobile, brake system is an essential part responsible for control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the vehicle cum passenger's safety. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is essential. Vibration based condition monitoring using machine learning techniques are gaining momentum. This study is one such attempt to perform the condition monitoring of a hydraulic brake system through vibration analysis. In this research, the performance of a Clonal Selection Classification Algorithm (CSCA for brake fault diagnosis has been reported. A hydraulic brake system test rig was fabricated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signal. The best feature set was identified for classification using attribute evaluator. The selected features were then classified using CSCA. The classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed. The Clonal Selection Classification Algorithm performs better and gives the maximum classification accuracy (96% for the fault diagnosis of a hydraulic brake system.

Rapid learning dynamics in individual honeybees during classical conditioning

Directory of Open Access Journals (Sweden)

Evren ePamir

2014-09-01

Full Text Available Associative learning in insects has been studied extensively by a multitude of classical conditioning protocols. However, so far little emphasis has been put on the dynamics of learning in individuals. The honeybee is a well-established animal model for learning and memory. We here studied associative learning as expressed in individual behavior based on a large collection of data on olfactory classical conditioning (25 datasets, 3,298 animals. We show that the group-averaged learning curve and memory retention score confound three attributes of individual learning: the ability or inability to learn a given task, the generally fast acquisition of a conditioned response in learners, and the high stability of the conditioned response during consecutive training and memory retention trials. We reassessed the prevailing view that more training results in better memory performance and found that 24h memory retention can be indistinguishable after single-trial and multiple-trial conditioning in individuals. We explain how inter-individual differences in learning can be accommodated within the Rescorla-Wagner theory of associative learning. In both data-analysis and modeling we demonstrate how the conflict between population-level and single-animal perspectives on learning and memory can be disentangled.

How Important Are Student-Selected versus Instructor-Selected Literature Resources for Students' Learning and Motivation in Problem-Based Learning?

Science.gov (United States)

Wijnia, Lisette; Loyens, Sofie M.; Derous, Eva; Schmidt, Henk G.

2015-01-01

In problem-based learning students are responsible for their own learning process, which becomes evident when they must act independently, for example, when selecting literature resources for individual study. It is a matter of debate whether it is better to have students select their own literature resources or to present them with a list of…

Efficient abstraction selection in reinforcement learning

NARCIS (Netherlands)

Seijen, H. van; Whiteson, S.; Kester, L.

2013-01-01

This paper introduces a novel approach for abstraction selection in reinforcement learning problems modelled as factored Markov decision processes (MDPs), for which a state is described via a set of state components. In abstraction selection, an agent must choose an abstraction from a set of

The conditions that promote fear learning: prediction error and Pavlovian fear conditioning.

Science.gov (United States)

Li, Susan Shi Yuan; McNally, Gavan P

2014-02-01

A key insight of associative learning theory is that learning depends on the actions of prediction error: a discrepancy between the actual and expected outcomes of a conditioning trial. When positive, such error causes increments in associative strength and, when negative, such error causes decrements in associative strength. Prediction error can act directly on fear learning by determining the effectiveness of the aversive unconditioned stimulus or indirectly by determining the effectiveness, or associability, of the conditioned stimulus. Evidence from a variety of experimental preparations in human and non-human animals suggest that discrete neural circuits code for these actions of prediction error during fear learning. Here we review the circuits and brain regions contributing to the neural coding of prediction error during fear learning and highlight areas of research (safety learning, extinction, and reconsolidation) that may profit from this approach to understanding learning. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Spatial Learning: Conditions and Basic Effects

Directory of Open Access Journals (Sweden)

Victoria D. Chamizo

2002-01-01

Full Text Available A growing body of evidence suggests that the spatial and the temporal domains seem to share the same or similar conditions, basic effects, and mechanisms. The blocking, unblocking and overshadowing experiments (and also those of latent inhibition and perceptual learning reviewed by Prados and Redhead in this issue show that to exclude associative learning as a basic mechanism responsible for spatial learning is quite inappropriate. All these results, especially those obtained with strictly spatial tasks, seem inconsistent with O’Keefe and Nadel’s account of true spatial learning or locale learning. Their theory claims that this kind of learning is fundamentally different and develops with total independence from other ways of learning (like classical and instrumental conditioning -taxon learning. In fact, the results reviewed can be explained appealing on to a sophisticated guidance system, like for example the one proposed by Leonard and McNaughton (1990; see also McNaughton and cols, 1996. Such a system would allow that an animal generates new space information: given the distance and address from of A to B and from A to C, being able to infer the distance and the address from B to C, even when C is invisible from B (see Chapuis and Varlet, 1987 -the contribution by McLaren in this issue constitutes a good example of a sophisticated guidance system.

Rapid learning dynamics in individual honeybees during classical conditioning.

Science.gov (United States)

Pamir, Evren; Szyszka, Paul; Scheiner, Ricarda; Nawrot, Martin P

2014-01-01

Associative learning in insects has been studied extensively by a multitude of classical conditioning protocols. However, so far little emphasis has been put on the dynamics of learning in individuals. The honeybee is a well-established animal model for learning and memory. We here studied associative learning as expressed in individual behavior based on a large collection of data on olfactory classical conditioning (25 datasets, 3298 animals). We show that the group-averaged learning curve and memory retention score confound three attributes of individual learning: the ability or inability to learn a given task, the generally fast acquisition of a conditioned response (CR) in learners, and the high stability of the CR during consecutive training and memory retention trials. We reassessed the prevailing view that more training results in better memory performance and found that 24 h memory retention can be indistinguishable after single-trial and multiple-trial conditioning in individuals. We explain how inter-individual differences in learning can be accommodated within the Rescorla-Wagner theory of associative learning. In both data-analysis and modeling we demonstrate how the conflict between population-level and single-animal perspectives on learning and memory can be disentangled.

Robot soccer action selection based on Q learning

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

This paper researches robot soccer action selection based on Q learning . The robot learn to activate particular behavior given their current situation and reward signal. We adopt neural network to implementations of Q learning for their generalization properties and limited computer memory requirements

Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

2017-01-01

With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

Rapid e-Learning Tools Selection Process for Cognitive and Psychomotor Learning Objectives

Science.gov (United States)

Ku, David Tawei; Huang, Yung-Hsin

2012-01-01

This study developed a decision making process for the selection of rapid e-learning tools that could match different learning domains. With the development of the Internet, the speed of information updates has become faster than ever. E-learning has rapidly become the mainstream for corporate training and academic instruction. In order to reduce…

Evolution of conditional cooperation under multilevel selection.

Science.gov (United States)

Zhang, Huanren; Perc, Matjaž

2016-03-11

We study the emergence of conditional cooperation in the presence of both intra-group and inter-group selection. Individuals play public goods games within their groups using conditional strategies, which are represented as piecewise linear response functions. Accordingly, groups engage in conflicts with a certain probability. In contrast to previous studies, we consider continuous contribution levels and a rich set of conditional strategies, allowing for a wide range of possible interactions between strategies. We find that the existence of conditional strategies enables the stabilization of cooperation even under strong intra-group selection. The strategy that eventually dominates in the population has two key properties: (i) It is unexploitable with strong intra-group selection; (ii) It can achieve full contribution to outperform other strategies in the inter-group selection. The success of this strategy is robust to initial conditions as well as changes to important parameters. We also investigate the influence of different factors on cooperation levels, including group conflicts, group size, and migration rate. Their effect on cooperation can be attributed to and explained by their influence on the relative strength of intra-group and inter-group selection.

Informal Workplace Learning among Nurses: Organisational Learning Conditions and Personal Characteristics That Predict Learning Outcomes

Science.gov (United States)

Kyndt, Eva; Vermeire, Eva; Cabus, Shana

2016-01-01

Purpose: This paper aims to examine which organisational learning conditions and individual characteristics predict the learning outcomes nurses achieve through informal learning activities. There is specific relevance for the nursing profession because of the rapidly changing healthcare systems. Design/Methodology/Approach: In total, 203 nurses…

Feature and Region Selection for Visual Learning.

Science.gov (United States)

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Word learning emerges from the interaction of online referent selection and slow associative learning

Science.gov (United States)

McMurray, Bob; Horst, Jessica S.; Samuelson, Larissa K.

2013-01-01

Classic approaches to word learning emphasize the problem of referential ambiguity: in any naming situation the referent of a novel word must be selected from many possible objects, properties, actions, etc. To solve this problem, researchers have posited numerous constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative model in which referent selection is an online process that is independent of long-term learning. This two timescale approach creates significant power in the developing system. We illustrate this with a dynamic associative model in which referent selection is simulated as dynamic competition between competing referents, and learning is simulated using associative (Hebbian) learning. This model can account for a range of findings including the delay in expressive vocabulary relative to receptive vocabulary, learning under high degrees of referential ambiguity using cross-situational statistics, accelerating (vocabulary explosion) and decelerating (power-law) learning rates, fast-mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between individual differences in speed of processing and learning. Five theoretical points are illustrated. 1) Word learning does not require specialized processes – general association learning buttressed by dynamic competition can account for much of the literature. 2) The processes of recognizing familiar words are not different than those that support novel words (e.g., fast-mapping). 3) Online competition may allow the network (or child) to leverage information available in the task to augment performance or behavior despite what might be relatively slow learning or poor representations. 4) Even associative learning is more complex than previously thought – a major contributor to performance is the pruning of incorrect associations

Learning Conditions, Members' Motivation and Satisfaction: A Multilevel Analysis

Science.gov (United States)

Dimas, Isabel Dórdio; Rebelo, Teresa; Lourenço, Paulo Renato

2015-01-01

Purpose: The purpose of this paper was to contribute to the clarification of the conditions under which teams can be successful, especially those related to team learning. To attain this goal, in the present study, the mediating role played by team members' motivation on the relationship between team learning conditions (shared learning beliefs…

Learning conditional Gaussian networks

DEFF Research Database (Denmark)

Bøttcher, Susanne Gammelgaard

This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....

An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

Directory of Open Access Journals (Sweden)

Xiaohong Li

2018-03-01

Full Text Available Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices. Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.

When congruence breeds preference: the influence of selective attention processes on evaluative conditioning.

Science.gov (United States)

Blask, Katarina; Walther, Eva; Frings, Christian

2017-09-01

We investigated in two experiments whether selective attention processes modulate evaluative conditioning (EC). Based on the fact that the typical stimuli in an EC paradigm involve an affect-laden unconditioned stimulus (US) and a neutral conditioned stimulus (CS), we started from the assumption that learning might depend in part upon selective attention to the US. Attention to the US was manipulated by including a variant of the Eriksen flanker task in the EC paradigm. Similarly to the original Flanker paradigm, we implemented a target-distracter logic by introducing the CS as the task-relevant stimulus (i.e. the target) to which the participants had to respond and the US as a task-irrelevant distracter. Experiment 1 showed that CS-US congruence modulated EC if the CS had to be selected against the US. Specifically, EC was more pronounced for congruent CS-US pairs as compared to incongruent CS-US pairs. Experiment 2 disentangled CS-US congruence and CS-US compatibility and suggested that it is indeed CS-US stimulus congruence rather than CS-US response compatibility that modulates EC.

Conditional discrimination learning: A critique and amplification

OpenAIRE

Schrier, Allan M.; Thompson, Claudia R.

1980-01-01

Carter and Werner recently reviewed the literature on conditional discrimination learning by pigeons, which consists of studies of matching-to-sample and oddity-from-sample. They also discussed three models of such learning: the “multiple-rule” model (learning of stimulus-specific relations), the “configuration” model, and the “single-rule” model (concept learning). Although their treatment of the multiple-rule model, which seems most applicable to the pigeon data, is generally excellent, the...

Selection of appropriates E-learning personalization strategies from ontological perspectives

Directory of Open Access Journals (Sweden)

Fathi Essalmi

2010-10-01

Full Text Available When there are several personalization strategies of E-learning, authors of courses need to be supported for deciding which strategy will be applied for personalizing each course. In fact, the time, the efforts and the learning objects needed for preparing personalized learning scenarios depend on the personalization strategy to be applied. This paper presents an approach for selecting personalization strategies according to the feasibility of generating personalized learning scenarios with minimal intervention of the author. Several metrics are proposed for putting in order and selecting useful personalization strategies. The calculus of these metrics is automated based on the analyses of the LOM (Learning Object Metadata standard according to the semantic relations between data elements and learners’ characteristics represented in the Ontology for Selection of Personalization Strategies (OSPS.

Associative learning in humans--conditioning of sensory-evoked brain activity.

Science.gov (United States)

Skrandies, W; Jedynak, A

2000-01-01

A classical conditioning paradigm was employed in two experiments performed on 35 human volunteers. In nine subjects, the presentation of Landolt rings (conditioned stimuli, CS + ) was paired with an electric stimulus (unconditioned stimuli, UCS) applied to the left median nerve. Neutral visual control stimuli were full circles (CS -) that were not paired with the UCS. The skin conductance response (SCR) was determined in a time interval of 5 s after onset of the visual stimuli, and it was measured in the acquisition and test phase. Associative learning was reflected by a SCR occurring selectively with CS +. The same experiment was repeated with another group of 26 adults while electroencephalogram (EEG) was recorded from 30 electrodes. For each subject, mean evoked potentials were computed. In 13 of the subjects, a conditioning paradigm was followed while the other subjects served as the control group (non-contingent stimulation). There were somatosensory and visual brain activity evoked by the stimuli. Conditioned components were identified by computing cross-correlation between evoked somatosensory components and the averaged EEG. In the visual evoked brain activity, three components with mean latencies of 105.4, 183.2, and 360.3 ms were analyzed. Somatosensory stimuli were followed by major components that occurred at mean latencies of 48.8, 132.5, 219.7, 294.8, and 374.2 ms latency after the shock. All components were analyzed in terms of latency, field strength, and topographic characteristics, and were compared between groups and experimental conditions. Both visual and somatosensory brain activity was significantly affected by classical conditioning. Our data illustrate how associative learning affects the topography of brain electrical activity elicited by presentation of conditioned visual stimuli.

Some psycholinguistic conditions for second language learning

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Bernard Spolsky

2013-02-01

Full Text Available The author discusses some psycho linguistic conditions for second language learning based on a preference rr ode! in linguistics. The outcome of second language learning depends on a number of conditions. Second language learning takes place in a social context, and social conditions determine a learner's attitudes. These attitudes are twofold in nature, namely those towards the community speaking the target language and those towards the learning situation. The two kinds of attitudes lead to motivation. The social context also provides opportunities for language learning and can be divided into formal and informal situations. There are also individual conditions of the learner. The author is concerned with the exploration of several specific psycholinguistic factors, as well as the kinds of rules which they contribute to the theory. Die skrywer bespreek enkele psigolinguistiese voorwaardes vir die aanleer van 'n tweede taal, gebaseer op 'n voorkeurmodel in die l!nguistiek. Die aanleer van 'n tweede taal geskied bin ne 'n sosiale konteks, en sosiale omstandighede bepaal 'n leerder se houding. Hierdie houding kan bestaan ten opsigte van die gemeenskap wat die teikentaal praat, sowel as ten opsigte van die leersituasie. Motivering word bepaal deur hierdie tweeledige houding. Die sosiale konteks bepaal ook geleenthede vir die aanleer van 'n taal en kan verdeel word in forme le en informele situasies. Verder is daar die individuele omstandighede van elke leerder. Die skrywer hou horn besig met 'n verkenning van spesifieke psigolinguistiese faktore, sowel as die soort reels wat hydra tot die teorie.

Computerized adaptive testing item selection in computerized adaptive learning systems

NARCIS (Netherlands)

Eggen, Theodorus Johannes Hendrikus Maria; Eggen, T.J.H.M.; Veldkamp, B.P.

2012-01-01

Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item

Embedded Incremental Feature Selection for Reinforcement Learning

Science.gov (United States)

2012-05-01

Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

Conditioning and learning in relation to disease.

Science.gov (United States)

Ban, T A; Guy, W

1985-12-01

Of the two generally recognized processes through which learning occurs--imprinting and conditioning--only the latter with its two paradigms, classical and operant, has both practical and heuristic implications for disease. From the classical conditioning experiments of Pavlov's laboratory over 100 years ago to the later work in operant conditioning by Skinner and others in the past four decades has evolved much of the basis of modern learning theory and its applications to disease in the form of behavior therapy. Variants of behavior therapy have been employed in the treatment of wide variety of medical and psychiatric illnesses. Recent developments in the study of brain function and biochemistry have led to renewed interest in the conditioning paradigm and its value as tool in these areas of research.

VALIDATION OF A SCALE OF LEVELS AND CONDITIONS OF ORGANIZATIONAL LEARNING

Directory of Open Access Journals (Sweden)

DELIO IGNACIO CASTAÑEDA

2007-08-01

Full Text Available Organizational learning has been studied from the perspective of levels of learning: individual, group and organizational,as well as from the needed conditions for learning in order to be produced. An instrument of six dimensions wasvalidated, three of them levels: individual, group and organizational, and three of them conditions: culture oforganizational learning, training and transmission of information. Participants were 845 workers of a public institution.From results support was found for the three levels of learning and for two conditions: culture of organizationallearning and training. Additionally a condition called strategic clarity was identified.

Sex differences in learning processes of classical and operant conditioning.

Science.gov (United States)

Dalla, Christina; Shors, Tracey J

2009-05-25

Males and females learn and remember differently at different times in their lives. These differences occur in most species, from invertebrates to humans. We review here sex differences as they occur in laboratory rodent species. We focus on classical and operant conditioning paradigms, including classical eyeblink conditioning, fear-conditioning, active avoidance and conditioned taste aversion. Sex differences have been reported during acquisition, retention and extinction in most of these paradigms. In general, females perform better than males in the classical eyeblink conditioning, in fear-potentiated startle and in most operant conditioning tasks, such as the active avoidance test. However, in the classical fear-conditioning paradigm, in certain lever-pressing paradigms and in the conditioned taste aversion, males outperform females or are more resistant to extinction. Most sex differences in conditioning are dependent on organizational effects of gonadal hormones during early development of the brain, in addition to modulation by activational effects during puberty and adulthood. Critically, sex differences in performance account for some of the reported effects on learning and these are discussed throughout the review. Because so many mental disorders are more prevalent in one sex than the other, it is important to consider sex differences in learning when applying animal models of learning for these disorders. Finally, we discuss how sex differences in learning continue to alter the brain throughout the lifespan. Thus, sex differences in learning are not only mediated by sex differences in the brain, but also contribute to them.

Learning a New Selection Rule in Visual and Frontal Cortex.

Science.gov (United States)

van der Togt, Chris; Stănişor, Liviu; Pooresmaeili, Arezoo; Albantakis, Larissa; Deco, Gustavo; Roelfsema, Pieter R

2016-08-01

How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the visual (area V1) and frontal cortex (area FEF) of monkeys. We devised a new icon-selection task. On each day, the monkeys saw 2 new icons (small pictures) and learned which one was relevant. We rewarded eye movements to a saccade target connected to the relevant icon with a curve. Neurons in visual and frontal cortex coded the monkey's choice, because the representation of the selected curve was enhanced. Learning delayed the neuronal selection signals and we uncovered the cause of this delay in V1, where learning to select the relevant icon caused an early suppression of surrounding image elements. These results demonstrate that the learning of a new rule causes a transition from fast and random decisions to a more considerate strategy that takes additional time and they reveal the contribution of visual and frontal cortex to the learning process. © The Author 2016. Published by Oxford University Press.

Second-order conditioning and conditioned inhibition: influences of speed versus accuracy on human causal learning.

Directory of Open Access Journals (Sweden)

Jessica C Lee

Full Text Available In human causal learning, excitatory and inhibitory learning effects can sometimes be found in the same paradigm by altering the learning conditions. This study aims to explore whether learning in the feature negative paradigm can be dissociated by emphasising speed over accuracy. In two causal learning experiments, participants were given a feature negative discrimination in which the outcome caused by one cue was prevented by the addition of another. Participants completed training trials either in a self-paced fashion with instructions emphasising accuracy, or under strict time constraints with instructions emphasising speed. Using summation tests in which the preventative cue was paired with another causal cue, participants in the accuracy groups correctly rated the preventative cue as if it reduced the probability of the outcome. However, participants in the speed groups rated the preventative cue as if it increased the probability of the outcome. In Experiment 1, both speed and accuracy groups later judged the same cue to be preventative in a reasoned inference task. Experiment 2 failed to find evidence of similar dissociations in retrospective revaluation (release from overshadowing vs. mediated extinction or learning about a redundant cue (blocking vs. augmentation. However in the same experiment, the tendency for the accuracy group to show conditioned inhibition and the speed group to show second-order conditioning was consistent even across sub-sets of the speed and accuracy groups with equivalent accuracy in training, suggesting that second-order conditioning is not merely a consequence of poorer acquisition. This dissociation mirrors the trade-off between second-order conditioning and conditioned inhibition observed in animal conditioning when training is extended.

The Role of Nucleus Accumbens Shell in Learning about Neutral versus Excitatory Stimuli during Pavlovian Fear Conditioning

Science.gov (United States)

Bradfield, Laura A.; McNally, Gavan P.

2010-01-01

We studied the role of nucleus accumbens shell (AcbSh) in Pavlovian fear conditioning. Rats were trained to fear conditioned stimulus A (CSA) in Stage I, which was then presented in compound with a neutral stimulus and paired with shock in Stage II. AcbSh lesions had no effect on fear-learning to CSA in Stage I, but selectively prevented learning…

Bioinspired Architecture Selection for Multitask Learning

Directory of Open Access Journals (Sweden)

Andrés Bueno-Crespo

2017-06-01

Full Text Available Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL, which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method.

LEARNING MATERIALS SELECTION FOR DIFFERENTIATED INSTRUCTION OF ENGLISH FOR SPECIFIC PURPOSES OF FUTURE PROFESSIONALS IN THE FIELD OF INFORMATION TECHNOLOGY

Directory of Open Access Journals (Sweden)

Oksana Synekop

2017-09-01

Full Text Available In conditions of differentiation the learning materials selection will optimize the training English for Specific Purposes of the future professionals in the field of information technology at university level. The purpose of the article is to define the basic unit of learning material, the factors of influence on the learning material selection, principles, criteria and the procedure of learning material selection in this paper. Reviewing the scientific achievements in the learning material selection in teaching English has become a basis for defining the factors of influence, principles and criteria in the research. The basic unit of learning material (learning English text for professional purposes is outlined. The factors of influence and principles (correspondence of learning materials to professional interests and needs of information technology students; necessary ability and accessibility; regarding the linguistic and stylistic necessity and sufficiency; availability of Internet sources information of the learning material selection are defined. Also, the qualitative criteria (authenticity; professional significance, relevance and informativeness; conformity of foreign language level and intellectual development of students; variety of genres and forms of speech, their sufficient filling by linguistic material; coherence, integrity, consistency, semantic completeness; topic conformity; situation conformity; unlimited access, reliability and exemplarity of Internet sources and the quantitative criteria (the amount of material of the learning material selection are highlighted. The process of English for Specific Purposes material selection (defining the disciplines of different cycles; defining spheres and related topics; outlining situations, communicative roles and intentions of professional communication; specifying the sources of selection; evaluating the texts; analysis of the knowledge, skills and sub-skills required for the

System Quality Characteristics for Selecting Mobile Learning Applications

Directory of Open Access Journals (Sweden)

Mohamed SARRAB

2015-10-01

Full Text Available The majority of M-learning (Mobile learning applications available today are developed for the formal learning and education environment. These applications are characterized by the improvement in the interaction between learners and instructors to provide high interaction and flexibility to the learning process. M-learning is gaining increased recognition and adoption by different organizations. With the high number of M-learning applications available today, making the right decision about which, application to choose can be quite challenging. To date there is no complete and well defined set of system characteristics for such M-learning applications. This paper presents system quality characteristics for selecting M-learning applications based on the result of a systematic review conducted in this domain.

A Machine LearningFramework to Forecast Wave Conditions

Science.gov (United States)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

Relevance feature selection of modal frequency-ambient condition pattern recognition in structural health assessment for reinforced concrete buildings

Directory of Open Access Journals (Sweden)

He-Qing Mu

2016-08-01

Full Text Available Modal frequency is an important indicator for structural health assessment. Previous studies have shown that this indicator is substantially affected by the fluctuation of ambient conditions, such as temperature and humidity. Therefore, recognizing the pattern between modal frequency and ambient conditions is necessary for reliable long-term structural health assessment. In this article, a novel machine-learning algorithm is proposed to automatically select relevance features in modal frequency-ambient condition pattern recognition based on structural dynamic response and ambient condition measurement. In contrast to the traditional feature selection approaches by examining a large number of combinations of extracted features, the proposed algorithm conducts continuous relevance feature selection by introducing a sophisticated hyperparameterization on the weight parameter vector controlling the relevancy of different features in the prediction model. The proposed algorithm is then utilized for structural health assessment for a reinforced concrete building based on 1-year daily measurements. It turns out that the optimal model class including the relevance features for each vibrational mode is capable to capture the pattern between the corresponding modal frequency and the ambient conditions.

Selective attention and recognition: effects of congruency on episodic learning.

Science.gov (United States)

Rosner, Tamara M; D'Angelo, Maria C; MacLellan, Ellen; Milliken, Bruce

2015-05-01

Recent research on cognitive control has focused on the learning consequences of high selective attention demands in selective attention tasks (e.g., Botvinick, Cognit Affect Behav Neurosci 7(4):356-366, 2007; Verguts and Notebaert, Psychol Rev 115(2):518-525, 2008). The current study extends these ideas by examining the influence of selective attention demands on remembering. In Experiment 1, participants read aloud the red word in a pair of red and green spatially interleaved words. Half of the items were congruent (the interleaved words had the same identity), and the other half were incongruent (the interleaved words had different identities). Following the naming phase, participants completed a surprise recognition memory test. In this test phase, recognition memory was better for incongruent than for congruent items. In Experiment 2, context was only partially reinstated at test, and again recognition memory was better for incongruent than for congruent items. In Experiment 3, all of the items contained two different words, but in one condition the words were presented close together and interleaved, while in the other condition the two words were spatially separated. Recognition memory was better for the interleaved than for the separated items. This result rules out an interpretation of the congruency effects on recognition in Experiments 1 and 2 that hinges on stronger relational encoding for items that have two different words. Together, the results support the view that selective attention demands for incongruent items lead to encoding that improves recognition.

Contingency learning in human fear conditioning involves the ventral striatum.

Science.gov (United States)

Klucken, Tim; Tabbert, Katharina; Schweckendiek, Jan; Merz, Christian Josef; Kagerer, Sabine; Vaitl, Dieter; Stark, Rudolf

2009-11-01

The ability to detect and learn contingencies between fearful stimuli and their predictive cues is an important capacity to cope with the environment. Contingency awareness refers to the ability to verbalize the relationships between conditioned and unconditioned stimuli. Although there is a heated debate about the influence of contingency awareness on conditioned fear responses, neural correlates behind the formation process of contingency awareness have gained only little attention in human fear conditioning. Recent animal studies indicate that the ventral striatum (VS) could be involved in this process, but in human studies the VS is mostly associated with positive emotions. To examine this question, we reanalyzed four recently published classical fear conditioning studies (n = 117) with respect to the VS at three distinct levels of contingency awareness: subjects, who did not learn the contingencies (unaware), subjects, who learned the contingencies during the experiment (learned aware) and subjects, who were informed about the contingencies in advance (instructed aware). The results showed significantly increased activations in the left and right VS in learned aware compared to unaware subjects. Interestingly, this activation pattern was only found in learned but not in instructed aware subjects. We assume that the VS is not involved when contingency awareness does not develop during conditioning or when contingency awareness is unambiguously induced already prior to conditioning. VS involvement seems to be important for the transition from a contingency unaware to a contingency aware state. Implications for fear conditioning models as well as for the contingency awareness debate are discussed.

Serial Entrepreneurship, Learning by Doing and Self-selection

DEFF Research Database (Denmark)

Rocha, Vera; Carneiro, Anabela; Varum, Celeste

2015-01-01

of the person-specific effect, using information on individuals’ past histories in paid employment, confirm that serial entrepreneurs exhibit, on average, a larger person-specific effect than non-serial business owners. Moreover, ignoring serial entrepreneurs’ self-selection overestimates learning by doing......It remains a question whether serial entrepreneurs typically perform better than their novice counterparts owing to learning by doing effects or mostly because they are a selected sample of higher-than-average ability entrepreneurs. This paper tries to unravel these two effects by exploring a novel...... empirical strategy based on continuous time duration models with selection. We use a large longitudinal matched employer-employee dataset that allows us to identify about 220,000 individuals who have left their first entrepreneurial experience, out of which over 35,000 became serial entrepreneurs. We...

Learning for Climate Change Adaptation among Selected ...

African Journals Online (AJOL)

Learning for Climate Change Adaptation among Selected Communities of Lusaka ... This research was aimed at surveying perceptions of climate change and ... This work is licensed under a Creative Commons Attribution 3.0 License.

Automatic learning-based beam angle selection for thoracic IMRT

International Nuclear Information System (INIS)

Amit, Guy; Marshall, Andrea; Purdie, Thomas G.; Jaffray, David A.; Levinshtein, Alex; Hope, Andrew J.; Lindsay, Patricia; Pekar, Vladimir

2015-01-01

Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume

Oxytocin selectively facilitates learning with social feedback and increases activity and functional connectivity in emotional memory and reward processing regions.

Science.gov (United States)

Hu, Jiehui; Qi, Song; Becker, Benjamin; Luo, Lizhu; Gao, Shan; Gong, Qiyong; Hurlemann, René; Kendrick, Keith M

2015-06-01

In male Caucasian subjects, learning is facilitated by receipt of social compared with non-social feedback, and the neuropeptide oxytocin (OXT) facilitates this effect. In this study, we have first shown a cultural difference in that male Chinese subjects actually perform significantly worse in the same reinforcement associated learning task with social (emotional faces) compared with non-social feedback. Nevertheless, in two independent double-blind placebo (PLC) controlled between-subject design experiments we found OXT still selectively facilitated learning with social feedback. Similar to Caucasian subjects this OXT effect was strongest with feedback using female rather than male faces. One experiment performed in conjunction with functional magnetic resonance imaging showed that during the response, but not feedback phase of the task, OXT selectively increased activity in the amygdala, hippocampus, parahippocampal gyrus and putamen during the social feedback condition, and functional connectivity between the amygdala and insula and caudate. Therefore, OXT may be increasing the salience and reward value of anticipated social feedback. In the PLC group, response times and state anxiety scores during social feedback were associated with signal changes in these same regions but not in the OXT group. OXT may therefore have also facilitated learning by reducing anxiety in the social feedback condition. Overall our results provide the first evidence for cultural differences in social facilitation of learning per se, but a similar selective enhancement of learning with social feedback under OXT. This effect of OXT may be associated with enhanced responses and functional connectivity in emotional memory and reward processing regions. © 2015 Wiley Periodicals, Inc.

Statistical learning of action: the role of conditional probability.

Science.gov (United States)

Meyer, Meredith; Baldwin, Dare

2011-12-01

Identification of distinct units within a continuous flow of human action is fundamental to action processing. Such segmentation may rest in part on statistical learning. In a series of four experiments, we examined what types of statistics people can use to segment a continuous stream involving many brief, goal-directed action elements. The results of Experiment 1 showed no evidence for sensitivity to conditional probability, whereas Experiment 2 displayed learning based on joint probability. In Experiment 3, we demonstrated that additional exposure to the input failed to engender sensitivity to conditional probability. However, the results of Experiment 4 showed that a subset of adults-namely, those more successful at identifying actions that had been seen more frequently than comparison sequences-were also successful at learning conditional-probability statistics. These experiments help to clarify the mechanisms subserving processing of intentional action, and they highlight important differences from, as well as similarities to, prior studies of statistical learning in other domains, including language.

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Science.gov (United States)

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Statistical learning and selective inference.

Science.gov (United States)

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Conditions of Practice in Perceptual Skill Learning

Science.gov (United States)

Memmert, D.; Hagemann, N.; Althoetmar, R.; Geppert, S.; Seiler, D.

2009-01-01

This study uses three experiments with different kinds of training conditions to investigate the "easy-to-hard" principle, context interference conditions, and feedback effects for learning anticipatory skills in badminton. Experiment 1 (N = 60) showed that a training program that gradually increases the difficulty level has no advantage over the…

The cost of selective attention in category learning: Developmental differences between adults and infants

Science.gov (United States)

Best, Catherine A.; Yim, Hyungwook; Sloutsky, Vladimir M.

2013-01-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6–8 months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. PMID:23773914

Pairwise Constraint-Guided Sparse Learning for Feature Selection.

Science.gov (United States)

Liu, Mingxia; Zhang, Daoqiang

2016-01-01

Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

Science.gov (United States)

Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

2017-11-01

Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

The cost of selective attention in category learning: developmental differences between adults and infants.

Science.gov (United States)

Best, Catherine A; Yim, Hyungwook; Sloutsky, Vladimir M

2013-10-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6-8months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. Copyright © 2013 Elsevier Inc. All rights reserved.

Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin.

Directory of Open Access Journals (Sweden)

Takahiro Ezaki

2016-07-01

Full Text Available Direct reciprocity, or repeated interaction, is a main mechanism to sustain cooperation under social dilemmas involving two individuals. For larger groups and networks, which are probably more relevant to understanding and engineering our society, experiments employing repeated multiplayer social dilemma games have suggested that humans often show conditional cooperation behavior and its moody variant. Mechanisms underlying these behaviors largely remain unclear. Here we provide a proximate account for this behavior by showing that individuals adopting a type of reinforcement learning, called aspiration learning, phenomenologically behave as conditional cooperator. By definition, individuals are satisfied if and only if the obtained payoff is larger than a fixed aspiration level. They reinforce actions that have resulted in satisfactory outcomes and anti-reinforce those yielding unsatisfactory outcomes. The results obtained in the present study are general in that they explain extant experimental results obtained for both so-called moody and non-moody conditional cooperation, prisoner's dilemma and public goods games, and well-mixed groups and networks. Different from the previous theory, individuals are assumed to have no access to information about what other individuals are doing such that they cannot explicitly use conditional cooperation rules. In this sense, myopic aspiration learning in which the unconditional propensity of cooperation is modulated in every discrete time step explains conditional behavior of humans. Aspiration learners showing (moody conditional cooperation obeyed a noisy GRIM-like strategy. This is different from the Pavlov, a reinforcement learning strategy promoting mutual cooperation in two-player situations.

Mixed-Handedness Advantages in Episodic Memory Obtained under Conditions of Intentional Learning Extend to Incidental Learning

Science.gov (United States)

Christman, Stephen D.; Butler, Michael

2011-01-01

The existence of handedness differences in the retrieval of episodic memories is well-documented, but virtually all have been obtained under conditions of intentional learning. Two experiments are reported that extend the presence of such handedness differences to memory retrieval under conditions of incidental learning. Experiment 1 used Craik…

Conditioning Factors for Group Management in Blended Learning Scenarios

NARCIS (Netherlands)

Pérez-Sanagustín, Mar; Hernández-Leo, Davinia; Blat, Josep

2009-01-01

Pérez-Sanagustín, M., Hernández-Leo D., & Blat, J. (accepted). Conditioning Factors for Group Management in Blended Learning Scenarios. The 9th IEEE International Conference on Advanced Learning Technologies. July, 14-18, 2009, Riga, Latvia.

Experienced teachers' informal workplace learning and perceptions of workplace conditions

NARCIS (Netherlands)

Hoekstra, A.; Korthagen, F.; Brekelmans, M.; Beijaard, D.; Imants, J.

2009-01-01

Purpose: The purpose of this paper is to explore in detail how teachers' perceptions of workplace conditions for learning are related to their informal workplace learning activities and learning outcomes. Design/methodology/approach: From a sample of 32 teachers, a purposeful sampling technique of

Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

Science.gov (United States)

Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

2008-01-01

Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

Learning Mixtures of Polynomials of Conditional Densities from Data

DEFF Research Database (Denmark)

L. López-Cruz, Pedro; Nielsen, Thomas Dyhre; Bielza, Concha

2013-01-01

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique for hybrid Bayesian networks with continuous and discrete variables. We propose two methods for learning MoP ap- proximations of conditional densities from data. Both approaches are based on learning MoP approximatio...

Selective Learning and Teaching among Japanese and German Children

Science.gov (United States)

Kim, Sunae; Paulus, Markus; Sodian, Beate; Itakura, Shoji; Ueno, Mika; Senju, Atsushi; Proust, Joëlle

2018-01-01

Despite an increasing number of studies demonstrating that young children selectively learn from others, and a few studies of children's selective teaching, the evidence almost exclusively comes from Western cultures, and cross-cultural comparison in this line of work is very rare. In the present research, we investigated Japanese and German…

Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

Science.gov (United States)

Yu, Jianbo

2016-11-01

The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

The chemotherapeutic agent paclitaxel selectively impairs reversal learning while sparing prior learning, new learning and episodic memory.

Science.gov (United States)

Panoz-Brown, Danielle; Carey, Lawrence M; Smith, Alexandra E; Gentry, Meredith; Sluka, Christina M; Corbin, Hannah E; Wu, Jie-En; Hohmann, Andrea G; Crystal, Jonathon D

2017-10-01

Chemotherapy is widely used to treat patients with systemic cancer. The efficacy of cancer therapies is frequently undermined by adverse side effects that have a negative impact on the quality of life of cancer survivors. Cancer patients who receive chemotherapy often experience chemotherapy-induced cognitive impairment across a variety of domains including memory, learning, and attention. In the current study, the impact of paclitaxel, a taxane derived chemotherapeutic agent, on episodic memory, prior learning, new learning, and reversal learning were evaluated in rats. Neurogenesis was quantified post-treatment in the dentate gyrus of the same rats using immunostaining for 5-Bromo-2'-deoxyuridine (BrdU) and Ki67. Paclitaxel treatment selectively impaired reversal learning while sparing episodic memory, prior learning, and new learning. Furthermore, paclitaxel-treated rats showed decreases in markers of hippocampal cell proliferation, as measured by markers of cell proliferation assessed using immunostaining for Ki67 and BrdU. This work highlights the importance of using multiple measures of learning and memory to identify the pattern of impaired and spared aspects of chemotherapy-induced cognitive impairment. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of an Interactive Technology Supported Problem-Based Learning STEM Project Using Selected Learning Sciences Interest Areas (SLSIA)

Science.gov (United States)

Kumar, David Devraj

2017-01-01

This paper reports an analysis of an interactive technology-supported, problem-based learning (PBL) project in science, technology, engineering and mathematics (STEM) from a Learning Sciences perspective using the Selected Learning Sciences Interest Areas (SLSIA). The SLSIA was adapted from the "What kinds of topics do ISLS [International…

Feature selection is the ReliefF for multiple instance learning

NARCIS (Netherlands)

Zafra, A.; Pechenizkiy, M.; Ventura, S.

2010-01-01

Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In

Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

Science.gov (United States)

Manouselis, Nikos; Sampson, Demetrios

This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

On a selection method of imaging condition in scintigraphy

International Nuclear Information System (INIS)

Ikeda, Hozumi; Kishimoto, Kenji; Shimonishi, Yoshihiro; Ohmura, Masahiro; Kosakai, Kazuhisa; Ochi, Hironobu

1992-01-01

Selection of imaging condition in scintigraphy was evaluated using analytic hierarchy process. First, a method of the selection was led by determining at the points of image quantity and imaging time. Influence of image quality was thought to depend on changes of system resolution, count density, image size, and image density. Also influence of imaging time was thought to depend on changes of system sensitivity and data acquisition time. Phantom study was done for paired comparison of these selection factors, and relations of sample data and the factors, that is Rollo phantom images were taken by changing count density, image size, and image density. Image quality was shown by calculating the score of visual evaluation that done by comparing of a pair of images in clearer cold lesion on the scintigrams. Imaging time was shown by relative values for changes of count density. However, system resolution and system sensitivity were constant in this study. Next, using these values analytic hierarchy process was adapted for this selection of imaging conditions. We conclude that this selection of imaging conditions can be analyzed quantitatively using analytic hierarchy process and this analysis develops theoretical consideration of imaging technique. (author)

Code-specific learning rules improve action selection by populations of spiking neurons.

Science.gov (United States)

Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

2014-08-01

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

How learning might strengthen existing visual object representations in human object-selective cortex.

Science.gov (United States)

Brants, Marijke; Bulthé, Jessica; Daniels, Nicky; Wagemans, Johan; Op de Beeck, Hans P

2016-02-15

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties. Copyright © 2015 Elsevier Inc. All rights reserved.

Variability in Second Language Learning: The Roles of Individual Differences, Learning Conditions, and Linguistic Complexity

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Tagarelli, Kaitlyn M.; Ruiz, Simón; Vega, José Luis Moreno; Rebuschat, Patrick

2016-01-01

Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This article examines the relationship between these three variables in language learners.…

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

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Lindsay, Grace W.

2017-01-01

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (�

A numeric comparison of variable selection algorithms for supervised learning

International Nuclear Information System (INIS)

Palombo, G.; Narsky, I.

2009-01-01

Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.

Creating conditions for cooperative learning: Basic elements

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Ševkušić-Mandić Slavica G.

2003-01-01

Full Text Available Although a large number of research evidence speak out in favor of cooperative learning, its effectiveness in teaching does not depend only on teacher’s and students’ enthusiasm and willingness to work in such a manner. Creating cooperative situations in learning demands a serious preparation and engagement on the part of teacher who is structuring various aspects of work in the classroom. Although there exist a large number of models and techniques of cooperative learning, which vary in the way in which students work together, in the structure of learning tasks as well as in the degree to which cooperative efforts of students are coupled with competition among groups, some elements should be present in the structure of conditions irrespective of the type of group work in question. Potential effects of cooperation are not likely to emerge unless teachers apply five basic elements of cooperative structure: 1. structuring of the learning task and students’ positive interdependence, 2. individual responsibility, 3. upgrading of "face to face" interaction, 4. training of students’ social skills, and 5. evaluation of group processes. The paper discusses various strategies for establishing the mentioned elements and concrete examples for teaching practice are provided, which should be of assistance to teachers for as much successful cooperative learning application as possible in work with children.

Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

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Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2018-01-01

Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

Science.gov (United States)

Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2017-09-01

Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven

Learning-dependent plasticity in human auditory cortex during appetitive operant conditioning.

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Puschmann, Sebastian; Brechmann, André; Thiel, Christiane M

2013-11-01

Animal experiments provide evidence that learning to associate an auditory stimulus with a reward causes representational changes in auditory cortex. However, most studies did not investigate the temporal formation of learning-dependent plasticity during the task but rather compared auditory cortex receptive fields before and after conditioning. We here present a functional magnetic resonance imaging study on learning-related plasticity in the human auditory cortex during operant appetitive conditioning. Participants had to learn to associate a specific category of frequency-modulated tones with a reward. Only participants who learned this association developed learning-dependent plasticity in left auditory cortex over the course of the experiment. No differential responses to reward predicting and nonreward predicting tones were found in auditory cortex in nonlearners. In addition, learners showed similar learning-induced differential responses to reward-predicting and nonreward-predicting tones in the ventral tegmental area and the nucleus accumbens, two core regions of the dopaminergic neurotransmitter system. This may indicate a dopaminergic influence on the formation of learning-dependent plasticity in auditory cortex, as it has been suggested by previous animal studies. Copyright © 2012 Wiley Periodicals, Inc.

Reinforcement Learning for Ramp Control: An Analysis of Learning Parameters

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Chao Lu

2016-08-01

Full Text Available Reinforcement Learning (RL has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestionsabout how to select suitable parameter values that can achieve a superior performance were provided.

Role of classical conditioning in learning gastrointestinal symptoms

OpenAIRE

Stockhorst, Ursula; Enck, Paul; Klosterhalfen, Sibylle

2007-01-01

Nausea and/or vomiting are aversive gastrointestinal (GI) symptoms. Nausea and vomiting manifest unconditionally after a nauseogenic experience. However, there is correlative, quasiexperimental and experimental evidence that nausea and vomiting can also be learned via classical (Pavlovian) conditioning and might occur in anticipation of the nauseogenic event. Classical conditioning of nausea can develop with chemotherapy in cancer patients. Initially, nausea and vomiting occur during and afte...

Mixed-handedness advantages in episodic memory obtained under conditions of intentional learning extend to incidental learning.

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Christman, Stephen D; Butler, Michael

2011-10-01

The existence of handedness differences in the retrieval of episodic memories is well-documented, but virtually all have been obtained under conditions of intentional learning. Two experiments are reported that extend the presence of such handedness differences to memory retrieval under conditions of incidental learning. Experiment 1 used Craik and Tulving's (1975) classic levels-of-processing paradigm and obtained handedness differences under incidental and intentional conditions of deep processing, but not under conditions of shallow incidental processing. Experiment 2 looked at incidental memory for distracter items from a recognition memory task and again found a mixed-handed advantage. Results are discussed in terms of the relation between interhemispheric interaction, levels of processing, and episodic memory retrieval. Copyright © 2011 Elsevier Inc. All rights reserved.

Role of classical conditioning in learning gastrointestinal symptoms.

Science.gov (United States)

Stockhorst, Ursula; Enck, Paul; Klosterhalfen, Sibylle

2007-07-07

Nausea and/or vomiting are aversive gastrointestinal (GI) symptoms. Nausea and vomiting manifest unconditionally after a nauseogenic experience. However, there is correlative, quasiexperimental and experimental evidence that nausea and vomiting can also be learned via classical (Pavlovian) conditioning and might occur in anticipation of the nauseogenic event. Classical conditioning of nausea can develop with chemotherapy in cancer patients. Initially, nausea and vomiting occur during and after the administration of cytotoxic drugs (post-treatment nausea and vomiting) as unconditioned responses (UR). In addition, 20%-30% of cancer patients receiving chemotherapy report these side effects, despite antiemetic medication, when being re-exposed to the stimuli that usually signal the chemotherapy session and its drug infusion. These symptoms are called anticipatory nausea (AN) and/or anticipatory vomiting (ANV) and are explained by classical conditioning. Moreover, there is recent evidence for the assumption that post-chemotherapy nausea is at least partly influenced by learning. After summarizing the relevant assumptions of the conditioning model, revealing that a context can become a conditioned stimulus (CS), the present paper summarizes data that nausea and/or vomiting is acquired by classical conditioning and, consequently, may be alleviated by conditioning techniques. Our own research has focussed on two aspects and is emphasized here. First, a conditioned nausea model was established in healthy humans using body rotation as the nausea-inducing treatment. The validity of this motion-sickness model to examine conditioning mechanisms in the acquisition and alleviation of conditioned nausea and associated endocrine and immunological responses is summarized. Results from the rotation-induced motion sickness model showed that gender is an important moderator variable to be considered in further studies. This paper concludes with a review of the application of the

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

Science.gov (United States)

Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

2017-09-01

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

Exploring the Relation between Teachers' Perceptions of Workplace Conditions and Their Professional Learning Goals

Science.gov (United States)

Louws, Monika L.; Meirink, Jacobiene A.; van Veen, Klaas; van Driel, Jan H.

2017-01-01

Schools' structural workplace conditions (e.g. learning resources and professional development policies) and cultural workplace conditions (e.g. school leadership, teachers' collaborative culture) have been found to affect the way teachers learn. It is not so much the objective conditions that support or impede professional learning but the way…

Operant Conditioning and Learning: Examples, Sources, Technology.

Science.gov (United States)

Pedrini, Bonnie C.; Pedrini, D. T.

The purpose of this paper is to relate psychology to teaching generally, and to relate behavior shaping to curriculum, specifically. Focusing on operant conditioning and learning, many studies are cited which illustrate some of the work being done toward effectively shaping or modifying student behavior whether in terms of subject matter or…

Cue competition in evaluative conditioning as a function of the learning process.

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Kattner, Florian; Green, C Shawn

2015-11-01

Evaluative conditioning (EC) is the change in the valence of a stimulus resulting from pairings with an affective (unconditioned) stimulus (US). With some exceptions, previous work has indicated that this form of conditioning might be insensitive to cue competition effects such as blocking and overshadowing. Here we assessed whether the extent of cue competition in EC depends upon the type of contingency learning during conditioning. Specifically, we contrasted a learning task that biased participants toward cognitive/inferential learning (i.e., predicting the US) with a learning task that prevented prolonged introspection (i.e., a rapid response made to the US). In all cases, standard EC effects were observed, with the subjective liking of stimuli changed in the direction of the valence of the US. More importantly, when inferential learning was likely, larger EC effects occurred for isolated stimuli than for compounds (indicating overshadowing). No blocking effects on explicit evaluations were observed for either learning task. Contingency judgments and implicit evaluations, however, were sensitive to blocking, indicating that the absence of a blocking effect on explicit evaluations might be due to inferences that occur during testing.

STRATEGY FOR EVALUATION AND SELECTION OF SYSTEMS FOR ELECTRONIC LEARNING

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Dubravka Mandušić

2012-12-01

Full Text Available Today`s technology supported and accelerated learning time requires constant and continuous acquisition of new knowledge. On the other hand, it does not leave enough time for additional education. Increasing number of E-learning systems, withdraws a need for precise evaluation of functionality that those systems provide; so they could be reciprocally compared. While implementing new systems for electronic learning, it is very important to pre-evaluate existing systems in order to select the one that meets all defined parameters, with low costs/investment. Proper evaluation can save time and money.

Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood

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Yunquan Song

2014-01-01

Full Text Available Empirical likelihood is a very popular method and has been widely used in the fields of artificial intelligence (AI and data mining as tablets and mobile application and social media dominate the technology landscape. This paper proposes an empirical likelihood shrinkage method to efficiently estimate unknown parameters and select correct moment conditions simultaneously, when the model is defined by moment restrictions in which some are possibly misspecified. We show that our method enjoys oracle-like properties; that is, it consistently selects the correct moment conditions and at the same time its estimator is as efficient as the empirical likelihood estimator obtained by all correct moment conditions. Moreover, unlike the GMM, our proposed method allows us to carry out confidence regions for the parameters included in the model without estimating the covariances of the estimators. For empirical implementation, we provide some data-driven procedures for selecting the tuning parameter of the penalty function. The simulation results show that the method works remarkably well in terms of correct moment selection and the finite sample properties of the estimators. Also, a real-life example is carried out to illustrate the new methodology.

Exploring the relation between teachers’ perceptions of workplace conditions and their professional learning goals

NARCIS (Netherlands)

Louws, Monika L.; Meirink, Jacobiene A.; van Veen, Klaas; van Driel, Jan H.

2017-01-01

Schools’ structural workplace conditions (e.g. learning resources and professional development policies) and cultural workplace conditions (e.g. school leadership, teachers’ collaborative culture) have been found to affect the way teachers learn. It is not so much the objective conditions that

Optimal Channel Selection Based on Online Decision and Offline Learning in Multichannel Wireless Sensor Networks

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Mu Qiao

2017-01-01

Full Text Available We propose a channel selection strategy with hybrid architecture, which combines the centralized method and the distributed method to alleviate the overhead of access point and at the same time provide more flexibility in network deployment. By this architecture, we make use of game theory and reinforcement learning to fulfill the optimal channel selection under different communication scenarios. Particularly, when the network can satisfy the requirements of energy and computational costs, the online decision algorithm based on noncooperative game can help each individual sensor node immediately select the optimal channel. Alternatively, when the network cannot satisfy the requirements of energy and computational costs, the offline learning algorithm based on reinforcement learning can help each individual sensor node to learn from its experience and iteratively adjust its behavior toward the expected target. Extensive simulation results validate the effectiveness of our proposal and also prove that higher system throughput can be achieved by our channel selection strategy over the conventional off-policy channel selection approaches.

Evolution of learning and levels of selection: a lesson from avian parent-offspring communication.

Science.gov (United States)

Lotem, Arnon; Biran-Yoeli, Inbar

2014-02-01

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

A calpain-2 selective inhibitor enhances learning & memory by prolonging ERK activation.

Science.gov (United States)

Liu, Yan; Wang, Yubin; Zhu, Guoqi; Sun, Jiandong; Bi, Xiaoning; Baudry, Michel

2016-06-01

While calpain-1 activation is required for LTP induction by theta burst stimulation (TBS), calpain-2 activation limits its magnitude during the consolidation period. A selective calpain-2 inhibitor applied either before or shortly after TBS enhanced the degree of potentiation. In the present study, we tested whether the selective calpain-2 inhibitor, Z-Leu-Abu-CONH-CH2-C6H3 (3, 5-(OMe)2 (C2I), could enhance learning and memory in wild-type (WT) and calpain-1 knock-out (C1KO) mice. We first showed that C2I could reestablish TBS-LTP in hippocampal slices from C1KO mice, and this effect was blocked by PD98059, an inhibitor of ERK. TBS resulted in PTEN degradation in hippocampal slices from both WT and C1KO mice, and C2I treatment blocked this effect in both mouse genotypes. Systemic injection of C2I 30 min before training in the fear-conditioning paradigm resulted in a biphasic dose-response curve, with low doses enhancing and high doses inhibiting freezing behavior. The difference between the doses needed to enhance and inhibit learning matches the difference in concentrations producing inhibition of calpain-2 and calpain-1. A low dose of C2I also restored normal learning in a novel object recognition task in C1KO mice. Levels of SCOP, a ERK phosphatase known to be cleaved by calpain-1, were decreased in dorsal hippocampus early but not late following training in WT mice; C2I treatment did not affect the early decrease in SCOP levels but prevented its recovery at the later time-point and prolonged ERK activation. The results indicate that calpain-2 activation limits the extent of learning, an effect possibly due to temporal limitation of ERK activation, as a result of SCOP synthesis induced by calpain-2-mediated PTEN degradation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

Science.gov (United States)

Quilty, J.; Adamowski, J. F.

2015-12-01

Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

The effect of encoding conditions on learning in the prototype distortion task.

Science.gov (United States)

Lee, Jessica C; Livesey, Evan J

2017-06-01

The prototype distortion task demonstrates that it is possible to learn about a category of physically similar stimuli through mere observation. However, there have been few attempts to test whether different encoding conditions affect learning in this task. This study compared prototypicality gradients produced under incidental learning conditions in which participants performed a visual search task, with those produced under intentional learning conditions in which participants were required to memorize the stimuli. Experiment 1 showed that similar prototypicality gradients could be obtained for category endorsement and familiarity ratings, but also found (weaker) prototypicality gradients in the absence of exposure. In Experiments 2 and 3, memorization was found to strengthen prototypicality gradients in familiarity ratings in comparison to visual search, but there were no group differences in participants' ability to discriminate between novel and presented exemplars. Although the Search groups in Experiments 2 and 3 produced prototypicality gradients, they were no different in magnitude to those produced in the absence of stimulus exposure in Experiment 1, suggesting that incidental learning during visual search was not conducive to producing prototypicality gradients. This study suggests that learning in the prototype distortion task is not implicit in the sense of resulting automatically from exposure, is affected by the nature of encoding, and should be considered in light of potential learning-at-test effects.

Selective transfer of visual working memory training on Chinese character learning.

Science.gov (United States)

Opitz, Bertram; Schneiders, Julia A; Krick, Christoph M; Mecklinger, Axel

2014-01-01

Previous research has shown a systematic relationship between phonological working memory capacity and second language proficiency for alphabetic languages. However, little is known about the impact of working memory processes on second language learning in a non-alphabetic language such as Mandarin Chinese. Due to the greater complexity of the Chinese writing system we expect that visual working memory rather than phonological working memory exerts a unique influence on learning Chinese characters. This issue was explored in the present experiment by comparing visual working memory training with an active (auditory working memory training) control condition and a passive, no training control condition. Training induced modulations in language-related brain networks were additionally examined using functional magnetic resonance imaging in a pretest-training-posttest design. As revealed by pre- to posttest comparisons and analyses of individual differences in working memory training gains, visual working memory training led to positive transfer effects on visual Chinese vocabulary learning compared to both control conditions. In addition, we found sustained activation after visual working memory training in the (predominantly visual) left infero-temporal cortex that was associated with behavioral transfer. In the control conditions, activation either increased (active control condition) or decreased (passive control condition) without reliable behavioral transfer effects. This suggests that visual working memory training leads to more efficient processing and more refined responses in brain regions involved in visual processing. Furthermore, visual working memory training boosted additional activation in the precuneus, presumably reflecting mental image generation of the learned characters. We, therefore, suggest that the conjoint activity of the mid-fusiform gyrus and the precuneus after visual working memory training reflects an interaction of working memory and

Evolution of learning in fluctuating environments: when selection favors both social and exploratory individual learning.

Science.gov (United States)

Borenstein, Elhanan; Feldman, Marcus W; Aoki, Kenichi

2008-03-01

Cumulative cultural change requires organisms that are capable of both exploratory individual learning and faithful social learning. In our model, an organism's phenotype is initially determined innately (by its genotypic value) or by social learning (copying a phenotype from the parental generation), and then may or may not be modified by individual learning (exploration around the initial phenotype). The environment alternates periodically between two states, each defined as a certain range of phenotypes that can survive. These states may overlap, in which case the same phenotype can survive in both states, or they may not. We find that a joint social and exploratory individual learning strategy-the strategy that supports cumulative culture-is likely to spread when the environmental states do not overlap. In particular, when the environmental states are contiguous and mutation is allowed among the genotypic values, this strategy will spread in either moderately or highly stable environments, depending on the exact nature of the individual learning applied. On the other hand, natural selection often favors a social learning strategy without exploration when the environmental states overlap. We find only partial support for the "consensus" view, which holds that individual learning, social learning, and innate determination of behavior will evolve at short, intermediate, and long environmental periodicities, respectively.

Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

Energy Technology Data Exchange (ETDEWEB)

Hajian, Amir; Alvarez, Marcelo A.; Bond, J. Richard, E-mail: ahajian@cita.utoronto.ca, E-mail: malvarez@cita.utoronto.ca, E-mail: bond@cita.utoronto.ca [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada)

2015-01-01

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features.

Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

International Nuclear Information System (INIS)

Hajian, Amir; Alvarez, Marcelo A.; Bond, J. Richard

2015-01-01

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

Science.gov (United States)

Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

2017-11-01

This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

Unweaving misconceptions: Guided learning, simulations, and misconceptions in learning principles of natural selection

Science.gov (United States)

Weeks, Brian E.

College students often come to the study of evolutionary biology with many misconceptions of how the processes of natural selection and speciation occur. How to relinquish these misconceptions with learners is a question that many educators face in introductory biology courses. Constructivism as a theoretical framework has become an accepted and promoted model within the epistemology of science instruction. However, constructivism is not without its skeptics who see some problems of its application in lacking necessary guidance for novice learners. This study within a quantitative, quasi-experimental format tested whether guided online instruction in a video format of common misconceptions in evolutionary biology produced higher performance on a survey of knowledge of natural selection versus more constructivist style learning in the form of student exploration of computer simulations of the evolutionary process. Performances on surveys were also explored for a combination of constructivist and guided techniques to determine if a consolidation of approaches produced higher test scores. Out of the 94 participants 95% displayed at least one misconception of natural selection in the pre-test while the study treatments produced no statistically significant improvements in post-test scores except within the video (guided learning treatment). These overall results demonstrated the stubbornness of misconceptions involving natural selection for adult learners and the difficulty of helping them overcome them. It also bolsters the idea that some misconceptions of natural selection and evolution may be hardwired in a neurological sense and that new, more long-term teaching techniques may be warranted. Such long-term strategies may not be best implemented with constructivist techniques alone, and it is likely that some level of guidance may be necessary for novice adult learners. A more substantial, nuanced approach for undergraduates is needed that consolidates successful

Learning outdoors: male lizards show flexible spatial learning under semi-natural conditions

Science.gov (United States)

Noble, Daniel W. A.; Carazo, Pau; Whiting, Martin J.

2012-01-01

Spatial cognition is predicted to be a fundamental component of fitness in many lizard species, and yet some studies suggest that it is relatively slow and inflexible. However, such claims are based on work conducted using experimental designs or in artificial contexts that may underestimate their cognitive abilities. We used a biologically realistic experimental procedure (using simulated predatory attacks) to study spatial learning and its flexibility in the lizard Eulamprus quoyii in semi-natural outdoor enclosures under similar conditions to those experienced by lizards in the wild. To evaluate the flexibility of spatial learning, we conducted a reversal spatial-learning task in which positive and negative reinforcements of learnt spatial stimuli were switched. Nineteen (32%) male lizards learnt both tasks within 10 days (spatial task mean: 8.16 ± 0.69 (s.e.) and reversal spatial task mean: 10.74 ± 0.98 (s.e.) trials). We demonstrate that E. quoyii are capable of flexible spatial learning and suggest that future studies focus on a range of lizard species which differ in phylogeny and/or ecology, using biologically relevant cognitive tasks, in an effort to bridge the cognitive divide between ecto- and endotherms. PMID:23075525

Lessons learned? Selected public acceptance case studies since Three Mile Island

Energy Technology Data Exchange (ETDEWEB)

Blee, D. [NAC International, Atlanta Corporate Headquarters, Atlanta, GA (United States)

2001-02-01

This paper will present an overview of the present situation, some recent polling survey information, and then look at lessons learned in terms of selected case studies and some global issues over the 22 years since the Three Mile Island (TMI) accident. That is quite an ambitious topic but there are some important lessons we can learn from the post-TMI era. (author)

Women's Learning in Contract Work: Practicing Contradictions in Boundaryless Conditions

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Fenwick, Tara

2008-01-01

The general rise in contractors, particularly among knowledge workers negotiating "boundaryless" employment conditions, has generated interest in the nature and forms of contract work. This article explores the learning of contract workers as they negotiate these conditions, with a focus on women. Drawing from a qualitative study of…

Conditional selectivity performance of Indian mutual fund schemes: An empirical study

Directory of Open Access Journals (Sweden)

Subrata Roy

2015-06-01

Full Text Available The present study seeks to examine the stock-selection performance of the sample open-ended equity mutual fund schemes of Birla Sun Life Mutual Fund Company based on traditional and conditional performance measures. It is generally expected that inclusion of some relevant predetermined public information variables in the conditional CAPM provides better performance estimates as compared to the traditional measures. The study reports that after inclusion of conditioning public information variables, the selectivity performances of the schemes have dramatically improved relative to the traditional measure and also found that conditional measure is superior to traditional measure in statistical test.

Secure relay selection based on learning with negative externality in wireless networks

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Zhao, Caidan; Xiao, Liang; Kang, Shan; Chen, Guiquan; Li, Yunzhou; Huang, Lianfen

2013-12-01

In this paper, we formulate relay selection into a Chinese restaurant game. A secure relay selection strategy is proposed for a wireless network, where multiple source nodes send messages to their destination nodes via several relay nodes, which have different processing and transmission capabilities as well as security properties. The relay selection utilizes a learning-based algorithm for the source nodes to reach their best responses in the Chinese restaurant game. In particular, the relay selection takes into account the negative externality of relay sharing among the source nodes, which learn the capabilities and security properties of relay nodes according to the current signals and the signal history. Simulation results show that this strategy improves the user utility and the overall security performance in wireless networks. In addition, the relay strategy is robust against the signal errors and deviations of some user from the desired actions.

Dynamics of the evolution of learning algorithms by selection

International Nuclear Information System (INIS)

Neirotti, Juan Pablo; Caticha, Nestor

2003-01-01

We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results

Influence of visual observational conditions on tongue motor learning

DEFF Research Database (Denmark)

Kothari, Mohit; Liu, Xuimei; Baad-Hansen, Lene

2016-01-01

To investigate the impact of visual observational conditions on performance during a standardized tongue-protrusion training (TPT) task and to evaluate subject-based reports of helpfulness, disturbance, pain, and fatigue due to the observational conditions on 0-10 numerical rating scales. Forty...... regarding the level of disturbance, pain or fatigue. Self-observation of tongue-training facilitated behavioral aspects of tongue motor learning compared with model-observation but not compared with control....

Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism.

Directory of Open Access Journals (Sweden)

Bineng Zhong

Full Text Available To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection for an object is one of the key issues. Typically, a common assumption used in visual tracking is that the raw video sequences are clear, while real-world data is with significant noise and irrelevant patterns. Consequently, the learned features may be not all relevant and noisy. To address this problem, we propose a novel visual tracking method via a point-wise gated convolutional deep network (CPGDN that jointly performs the feature learning and feature selection in a unified framework. The proposed method performs dynamic feature selection on raw features through a gating mechanism. Therefore, the proposed method can adaptively focus on the task-relevant patterns (i.e., a target object, while ignoring the task-irrelevant patterns (i.e., the surrounding background of a target object. Specifically, inspired by transfer learning, we firstly pre-train an object appearance model offline to learn generic image features and then transfer rich feature hierarchies from an offline pre-trained CPGDN into online tracking. In online tracking, the pre-trained CPGDN model is fine-tuned to adapt to the tracking specific objects. Finally, to alleviate the tracker drifting problem, inspired by an observation that a visual target should be an object rather than not, we combine an edge box-based object proposal method to further improve the tracking accuracy. Extensive evaluation on the widely used CVPR2013 tracking benchmark validates the robustness and effectiveness of the proposed method.

A Selective Role for Dopamine in Learning to Maximize Reward But Not to Minimize Effort: Evidence from Patients with Parkinson's Disease.

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Skvortsova, Vasilisa; Degos, Bertrand; Welter, Marie-Laure; Vidailhet, Marie; Pessiglione, Mathias

2017-06-21

Instrumental learning is a fundamental process through which agents optimize their choices, taking into account various dimensions of available options such as the possible reward or punishment outcomes and the costs associated with potential actions. Although the implication of dopamine in learning from choice outcomes is well established, less is known about its role in learning the action costs such as effort. Here, we tested the ability of patients with Parkinson's disease (PD) to maximize monetary rewards and minimize physical efforts in a probabilistic instrumental learning task. The implication of dopamine was assessed by comparing performance ON and OFF prodopaminergic medication. In a first sample of PD patients ( n = 15), we observed that reward learning, but not effort learning, was selectively impaired in the absence of treatment, with a significant interaction between learning condition (reward vs effort) and medication status (OFF vs ON). These results were replicated in a second, independent sample of PD patients ( n = 20) using a simplified version of the task. According to Bayesian model selection, the best account for medication effects in both studies was a specific amplification of reward magnitude in a Q-learning algorithm. These results suggest that learning to avoid physical effort is independent from dopaminergic circuits and strengthen the general idea that dopaminergic signaling amplifies the effects of reward expectation or obtainment on instrumental behavior. SIGNIFICANCE STATEMENT Theoretically, maximizing reward and minimizing effort could involve the same computations and therefore rely on the same brain circuits. Here, we tested whether dopamine, a key component of reward-related circuitry, is also implicated in effort learning. We found that patients suffering from dopamine depletion due to Parkinson's disease were selectively impaired in reward learning, but not effort learning. Moreover, anti-parkinsonian medication restored the

Lateralized implicit sequence learning in uni- and bi-manual conditions.

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Schmitz, Rémy; Pasquali, Antoine; Cleeremans, Axel; Peigneux, Philippe

2013-02-01

It has been proposed that the right hemisphere (RH) is better suited to acquire novel material whereas the left hemisphere (LH) is more able to process well-routinized information. Here, we ask whether this potential dissociation also manifests itself in an implicit learning task. Using a lateralized version of the serial reaction time task (SRT), we tested whether participants trained in a divided visual field condition primarily stimulating the RH would learn the implicit regularities embedded in sequential material faster than participants in a condition favoring LH processing. In the first study, half of participants were presented sequences in the left (vs. right) visual field, and had to respond using their ipsilateral hand (unimanual condition), hence making visuo-motor processing possible within the same hemisphere. Results showed successful implicit sequence learning, as indicated by increased reaction time for a transfer sequence in both hemispheric conditions and lack of conscious knowledge in a generation task. There was, however, no evidence of interhemispheric differences. In the second study, we hypothesized that a bimanual response version of the lateralized SRT, which requires interhemispheric communication and increases computational and cognitive processing loads, would favor RH-dependent visuospatial/attentional processes. In this bimanual condition, our results revealed a much higher transfer effect in the RH than in the LH condition, suggesting higher RH sensitivity to the processing of novel sequential material. This LH/RH difference was interpreted within the framework of the Novelty-Routinization model [Goldberg, E., & Costa, L. D. (1981). Hemisphere differences in the acquisition and use of descriptive systems. Brain and Language, 14(1), 144-173] and interhemispheric interactions in attentional processing [Banich, M. T. (1998). The missing link: the role of interhemispheric interaction in attentional processing. Brain and Cognition, 36

Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

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Rose, Sherri

2018-03-11

To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending. 2011-2012 Truven MarketScan database. I evaluate how much more, on average, commercially insured enrollees with each of 26 of the most prevalent medical conditions cost per year after controlling for demographics and other medical conditions. This is accomplished within the nonparametric targeted learning framework, which incorporates ensemble machine learning. Previous literature studying the impact of medical conditions on health care spending has almost exclusively focused on parametric risk adjustment; thus, I compare my approach to parametric regression. My results demonstrate that multiple sclerosis, congestive heart failure, severe cancers, major depression and bipolar disorders, and chronic hepatitis are the most costly medical conditions on average per individual. These findings differed from those obtained using parametric regression. The literature may be underestimating the spending contributions of several medical conditions, which is a potentially critical oversight. If current methods are not capturing the true incremental effect of medical conditions, undesirable incentives related to care may remain. Further work is needed to directly study these issues in the context of federal formulas. © Health Research and Educational Trust.

42 CFR 482.90 - Condition of participation: Patient and living donor selection.

Science.gov (United States)

2010-10-01

... selected to receive a transplant, the center must document in the patient's medical record the patient... 42 Public Health 5 2010-10-01 2010-10-01 false Condition of participation: Patient and living... Condition of participation: Patient and living donor selection. The transplant center must use written...

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

Science.gov (United States)

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Learning to Select Supplier Portfolios for Service Supply Chain.

Science.gov (United States)

Zhang, Rui; Li, Jingfei; Wu, Shaoyu; Meng, Dabin

2016-01-01

The research on service supply chain has attracted more and more focus from both academia and industrial community. In a service supply chain, the selection of supplier portfolio is an important and difficult problem due to the fact that a supplier portfolio may include multiple suppliers from a variety of fields. To address this problem, we propose a novel supplier portfolio selection method based on a well known machine learning approach, i.e., Ranking Neural Network (RankNet). In the proposed method, we regard the problem of supplier portfolio selection as a ranking problem, which integrates a large scale of decision making features into a ranking neural network. Extensive simulation experiments are conducted, which demonstrate the feasibility and effectiveness of the proposed method. The proposed supplier portfolio selection model can be applied in a real corporation easily in the future.

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

Feature selection and multi-kernel learning for sparse representation on a manifold.

Science.gov (United States)

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

Memory and selective learning in children with spina bifida-myelomeningocele and shunted hydrocephalus: A preliminary study

Directory of Open Access Journals (Sweden)

Vachha Behroze

2005-11-01

Full Text Available Abstract Background Selective learning is the ability to select items of relevance from among less important items. Limited evidence exists regarding the efficiency with which children with spina bifida-myelomeningocele and shunted hydrocephalus (SB/SH are able to learn information. This report describes initial data related to components of learning and metacognitive skills in children with SB/SH. Methods Twenty six children with SB/SH and 26 controls (age: 7 – 16 y with average intelligence, and monolingual English-speaking backgrounds participated in the study. Exclusion criteria for the SB/SH group were: prior history of shunt infection, history of seizure or shunt malfunction within the previous three months, prior diagnoses of attention disorders and/or clinical depression. Children were presented lists of words with equal exemplars each of two distinct semantic categories (e.g. fruits, animals, and told to make as high a score as possible by learning the words. The value of the words was designated by category membership (e.g. animals = low value; fruits = high value. The total number of words learned across three learning trials was used to determine memory span. Selective learning efficiency (SLE was computed as the efficiency with which items of greater value were selectively learned across three trials. Results Children with SB/SH did worse than controls on memory span (P Conclusion Success in school is often dependent on the ability to recall important facts selectively and ignore less important information. Children with SB/SH in our study had a poor memory span and were unable to monitor and report an efficient and workable metacognitive strategy required to remember a list of words. Preliminary findings may begin to explain our previous clinical and research findings wherein children with SB/SH often focus on extraneous details, but demonstrate difficulty remembering the main gist of a story/event.

Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

Science.gov (United States)

Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

2014-01-01

Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

Online Learning in Higher Education: Necessary and Sufficient Conditions

Science.gov (United States)

Lim, Cher Ping

2005-01-01

The spectacular development of information and communication technologies through the Internet has provided opportunities for students to explore the virtual world of information. In this article, the author discusses the necessary and sufficient conditions for successful online learning in educational institutions. The necessary conditions…

Instance Selection for Classifier Performance Estimation in Meta Learning

Directory of Open Access Journals (Sweden)

Marcin Blachnik

2017-11-01

Full Text Available Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be informative to allow the precise estimation of prediction accuracy. In this paper, a new type of metadata descriptors is analyzed. These descriptors are based on the compression level obtained from the instance selection methods at the data-preprocessing stage. To verify their suitability, two types of experiments on real-world datasets have been conducted. In the first one, 11 instance selection methods were examined in order to validate the compression–accuracy relation for three classifiers: k-nearest neighbors (kNN, support vector machine (SVM, and random forest. From this analysis, two methods are recommended (instance-based learning type 2 (IB2, and edited nearest neighbor (ENN which are then compared with the state-of-the-art metaset descriptors. The obtained results confirm that the two suggested compression-based meta-features help to predict accuracy of the base model much more accurately than the state-of-the-art solution.

The Role of Executive Control of Attention and Selective Encoding for Preschoolers' Learning

Science.gov (United States)

Roderer, Thomas; Krebs, Saskia; Schmid, Corinne; Roebers, Claudia M.

2012-01-01

Selectivity in encoding, aspects of attentional control and their contribution to learning performance were explored in a sample of preschoolers. While the children are performing a learning task, their encoding of relevant and attention towards irrelevant information was recorded through an eye-tracking device. Recognition of target items was…

Modulating effects in learned helplessness of dyadic dominance-submission relations.

Science.gov (United States)

Díaz-Berciano, Cristina; de Vicente, Francisco; Fontecha, Elisa

2008-01-01

In this experiment, learned helplessness was studied from an ethological perspective by examining individual differences in social dominance and its influence on the effects of helplessness. Ninety animals were used, 30 randomly selected and 60 selected because of their clear dominance or submission. Each condition (dominant, submissive, and random) was distributed in three subgroups corresponding to the triadic design. The test consisted of an escape/avoidance task. The results showed that the animals in the uncontrollable condition performed worse than those in the controllable and no treatment conditions. Social submission and dominance reduced vulnerability of the subjects against learned helplessness. Submission had a facilitating effect on subsequent learning, independently of whether pretreatment was controllability or uncontrollability. Learned mastery was observed in the submissive condition, because submission benefited the subjects in the controllable condition in comparison with the untreated subjects, and dominance impaired the subjects in the controllable condition. Copyright 2007 Wiley-Liss, Inc.

Training self-assessment and task-selection skills : A cognitive approach to improving self-regulated learning

NARCIS (Netherlands)

Kostons, Danny; van Gog, Tamara; Paas, Fred

For self-regulated learning to be effective, students need to be able to accurately assess their own performance on a learning task and use this assessment for the selection of a new learning task. Evidence suggests, however, that students have difficulties with accurate self-assessment and task

Drosophila Courtship Conditioning As a Measure of Learning and Memory.

Science.gov (United States)

Koemans, Tom S; Oppitz, Cornelia; Donders, Rogier A T; van Bokhoven, Hans; Schenck, Annette; Keleman, Krystyna; Kramer, Jamie M

2017-06-05

Many insights into the molecular mechanisms underlying learning and memory have been elucidated through the use of simple behavioral assays in model organisms such as the fruit fly, Drosophila melanogaster. Drosophila is useful for understanding the basic neurobiology underlying cognitive deficits resulting from mutations in genes associated with human cognitive disorders, such as intellectual disability (ID) and autism. This work describes a methodology for testing learning and memory using a classic paradigm in Drosophila known as courtship conditioning. Male flies court females using a distinct pattern of easily recognizable behaviors. Premated females are not receptive to mating and will reject the male's copulation attempts. In response to this rejection, male flies reduce their courtship behavior. This learned reduction in courtship behavior is measured over time, serving as an indicator of learning and memory. The basic numerical output of this assay is the courtship index (CI), which is defined as the percentage of time that a male spends courting during a 10 min interval. The learning index (LI) is the relative reduction of CI in flies that have been exposed to a premated female compared to naïve flies with no previous social encounters. For the statistical comparison of LIs between genotypes, a randomization test with bootstrapping is used. To illustrate how the assay can be used to address the role of a gene relating to learning and memory, the pan-neuronal knockdown of Dihydroxyacetone phosphate acyltransferase (Dhap-at) was characterized here. The human ortholog of Dhap-at, glyceronephosphate O-acyltransferase (GNPT), is involved in rhizomelic chondrodysplasia punctata type 2, an autosomal-recessive syndrome characterized by severe ID. Using the courtship conditioning assay, it was determined that Dhap-at is required for long-term memory, but not for short-term memory. This result serves as a basis for further investigation of the underlying molecular

The orexin component of fasting triggers memory processes underlying conditioned food selection in the rat.

Science.gov (United States)

Ferry, Barbara; Duchamp-Viret, Patricia

2014-03-14

To test the selectivity of the orexin A (OXA) system in olfactory sensitivity, the present study compared the effects of fasting and of central infusion of OXA on the memory processes underlying odor-malaise association during the conditioned odor aversion (COA) paradigm. Animals implanted with a cannula in the left ventricle received ICV infusion of OXA or artificial cerebrospinal fluid (ACSF) 1 h before COA acquisition. An additional group of intact rats were food-deprived for 24 h before acquisition. Results showed that the increased olfactory sensitivity induced by fasting and by OXA infusion was accompanied by enhanced COA performance. The present results suggest that fasting-induced central OXA release influenced COA learning by increasing not only olfactory sensitivity, but also the memory processes underlying the odor-malaise association.

Learning: from association to cognition.

Science.gov (United States)

Shanks, David R

2010-01-01

Since the very earliest experimental investigations of learning, tension has existed between association-based and cognitive theories. Associationism accounts for the phenomena of both conditioning and "higher" forms of learning via concepts such as excitation, inhibition, and reinforcement, whereas cognitive theories assume that learning depends on hypothesis testing, cognitive models, and propositional reasoning. Cognitive theories have received considerable impetus in regard to both human and animal learning from recent research suggesting that the key illustration of cue selection in learning, blocking, often arises from inferential reasoning. At the same time, a dichotomous view that separates noncognitive, unconscious (implicit) learning from cognitive, conscious (explicit) learning has gained favor. This review selectively describes key findings from this research, evaluates evidence for and against associative and cognitive explanatory constructs, and critically examines both the dichotomous view of learning as well as the claim that learning can occur unconsciously.

From conditioning to learning communities: implications of fifty years of research in e-learning interaction design

Directory of Open Access Journals (Sweden)

Andrew Ravenscroft

2003-12-01

Full Text Available This paper will consider e-learning in terms of the underlying learning processes and interactions that are stimulated, supported or favoured by new media and the contexts or communities in which it is used. We will review and critique a selection of research and development from the past fifty years that has linked pedagogical and learning theory to the design of innovative e-learning systems and activities, and discuss their implications. It will include approaches that are, essentially, behaviourist (Skinner and Gagné, cognitivist (Pask, Piaget and Papert, situated (Lave, Wenger and Seely-Brown, socioconstructivist (Vygotsky, socio-cultural (Nardi and Engestrom and community-based (Wenger and Preece. Emerging from this review is the argument that effective elearning usually requires, or involves, high-quality educational discourse, that leads to, at the least, improved knowledge, and at the best, conceptual development and improved understanding. To achieve this I argue that we need to adopt a more holistic approach to design that synthesizes features of the included approaches, leading to a framework that emphasizes the relationships between cognitive changes, dialogue processes and the communities, or contexts for e-learning.

Project Selection in the Design Studio: Absence of Learning Environments

Science.gov (United States)

Basa, Inci

2010-01-01

Project selection is an essential matter of design teaching. Based on observations of a specific curriculum, the author claims that a wide repertoire of subjects including offices, restaurants, hotels, and other public places are used to prepare design students, but that schools and other "learning environments/ schools" are similarly…

Selective Use of the Mother Tongue to Enhance Students’ English Learning Processes...Beyond the Same Assumptions

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Luis Fernando Cuartas Alvarez

2014-04-01

Full Text Available This article reports the results of an action-research project that examines enhancing students’ English learning processes through the selective use of their mother tongues with the aim of overcoming their reluctant attitudes toward learning English in the classroom. This study involves forty ninth-graders from an all-girls public school in Medellin, Colombia. The data gathered included field notes, questionnaires, and participants’ focus group interviews. The findings show that the mother tongue plays an important role in students’ English learning processes by fostering students’ affective, motivational, cognitive, and attitudinal aspects. Thus, the mother tongue serves as the foothold for further advances in learning English when used selectively.

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan

2014-09-20

Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

Selective bilateral amygdala lesions in rhesus monkeys fail to disrupt object reversal learning.

Science.gov (United States)

Izquierdo, Alicia; Murray, Elisabeth A

2007-01-31

Neuropsychological studies in nonhuman primates have led to the view that the amygdala plays an essential role in stimulus-reward association. The main evidence in support of this idea is that bilateral aspirative or radiofrequency lesions of the amygdala yield severe impairments on object reversal learning, a task that assesses the ability to shift choices of objects based on the presence or absence of food reward (i.e., reward contingency). The behavioral effects of different lesion techniques, however, can vary. The present study therefore evaluated the effects of selective, excitotoxic lesions of the amygdala in rhesus monkeys on object reversal learning. For comparison, we tested the same monkeys on a task known to be sensitive to amygdala damage, the reinforcer devaluation task. Contrary to previous results based on less selective lesion techniques, monkeys with complete excitotoxic amygdala lesions performed object reversal learning as quickly as controls. As predicted, however, the same operated monkeys were impaired in making object choices after devaluation of the associated food reinforcer. The results suggest two conclusions. First, the results demonstrate that the amygdala makes a selective contribution to stimulus-reward association; the amygdala is critical for guiding object choices after changes in reward value but not after changes in reward contingency. Second, the results implicate a critical contribution to object reversal learning of structures nearby the amygdala, perhaps the subjacent rhinal cortex.

When Average Is Not Good Enough: Students with Learning Disabilities at Selective, Private Colleges

Science.gov (United States)

Weis, Robert; Erickson, Celeste P.; Till, Christina H.

2017-01-01

Adolescents with learning disabilities disproportionately come from lower socioeconomic status backgrounds, show normative deficits in academic skills, and attend 2-year, public colleges instead of 4-year institutions. However, students with learning disabilities are well represented at the United States' most expensive and selective postsecondary…

Mosaic model for sensorimotor learning and control.

Science.gov (United States)

Haruno, M; Wolpert, D M; Kawato, M

2001-10-01

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

Learning and memory

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P. A. J. Ryke

1989-03-01

Full Text Available Under various circumstances and in different species the outward expression of learning varies considerably, and this has led to the classification of different categories of learning. Just as there is no generally agreed on definition of learning, there is no one system of classification. Types of learning commonly recognized are: Habituation, sensitization, classical conditioning, operant conditioning, trial and error, taste aversion, latent learning, cultural learning, imprinting, insight learning, learning-set learning and instinct. The term memory must include at least two separate processes. It must involve, on the one hand, that of learning something and on the other, at some later date, recalling that thing. What lies between the learning and (he remembering must be some permanent record — a memory trace — within the brain. Memory exists in at least two forms: memory for very recent events (short-term which is relatively labile and easily disruptable; and long-term memory, which is much more stable. Not everything that gets into short-term memory becomes fixed in the long-term store; a filtering mechanism selects things that might be important and discards the rest.

Goal selection versus process control while learning to use a brain-computer interface

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Royer, Audrey S.; Rose, Minn L.; He, Bin

2011-06-01

A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.

Selection Criteria in Regime Switching Conditional Volatility Models

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Thomas Chuffart

2015-05-01

Full Text Available A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification ; BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.

You see what you have learned. Evidence for an interrelation of associative learning and visual selective attention.

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Feldmann-Wüstefeld, Tobias; Uengoer, Metin; Schubö, Anna

2015-11-01

Besides visual salience and observers' current intention, prior learning experience may influence deployment of visual attention. Associative learning models postulate that observers pay more attention to stimuli previously experienced as reliable predictors of specific outcomes. To investigate the impact of learning experience on deployment of attention, we combined an associative learning task with a visual search task and measured event-related potentials of the EEG as neural markers of attention deployment. In the learning task, participants categorized stimuli varying in color/shape with only one dimension being predictive of category membership. In the search task, participants searched a shape target while disregarding irrelevant color distractors. Behavioral results showed that color distractors impaired performance to a greater degree when color rather than shape was predictive in the learning task. Neurophysiological results show that the amplified distraction was due to differential attention deployment (N2pc). Experiment 2 showed that when color was predictive for learning, color distractors captured more attention in the search task (ND component) and more suppression of color distractor was required (PD component). The present results thus demonstrate that priority in visual attention is biased toward predictive stimuli, which allows learning experience to shape selection. We also show that learning experience can overrule strong top-down control (blocked tasks, Experiment 3) and that learning experience has a longer-term effect on attention deployment (tasks on two successive days, Experiment 4). © 2015 Society for Psychophysiological Research.

Are environmental conditions in South African classrooms conducive for learning?

CSIR Research Space (South Africa)

Gibberd, Jeremy T

2013-10-01

Full Text Available not provide an environment that promotes productivity and comfort for particular summer conditions, and therefore is unlikely to be conducive for learning. The paper draws a number of conclusions from the study and makes recommendations for further research....

Endogenously- and Exogenously-Driven Selective Sustained Attention: Contributions to Learning in Kindergarten Children

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Erickson, Lucy C.; Thiessen, Erik D.; Godwin, Karrie E.; Dickerson, John P.; Fisher, Anna V.

2015-01-01

Selective sustained attention is vital for higher order cognition. Although endogenous and exogenous factors influence selective sustained attention, assessment of the degree to which these factors influence performance and learning is often challenging. We report findings from the Track-It task, a paradigm that aims to assess the contribution of…

Emotional eating and Pavlovian learning: does negative mood facilitate appetitive conditioning?

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Bongers, Peggy; van den Akker, Karolien; Havermans, Remco; Jansen, Anita

2015-06-01

Emotional eating has been suggested to be a learned behaviour; more specifically, classical conditioning processes might be involved in its development. In the present study we investigated whether a negative mood facilitates appetitive conditioning and whether trait impulsivity influences this process. After undergoing either a negative or neutral mood induction, participants were subjected to a differential classical conditioning procedure, using neutral stimuli and appetizing food. Two initially neutral distinctive vases with flowers were (CS+) or were not (CS-) paired with chocolate mousse intake. We measured participants' expectancy and desire to eat (4 CS+ and 4 CS- trials), salivation response, and actual food intake. The BIS-11 was administered to assess trait impulsivity. In both mood conditions, participants showed a classically conditioned appetite. Unexpectedly, there was no evidence of facilitated appetitive learning in a negative mood with regard to expectancy, desire, salivation, or intake. However, immediately before the taste test, participants in the negative mood condition reported a stronger desire to eat in the CS+ compared to the CS- condition, while no such effect occurred in the neutral group. An effect of impulsivity was found with regard to food intake in the neutral mood condition: high-impulsive participants consumed less food when presented with the CS+ compared to the CS-, and also less than low-impulsive participants. An alternative pathway to appetitive conditioning with regard to emotions is that it is not the neutral stimuli, but the emotions themselves that become conditioned stimuli and elicit appetitive responses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning

NARCIS (Netherlands)

Kostons, Danny; Van Gog, Tamara; Paas, Fred

2012-01-01

Kostons, D., Van Gog, T., & Paas, F. (2012). Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning. Learning and Instruction, 22(2), 121-132. doi:10.1016/j.learninstruc.2011.08.004

Emotional eating and Pavlovian learning: evidence for conditioned appetitive responding to negative emotional states.

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Bongers, Peggy; Jansen, Anita

2017-02-01

Appetitive learning has been demonstrated several times using neutral cues or contexts as a predictor of food intake and it has been shown that humans easily learn cued desires for foods. It has, however, never been studied whether internal cues are also capable of appetitive conditioning. In this study, we tested whether humans can learn cued eating desires to negative moods as conditioned stimuli (CS), thereby offering a potential explanation of emotional eating (EE). Female participants were randomly presented with 10 different stimuli eliciting either negative or neutral emotional states, with one of these states paired with eating chocolate. Expectancy to eat, desire to eat, salivation, and unpleasantness of experiencing negative emotions were assessed. After conditioning, participants were brought into a negative emotional state and were asked to choose between money and chocolate. Data showed differential conditioned responding on the expectancy and desire measures, but not on salivation. Specific conditioned effects were obtained for participants with a higher BMI (body mass index) on the choice task, and for participants high on EE on the unpleasantness ratings. These findings provide the first experimental evidence for the idea that negative emotions can act as conditioned stimuli, and might suggest that classical conditioning is involved in EE.

Instance Selection for Classifier Performance Estimation in Meta Learning

OpenAIRE

Marcin Blachnik

2017-01-01

Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be inform...

FACTORS THAT INFLUENCE THE SELECTION OF LEARNING OPPORTUNITIES FOR STUDENT NURSES IN PRIMARY HEALTH CARE

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H. lita

2002-11-01

The study therefore focused on the following objective: To identify the factors that influence the selection of learning opportunities for primary health care in hospital units. A qualitative research design utilising focus group discussions were used. The population consisted of conveniently selected lecturers, student nurses and registered nurses. The same initial question was asked in each focus group to initiate the discussions. The data were analysed according to Tesch's method. The results indicated that there is positive commitment from the lecturers and registered nurses to be involved in selecting appropriate learning opportunities. The student nurses also demonstrated a willingness to learn and to be exposed to learning opportunities in primary health care. There were however certain constraints that emerged as themes, namely: • Managerial constraints • Educational constraints Under the theme "managerial constraints" categories such as workload, nursing staff shortages and communication problems were identified. Under the theme "educational constraints" categories such as a lack of guidance, and the correlation of theory and practice emerged. Recommendations based on this research report include improvement of in-service education on managerial and educational aspects to facilitate the primary health care approach in hospitals.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

Energy Technology Data Exchange (ETDEWEB)

Baraldi, Piero, E-mail: piero.baraldi@polimi.i [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

2011-04-15

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

International Nuclear Information System (INIS)

Baraldi, Piero; Razavi-Far, Roozbeh; Zio, Enrico

2011-01-01

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Hippocampal theta activity is selectively associated with contingency detection but not discrimination in rabbit discrimination-reversal eyeblink conditioning.

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Nokia, Miriam S; Wikgren, Jan

2010-04-01

The relative power of the hippocampal theta-band ( approximately 6 Hz) activity (theta ratio) is thought to reflect a distinct neural state and has been shown to affect learning rate in classical eyeblink conditioning in rabbits. We sought to determine if the theta ratio is mostly related to the detection of the contingency between the stimuli used in conditioning or also to the learning of more complex inhibitory associations when a highly demanding delay discrimination-reversal eyeblink conditioning paradigm is used. A high hippocampal theta ratio was not only associated with a fast increase in conditioned responding in general but also correlated with slow emergence of discriminative responding due to sustained responding to the conditioned stimulus not paired with an unconditioned stimulus. The results indicate that the neural state reflected by the hippocampal theta ratio is specifically linked to forming associations between stimuli rather than to the learning of inhibitory associations needed for successful discrimination. This is in line with the view that the hippocampus is responsible for contingency detection in the early phase of learning in eyeblink conditioning. (c) 2009 Wiley-Liss, Inc.

Cocaine induces state-dependent learning of sexual conditioning in male Japanese quail.

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Gill, Karin E; Rice, Beth Ann; Akins, Chana K

2015-01-01

State dependent learning effects have been widely studied in a variety of drugs of abuse. However, they have yet to be studied in relation to sexual motivation. The current study investigated state-dependent learning effects of cocaine in male Japanese quail (Coturnix japonica) using a sexual conditioning paradigm. Cocaine-induced state-dependent learning effects were investigated using a 2×2 factorial design with training state as one factor and test state as the other factor. During a 14-day training phase, male quail were injected once daily with 10mg/kg cocaine or saline and then placed in a test chamber after 15min. In the test chamber, sexual conditioning trials consisted of presentation of a light conditioned stimulus (CS) followed by sexual reinforcement. During the state dependent test, half of the birds received a shift in drug state from training to testing (Coc→Sal or Sal→Coc) while the other half remained in the same drug state (Coc→Coc or Sal→Sal). Results showed that male quail that were trained and tested in the same state (Coc→Coc or Sal→Sal) showed greater sexual conditioning than male quail that were trained and tested in different states (Sal→Coc) except when cocaine was administered chronically prior to the test (Coc→Sal). For the latter condition, sexual conditioning persisted from cocaine training to the saline test. The findings suggest that state dependent effects may alter sexual motivation and that repeated exposure to cocaine during sexual activity may increase sexual motivation which, in turn, may lead to high risk sexual activities. An alternative explanation for the findings is also discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

Can young children learn words from a robot?

OpenAIRE

Moriguchi, Yusuke; Kanda, Takayuki; Ishiguro, Hiroshi; Shimada, Yoko; Itakura, Shoji

2011-01-01

Young children generally learn words from other people. Recent research has shown that children can learn new actions and skills from nonhuman agents. This study examines whether young children could learn words from a robot. Preschool children were shown a video in which either a woman (human condition) or a mechanical robot (robot condition) labeled novel objects. Then the children were asked to select the objects according to the names used in the video. The results revealed that children ...

Learning from Fables: Moral Values in Three Selected English Stories

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Abrar, Mukhlash

2016-01-01

Fable is not just a fun story, but it certainly has the moral lesson(s) inside of the storyline. This research tries to portray ethical value(s) in three selected English fable stories as well as to let the readers know that they can learn something from the fables. With this study, the researcher also correlated the value(s) to real life and…

Collaborative testing for key-term definitions under representative conditions: Efficiency costs and no learning benefits.

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Wissman, Kathryn T; Rawson, Katherine A

2018-01-01

Students are expected to learn key-term definitions across many different grade levels and academic disciplines. Thus, investigating ways to promote understanding of key-term definitions is of critical importance for applied purposes. A recent survey showed that learners report engaging in collaborative practice testing when learning key-term definitions, with outcomes also shedding light on the way in which learners report engaging in collaborative testing in real-world contexts (Wissman & Rawson, 2016, Memory, 24, 223-239). However, no research has directly explored the effectiveness of engaging in collaborative testing under representative conditions. Accordingly, the current research evaluates the costs (with respect to efficiency) and the benefits (with respect to learning) of collaborative testing for key-term definitions under representative conditions. In three experiments (ns = 94, 74, 95), learners individually studied key-term definitions and then completed retrieval practice, which occurred either individually or collaboratively (in dyads). Two days later, all learners completed a final individual test. Results from Experiments 1-2 showed a cost (with respect to efficiency) and no benefit (with respect to learning) of engaging in collaborative testing for key-term definitions. Experiment 3 evaluated a theoretical explanation for why collaborative benefits do not emerge under representative conditions. Collectively, outcomes indicate that collaborative testing versus individual testing is less effective and less efficient when learning key-term definitions under representative conditions.

The study of selective property of college student’s learning space

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Nagai, Mizuki; Matsumoto, Yuji; Naka, Ryusuke

2018-05-01

These days, college students study not only at places designed for learning such as libraries in colleges, but also cafes in downtown while the number of facilities for learning run by colleges is increasing. Then I have researched facilities in college and those in downtown to find selective properties of college students’ learning space. First, I found by questionnaire survey that students chose “3rd place” such as cafes and fast food shops, second to their houses and libraries in college. Next, I found “psychological factor” were also affected their choice. Furthermore, they studied different subjects at different places. In experiments, I researched how effectively they studied each subject at every place. The results show that I find that places you like and places where learning efficiency is good are different. They learned the least effective at “3d place” regardless of what they learned. The result of how long they kept high-level intellectual activity at each place shows that they could work on the study with more motivation at their favorite place and 3rd place. On the other hand, at the 2nd place, they could study rather effectively, but could not keep concentration and motivation for a long time. In this way, college students have 2 patterns of choosing learning space.

Dissociation of learned helplessness and fear conditioning in mice: a mouse model of depression.

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Dominic Landgraf

Full Text Available The state of being helpless is regarded as a central aspect of depression, and therefore the learned helplessness paradigm in rodents is commonly used as an animal model of depression. The term 'learned helplessness' refers to a deficit in escaping from an aversive situation after an animal is exposed to uncontrollable stress specifically, with a control/comparison group having been exposed to an equivalent amount of controllable stress. A key feature of learned helplessness is the transferability of helplessness to different situations, a phenomenon called 'trans-situationality'. However, most studies in mice use learned helplessness protocols in which training and testing occur in the same environment and with the same type of stressor. Consequently, failures to escape may reflect conditioned fear of a particular environment, not a general change of the helpless state of an animal. For mice, there is no established learned helplessness protocol that includes the trans-situationality feature. Here we describe a simple and reliable learned helplessness protocol for mice, in which training and testing are carried out in different environments and with different types of stressors. We show that with our protocol approximately 50% of mice develop learned helplessness that is not attributable to fear conditioning.

Dissociation of learned helplessness and fear conditioning in mice: a mouse model of depression.

Science.gov (United States)

Landgraf, Dominic; Long, Jaimie; Der-Avakian, Andre; Streets, Margo; Welsh, David K

2015-01-01

The state of being helpless is regarded as a central aspect of depression, and therefore the learned helplessness paradigm in rodents is commonly used as an animal model of depression. The term 'learned helplessness' refers to a deficit in escaping from an aversive situation after an animal is exposed to uncontrollable stress specifically, with a control/comparison group having been exposed to an equivalent amount of controllable stress. A key feature of learned helplessness is the transferability of helplessness to different situations, a phenomenon called 'trans-situationality'. However, most studies in mice use learned helplessness protocols in which training and testing occur in the same environment and with the same type of stressor. Consequently, failures to escape may reflect conditioned fear of a particular environment, not a general change of the helpless state of an animal. For mice, there is no established learned helplessness protocol that includes the trans-situationality feature. Here we describe a simple and reliable learned helplessness protocol for mice, in which training and testing are carried out in different environments and with different types of stressors. We show that with our protocol approximately 50% of mice develop learned helplessness that is not attributable to fear conditioning.

Statistical Learning Framework with Adaptive Retraining for Condition-Based Maintenance

International Nuclear Information System (INIS)

An, Sang Ha; Chang, Soon Heung; Heo, Gyun Young; Seo, Ho Joon; Kim, Su Young

2009-01-01

As systems become more complex and more critical in our daily lives, the need for the maintenance based on the reliable monitoring and diagnosis has become more apparent. However, in reality, the general opinion has been that 'maintenance is a necessary evil' or 'nothing can be done to improve maintenance costs'. Perhaps these were true statements twenty years ago when many of the diagnostic technologies were not fully developed. The developments of microprocessor or computer based instrumentation that can be used to monitor the operating condition of plant equipment, machinery and systems have provided the means to manage the maintenance operation. They have provided the means to reduce or eliminate unnecessary repairs, prevent catastrophic machine failures and reduce the negative impact of the maintenance operation on the profitability of manufacturing and production plants. Condition-based maintenance (CBM) techniques help determine the condition of in-service equipment in order to predict when maintenance should be performed. Most of the statistical learning techniques are only valid as long as the physics of a system does not change. If any significant change such as the replacement of a component or equipment occurs in the system, the statistical learning model should be re-trained or re-developed to adapt the new system. In this research, authors will propose a statistical learning framework which can be applicable for various CBMs, and the concept of the adaptive retraining technique will be described to support the execution of the framework so that the monitoring system does not need to be re-developed or re-trained even though there are any significant changes in the system or component

Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

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Guofeng Tong

2014-04-01

Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

How Much of Language Acquisition Does Operant Conditioning Explain?

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Sturdy, Christopher B; Nicoladis, Elena

2017-01-01

Since the 1950s, when Chomsky argued that Skinner's arguments could not explain syntactic acquisition, psychologists have generally avoided explicitly invoking operant or instrumental conditioning as a learning mechanism for language among human children. In this article, we argue that this is a mistake. We focus on research that has been done on language learning in human infants and toddlers in order to illustrate our points. Researchers have ended up inventing learning mechanisms that, in actual practice, not only resemble but also in fact are examples of operant conditioning (OC) by any other name they select. We argue that language acquisition researchers should proceed by first ruling out OC before invoking alternative learning mechanisms. While it is possible that OC cannot explain all of the language acquisition, simple learning mechanisms that work across species may have some explanatory power in children's language learning.

Selection and Use of Online Learning Resources by First-Year Medical Students: Cross-Sectional Study.

Science.gov (United States)

Judd, Terry; Elliott, Kristine

2017-10-02

Medical students have access to a wide range of learning resources, many of which have been specifically developed for or identified and recommended to them by curriculum developers or teaching staff. There is an expectation that students will access and use these resources to support their self-directed learning. However, medical educators lack detailed and reliable data about which of these resources students use to support their learning and how this use relates to key learning events or activities. The purpose of this study was to comprehensively document first-year medical student selection and use of online learning resources to support their bioscience learning within a case-based curriculum and assess these data in relation to our expectations of student learning resource requirements and use. Study data were drawn from 2 sources: a survey of student learning resource selection and use (2013 cohort; n=326) and access logs from the medical school learning platform (2012 cohort; n=337). The paper-based survey, which was distributed to all first-year students, was designed to assess the frequency and types of online learning resources accessed by students and included items about their perceptions of the usefulness, quality, and reliability of various resource types and sources. Of 237 surveys returned, 118 complete responses were analyzed (36.2% response rate). Usage logs from the learning platform for an entire semester were processed to provide estimates of first-year student resource use on an individual and cohort-wide basis according to method of access, resource type, and learning event. According to the survey data, students accessed learning resources via the learning platform several times per week on average, slightly more often than they did for resources from other online sources. Google and Wikipedia were the most frequently used nonuniversity sites, while scholarly information sites (eg, online journals and scholarly databases) were accessed

Selective increase of auditory cortico-striatal coherence during auditory-cued Go/NoGo discrimination learning.

Directory of Open Access Journals (Sweden)

Andreas L. Schulz

2016-01-01

Full Text Available Goal directed behavior and associated learning processes are tightly linked to neuronal activity in the ventral striatum. Mechanisms that integrate task relevant sensory information into striatal processing during decision making and learning are implicitly assumed in current reinforcementmodels, yet they are still weakly understood. To identify the functional activation of cortico-striatal subpopulations of connections during auditory discrimination learning, we trained Mongolian gerbils in a two-way active avoidance task in a shuttlebox to discriminate between falling and rising frequency modulated tones with identical spectral properties. We assessed functional coupling by analyzing the field-field coherence between the auditory cortex and the ventral striatum of animals performing the task. During the course of training, we observed a selective increase of functionalcoupling during Go-stimulus presentations. These results suggest that the auditory cortex functionally interacts with the ventral striatum during auditory learning and that the strengthening of these functional connections is selectively goal-directed.

Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds

Science.gov (United States)

Polewski, Przemyslaw; Yao, Wei; Heurich, Marco; Krzystek, Peter; Stilla, Uwe

2018-06-01

In this study, we present a method for improving the quality of automatic single fallen tree stem segmentation in ALS data by applying a specialized constrained conditional random field (CRF). The entire processing pipeline is composed of two steps. First, short stem segments of equal length are detected and a subset of them is selected for further processing, while in the second step the chosen segments are merged to form entire trees. The first step is accomplished using the specialized CRF defined on the space of segment labelings, capable of finding segment candidates which are easier to merge subsequently. To achieve this, the CRF considers not only the features of every candidate individually, but incorporates pairwise spatial interactions between adjacent segments into the model. In particular, pairwise interactions include a collinearity/angular deviation probability which is learned from training data as well as the ratio of spatial overlap, whereas unary potentials encode a learned probabilistic model of the laser point distribution around each segment. Each of these components enters the CRF energy with its own balance factor. To process previously unseen data, we first calculate the subset of segments for merging on a grid of balance factors by minimizing the CRF energy. Then, we perform the merging and rank the balance configurations according to the quality of their resulting merged trees, obtained from a learned tree appearance model. The final result is derived from the top-ranked configuration. We tested our approach on 5 plots from the Bavarian Forest National Park using reference data acquired in a field inventory. Compared to our previous segment selection method without pairwise interactions, an increase in detection correctness and completeness of up to 7 and 9 percentage points, respectively, was observed.

Evolution and natural selection: learning by playing and reflecting

Directory of Open Access Journals (Sweden)

David Herrero

2014-01-01

Full Text Available Scientific literacy is more than the simple reproduction of traditional school science knowledge and requires a set of skills, among them identifying scientific issues, explaining phenomena scientifically and using scientific evidence. Several studies have indicated that playing computer games in the classroom can support the development of students’ conceptual understanding about scientific phenomena and theories. Our paper presents a research study where the role of the video game Spore as a learning tool was analysed in a Biology class. An ethnographical perspective served as the framework for the organization and development of a workshop comprised of five sessions with 22 4th grade students, and their Biology teacher. The results show that this video game could become an interesting learning tool to improve students’ understanding of evolution and natural selection. The students could combine their previous knowledge with the academic knowledge obtained though the simulation presented by the video game. To sum up, an attempt has been made to give some empirical guidance about effective approaches to the utilisation of games in classrooms, additionally paying attention to a number of concerns related to the effectiveness of video games as learning tools.

Informative sensor selection and learning for prediction of lower limb kinematics using generative stochastic neural networks.

Science.gov (United States)

Eunsuk Chong; Taejin Choi; Hyungmin Kim; Seung-Jong Kim; Yoha Hwang; Jong Min Lee

2017-07-01

We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and real-valued. We addressed this problem by applying a generative stochastic neural network called the restricted Boltzmann machine, through which we could perform sampling based probability estimation. The mutual informations between inputs and outputs are evaluated in each backward sensor elimination step, and the least informative sensor is removed with its network connections. The entire network is fine-tuned by maximizing conditional likelihood in each step. Experimental results are shown for 4 healthy subjects walking with various speeds, recording 64 sensor measurements including electromyogram, acceleration, and foot-pressure sensors attached on both lower limbs for predicting hip and knee joint angles. For test set of walking with arbitrary speed, our results show that our suggested method can select informative sensors while maintaining a good prediction accuracy.

Conditions for sports activities in selected organisations for disabled individuals in the town Teplice

OpenAIRE

Shaymardanova, Karina

2010-01-01

3 ABSTRACT Name: Conditions for sports activities in selected organisations for disabled individuals in the town of Teplice. Aim of the work: Monitoring sports activities as a socialisation factor for integration and socialisation of individuals with disabilities caused by poliomyelitis in the selected town of Teplice. Another objective was to describe conditions of sports activities and to determine opinions of handicapped individuals on attendance at sports groups in selected centres as wel...

Learning by Exporting or Self Selection? Which Way for the Kenyan ...

African Journals Online (AJOL)

The results obtained show some significant differences between exporters and non exporters. The results also show some evidence for learning-by-doing hypothesis and evidence for self-selection of more efficient firms into exporting. On the policy front the paper calls for more focus on improving exports in order for Kenya ...

Selective Attention Enhances Beta-Band Cortical Oscillation to Speech under "Cocktail-Party" Listening Conditions.

Science.gov (United States)

Gao, Yayue; Wang, Qian; Ding, Yu; Wang, Changming; Li, Haifeng; Wu, Xihong; Qu, Tianshu; Li, Liang

2017-01-01

Human listeners are able to selectively attend to target speech in a noisy environment with multiple-people talking. Using recordings of scalp electroencephalogram (EEG), this study investigated how selective attention facilitates the cortical representation of target speech under a simulated "cocktail-party" listening condition with speech-on-speech masking. The result shows that the cortical representation of target-speech signals under the multiple-people talking condition was specifically improved by selective attention relative to the non-selective-attention listening condition, and the beta-band activity was most strongly modulated by selective attention. Moreover, measured with the Granger Causality value, selective attention to the single target speech in the mixed-speech complex enhanced the following four causal connectivities for the beta-band oscillation: the ones (1) from site FT7 to the right motor area, (2) from the left frontal area to the right motor area, (3) from the central frontal area to the right motor area, and (4) from the central frontal area to the right frontal area. However, the selective-attention-induced change in beta-band causal connectivity from the central frontal area to the right motor area, but not other beta-band causal connectivities, was significantly correlated with the selective-attention-induced change in the cortical beta-band representation of target speech. These findings suggest that under the "cocktail-party" listening condition, the beta-band oscillation in EEGs to target speech is specifically facilitated by selective attention to the target speech that is embedded in the mixed-speech complex. The selective attention-induced unmasking of target speech may be associated with the improved beta-band functional connectivity from the central frontal area to the right motor area, suggesting a top-down attentional modulation of the speech-motor process.

How Much of Language Acquisition Does Operant Conditioning Explain?

Science.gov (United States)

Sturdy, Christopher B.; Nicoladis, Elena

2017-01-01

Since the 1950s, when Chomsky argued that Skinner’s arguments could not explain syntactic acquisition, psychologists have generally avoided explicitly invoking operant or instrumental conditioning as a learning mechanism for language among human children. In this article, we argue that this is a mistake. We focus on research that has been done on language learning in human infants and toddlers in order to illustrate our points. Researchers have ended up inventing learning mechanisms that, in actual practice, not only resemble but also in fact are examples of operant conditioning (OC) by any other name they select. We argue that language acquisition researchers should proceed by first ruling out OC before invoking alternative learning mechanisms. While it is possible that OC cannot explain all of the language acquisition, simple learning mechanisms that work across species may have some explanatory power in children’s language learning. PMID:29163295

How Much of Language Acquisition Does Operant Conditioning Explain?

Directory of Open Access Journals (Sweden)

Christopher B. Sturdy

2017-10-01

Full Text Available Since the 1950s, when Chomsky argued that Skinner’s arguments could not explain syntactic acquisition, psychologists have generally avoided explicitly invoking operant or instrumental conditioning as a learning mechanism for language among human children. In this article, we argue that this is a mistake. We focus on research that has been done on language learning in human infants and toddlers in order to illustrate our points. Researchers have ended up inventing learning mechanisms that, in actual practice, not only resemble but also in fact are examples of operant conditioning (OC by any other name they select. We argue that language acquisition researchers should proceed by first ruling out OC before invoking alternative learning mechanisms. While it is possible that OC cannot explain all of the language acquisition, simple learning mechanisms that work across species may have some explanatory power in children’s language learning.

Learning an operant conditioning task differentially induces gliogenesis in the medial prefrontal cortex and neurogenesis in the hippocampus.

Directory of Open Access Journals (Sweden)

Maximiliano Rapanelli

Full Text Available Circuit modification associated with learning and memory involves multiple events, including the addition and remotion of newborn cells trough adulthood. Adult neurogenesis and gliogenesis were mainly described in models of voluntary exercise, enriched environments, spatial learning and memory task; nevertheless, it is unknown whether it is a common mechanism among different learning paradigms, like reward dependent tasks. Therefore, we evaluated cell proliferation, neurogenesis, astrogliogenesis, survival and neuronal maturation in the medial prefrontal cortex (mPFC and the hippocampus (HIPP during learning an operant conditioning task. This was performed by using endogenous markers of cell proliferation, and a bromodeoxiuridine (BrdU injection schedule in two different phases of learning. Learning an operant conditioning is divided in two phases: a first phase when animals were considered incompletely trained (IT, animals that were learning the task when they performed between 50% and 65% of the responses, and a second phase when animals were considered trained (Tr, animals that completely learned the task when they reached 100% of the responses with a latency time lower than 5 seconds. We found that learning an operant conditioning task promoted cell proliferation in both phases of learning in the mPFC and HIPP. Additionally, the results presented showed that astrogliogenesis was induced in the medial prefrontal cortex (mPFC in both phases, however, the first phase promoted survival of these new born astrocytes. On the other hand, an increased number of new born immature neurons was observed in the HIPP only in the first phase of learning, whereas, decreased values were observed in the second phase. Finally, we found that neuronal maturation was induced only during the first phase. This study shows for the first time that learning a reward-dependent task, like the operant conditioning, promotes neurogenesis, astrogliogenesis, survival and

Learning-dependent and -independent enhancement of mitral/tufted cell glomerular odor responses following olfactory fear conditioning in awake mice.

Science.gov (United States)

Ross, Jordan M; Fletcher, Max L

2018-04-18

Associative fear learning produces fear toward the conditioned stimulus (CS) and often generalization, the expansion of fear from the CS to similar, unlearned stimuli. However, how fear learning affects early sensory processing of learned and unlearned stimuli in relation to behavioral fear responses to these stimuli remains unclear. We subjected male and female mice expressing the fluorescent calcium indicator GCaMP3 in olfactory bulb mitral and tufted cells to a classical olfactory fear conditioning paradigm. We then used awake, in vivo calcium imaging to quantify learning-induced changes in glomerular odor responses, which constitute the first site of olfactory processing in the brain. The results demonstrate that odor-shock pairing non-specifically enhances glomerular odor representations in a learning-dependent manner and increases representational similarity between the CS and non-conditioned odors, potentially priming the system towards generalization of learned fear. Additionally, CS-specific glomerular enhancements remain even when associative learning is blocked, suggesting two separate mechanisms lead to enhanced glomerular responses following odor-shock pairings. SIGNIFICANCE STATEMENT In the olfactory bulb (OB), odors are uniquely coded in a spatial map that represents odor identity, making the OB a unique model system for investigating how learned fear alters sensory processing. Classical fear conditioning causes fear of the conditioned stimulus (CS) and of neutral stimuli, known as generalization. Combining fear conditioning with fluorescent calcium imaging of OB glomeruli, we found enhanced glomerular responses of the CS as well as neutral stimuli in awake mice, which mirrors fear generalization. We report that CS and neutral stimuli enhancements are, respectively, learning- independent and learning-dependent. Together, these results reveal distinct mechanisms leading to enhanced OB processing of fear-inducing stimuli and provide important

Dual learning processes in interactive skill acquisition.

Science.gov (United States)

Fu, Wai-Tat; Anderson, John R

2008-06-01

Acquisition of interactive skills involves the use of internal and external cues. Experiment 1 showed that when actions were interdependent, learning was effective with and without external cues in the single-task condition but was effective only with the presence of external cues in the dual-task condition. In the dual-task condition, actions closer to the feedback were learned faster than actions farther away but this difference was reversed in the single-task condition. Experiment 2 tested how knowledge acquired in single and dual-task conditions would transfer to a new reward structure. Results confirmed the two forms of learning mediated by the secondary task: A declarative memory encoding process that simultaneously assigned credits to actions and a reinforcement-learning process that slowly propagated credits backward from the feedback. The results showed that both forms of learning were engaged during training, but only at the response selection stage, one form of knowledge may dominate over the other depending on the availability of attentional resources. (c) 2008 APA, all rights reserved

Condition-dependence, pleiotropy and the handicap principle of sexual selection in melanin-based colouration.

Science.gov (United States)

Roulin, Alexandre

2016-05-01

The signalling function of melanin-based colouration is debated. Sexual selection theory states that ornaments should be costly to produce, maintain, wear or display to signal quality honestly to potential mates or competitors. An increasing number of studies supports the hypothesis that the degree of melanism covaries with aspects of body condition (e.g. body mass or immunity), which has contributed to change the initial perception that melanin-based colour ornaments entail no costs. Indeed, the expression of many (but not all) melanin-based colour traits is weakly sensitive to the environment but strongly heritable suggesting that these colour traits are relatively cheap to produce and maintain, thus raising the question of how such colour traits could signal quality honestly. Here I review the production, maintenance and wearing/displaying costs that can generate a correlation between melanin-based colouration and body condition, and consider other evolutionary mechanisms that can also lead to covariation between colour and body condition. Because genes controlling melanic traits can affect numerous phenotypic traits, pleiotropy could also explain a linkage between body condition and colouration. Pleiotropy may result in differently coloured individuals signalling different aspects of quality that are maintained by frequency-dependent selection or local adaptation. Colouration may therefore not signal absolute quality to potential mates or competitors (e.g. dark males may not achieve a higher fitness than pale males); otherwise genetic variation would be rapidly depleted by directional selection. As a consequence, selection on heritable melanin-based colouration may not always be directional, but mate choice may be conditional to environmental conditions (i.e. context-dependent sexual selection). Despite the interest of evolutionary biologists in the adaptive value of melanin-based colouration, its actual role in sexual selection is still poorly understood. �

Changing Conditions for Networked Learning?

DEFF Research Database (Denmark)

Ryberg, Thomas

2011-01-01

in describing the novel pedagogical potentials of these new technologies and practices (e.g. in debates around virtual learning environments versus personal learning environment). Likewise, I shall briefly discuss the notions of ‘digital natives’ or ‘the net generation’ from a critical perspective...... of social technologies. I argue that we are seeing the emergence of new architectures and scales of participation, collaboration and networking e.g. through interesting formations of learning networks at different levels of scale, for different purposes and often bridging boundaries such as formal...

REM sleep selectively prunes and maintains new synapses in development and learning.

Science.gov (United States)

Li, Wei; Ma, Lei; Yang, Guang; Gan, Wen-Biao

2017-03-01

The functions and underlying mechanisms of rapid eye movement (REM) sleep remain unclear. Here we show that REM sleep prunes newly formed postsynaptic dendritic spines of layer 5 pyramidal neurons in the mouse motor cortex during development and motor learning. This REM sleep-dependent elimination of new spines facilitates subsequent spine formation during development and when a new motor task is learned, indicating a role for REM sleep in pruning to balance the number of new spines formed over time. Moreover, REM sleep also strengthens and maintains newly formed spines, which are critical for neuronal circuit development and behavioral improvement after learning. We further show that dendritic calcium spikes arising during REM sleep are important for pruning and strengthening new spines. Together, these findings indicate that REM sleep has multifaceted functions in brain development, learning and memory consolidation by selectively eliminating and maintaining newly formed synapses via dendritic calcium spike-dependent mechanisms.

Physiotherapy students' perspectives of online e-learning for interdisciplinary management of chronic health conditions: a qualitative study.

Science.gov (United States)

Gardner, Peter; Slater, Helen; Jordan, Joanne E; Fary, Robyn E; Chua, Jason; Briggs, Andrew M

2016-02-16

To qualitatively explore physiotherapy students' perceptions of online e-learning for chronic disease management using a previously developed, innovative and interactive, evidence-based, e-learning package: Rheumatoid Arthritis for Physiotherapists e-Learning (RAP-eL). Physiotherapy students participated in three focus groups in Perth, Western Australia. Purposive sampling was employed to ensure maximum heterogeneity across age, gender and educational background. To explore students' perspectives on the advantages and disadvantages of online e-learning, ways to enhance e-learning, and information/learning gaps in relation to interdisciplinary management of chronic health conditions, a semi-structured interview schedule was developed. Verbatim transcripts were analysed using inductive methods within a grounded theory approach to derive key themes. Twenty-three students (78 % female; 39 % with previous tertiary qualification) of mean (SD) age 23 (3.6) years participated. Students expressed a preference for a combination of both online e-learning and lecture-style learning formats for chronic disease management, citing flexibility to work at one's own pace and time, and access to comprehensive information as advantages of e-learning learning. Personal interaction and ability to clarify information immediately were considered advantages of lecture-style formats. Perceived knowledge gaps included practical application of interdisciplinary approaches to chronic disease management and developing and implementing physiotherapy management plans for people with chronic health conditions. Physiotherapy students preferred multi-modal and blended formats for learning about chronic disease management. This study highlights the need for further development of practically-oriented knowledge and skills related to interdisciplinary care for people with chronic conditions among physiotherapy students. While RAP-eL focuses on rheumatoid arthritis, the principles of learning apply to

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

Directory of Open Access Journals (Sweden)

Krzysztof Rataj

2018-05-01

Full Text Available The identification of subtype-selective GPCR (G-protein coupled receptor ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP. This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivities.

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

Directory of Open Access Journals (Sweden)

Liansheng Liu

2016-04-01

Full Text Available In a complex system, condition monitoring (CM can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR. The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA Ames Research Center and have been used as Prognostics and Health Management (PHM challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies.

Science.gov (United States)

Mina, Marco; Raynaud, Franck; Tavernari, Daniele; Battistello, Elena; Sungalee, Stephanie; Saghafinia, Sadegh; Laessle, Titouan; Sanchez-Vega, Francisco; Schultz, Nikolaus; Oricchio, Elisa; Ciriello, Giovanni

2017-08-14

Cancer evolves through the emergence and selection of molecular alterations. Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. These results provide a framework for the design of strategies to predict cancer progression and therapeutic response. Copyright © 2017 Elsevier Inc. All rights reserved.

An Evaluation Model To Select an Integrated Learning System in a Large, Suburban School District.

Science.gov (United States)

Curlette, William L.; And Others

The systematic evaluation process used in Georgia's DeKalb County School System to purchase comprehensive instructional software--an integrated learning system (ILS)--is described, and the decision-making model for selection is presented. Selection and implementation of an ILS were part of an instructional technology plan for the DeKalb schools…

Dissociable Hippocampal and Amygdalar D1-like receptor contribution to Discriminated Pavlovian conditioned approach learning

Science.gov (United States)

Andrzejewski, Matthew E; Ryals, Curtis

2016-01-01

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336

Parents' learning needs and preferences when sharing management of their child's long-term/chronic condition: A systematic review.

Science.gov (United States)

Nightingale, Ruth; Friedl, Simone; Swallow, Veronica

2015-11-01

This review aimed to (1) identify parents' learning needs and preferences when sharing the management of their child's long-term/chronic (long-term) condition and (2) inform healthcare professional support provided to parents across the trajectory. We conducted a literature search in seven health databases from 1990 to 2013. The quality of included studies was assessed using a critical appraisal tool developed for reviewing the strengths and weaknesses of qualitative, quantitative and mixed methods studies. Twenty-three studies met our criteria and were included in the review. Three themes emerged from synthesis of the included studies: (1) parents' learning needs and preferences (2) facilitators to parents' learning, and (3) barriers to parents' learning. Asking parents directly about their learning needs and preferences may be the most reliable way for healthcare professionals to ascertain how to support and promote individual parents' learning when sharing management of their child's long-term condition. With the current emphasis on parent-healthcare professional shared management of childhood long-term conditions, it is recommended that professionals base their assessment of parents' learning needs and preferences on identified barriers and facilitators to parental learning. This should optimise delivery of home-based care, thereby contributing to improved clinical outcomes for the child. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Selective role for DNMT3a in learning and memory.

Science.gov (United States)

Morris, Michael J; Adachi, Megumi; Na, Elisa S; Monteggia, Lisa M

2014-11-01

Methylation of cytosine nucleotides is governed by DNA methyltransferases (DNMTs) that establish de novo DNA methylation patterns in early embryonic development (e.g., DNMT3a and DNMT3b) or maintain those patterns on hemimethylated DNA in dividing cells (e.g., DNMT1). DNMTs continue to be expressed at high levels in mature neurons, however their impact on neuronal function and behavior are unclear. To address this issue we examined DNMT1 and DNMT3a expression following associative learning. We also generated forebrain specific conditional Dnmt1 or Dnmt3a knockout mice and characterized them in learning and memory paradigms as well as for alterations in long-term potentiation (LTP) and synaptic plasticity. Here, we report that experience in an associative learning task impacts expression of Dnmt3a, but not Dnmt1, in brain areas that mediate learning of this task. We also found that Dnmt3a knockout mice, and not Dnmt1 knockouts have synaptic alterations as well as learning deficits on several associative and episodic memory tasks. These findings indicate that the de novo DNA methylating enzyme DNMT3a in postmitotic neurons is necessary for normal memory formation and its function cannot be substituted by the maintenance DNA methylating enzyme DNMT1. Copyright © 2014 Elsevier Inc. All rights reserved.

Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

Science.gov (United States)

Varga-Haszonits, I.; Kondor, I.

2007-11-01

This paper investigates the efficiency of minimum variance portfolio optimization for stock price movements following the Constant Conditional Correlation GARCH process proposed by Bollerslev. Simulations show that the quality of portfolio selection can be improved substantially by computing optimal portfolio weights from conditional covariances instead of unconditional ones. Measurement noise can be further reduced by applying some filtering method on the conditional correlation matrix (such as Random Matrix Theory based filtering). As an empirical support for the simulation results, the analysis is also carried out for a time series of S&P500 stock prices.

Mechanisms of radiation-induced conditioned taste aversion learning

International Nuclear Information System (INIS)

Rabin, B.M.; Hunt, W.A.

1986-01-01

The literature on taste aversion learning is reviewed and discussed, with particular emphasis on those studies that have used exposure to ionizing radiation as an unconditioned stimulus to produce a conditioned taste aversion. The primary aim of the review is to attempt to define the mechanisms that lead to the initiation of the taste aversion response following exposure to ionizing radiation. Studies using drug treatments to produce a taste aversion have been included to the extent that they are relevant to understanding the mechanisms by which exposure to ionizing radiation can affect the behavior of the organism. 141 references

USING A MULTI CRITERIA DECISION MAKING APPROACH FOR OPEN AND DISTANCE LEARNING SYSTEM SELECTION

OpenAIRE

KAMIŞLI ÖZTÜRK, Zehra

2015-01-01

Today, there's a wide variety of open and distance learning (ODL) systems around the world. Herein, for lifelong learning how to select an ODL program becomes a critic question for a learner who wants to extent abilities on his/her career path. This is a complex decision problem with interdependent criteria. The Analytic Network Process (ANP) is a multicriteria decision making methodology  that  reflects  these  interdependencies.  Within &...

Feature Selection Methods for Zero-Shot Learning of Neural Activity

Directory of Open Access Journals (Sweden)

Carlos A. Caceres

2017-06-01

Full Text Available Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

Science.gov (United States)

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

Deep Learning Questions Can Help Selection of High Ability Candidates for Universities

Science.gov (United States)

Mellanby, Jane; Cortina-Borja, Mario; Stein, John

2009-01-01

Selection of students for places at universities mainly depends on GCSE grades and predictions of A-level grades, both of which tend to favour applicants from independent schools. We have therefore developed a new type of test that would measure candidates' "deep learning" approach since this assesses the motivation and creative thinking…

Stochastic subset selection for learning with kernel machines.

Science.gov (United States)

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

The Effects of Study Tasks in a Computer-Based Chemistry Learning Environment

Science.gov (United States)

Urhahne, Detlef; Nick, Sabine; Poepping, Anna Christin; Schulz , Sarah Jayne

2013-01-01

The present study examines the effects of different study tasks on the acquisition of knowledge about acids and bases in a computer-based learning environment. Three different task formats were selected to create three treatment conditions: learning with gap-fill and matching tasks, learning with multiple-choice tasks, and learning only from text…

Technical guide for monitoring selected conditions related to wilderness character

Science.gov (United States)

Peter Landres; Steve Boutcher; Liese Dean; Troy Hall; Tamara Blett; Terry Carlson; Ann Mebane; Carol Hardy; Susan Rinehart; Linda Merigliano; David N. Cole; Andy Leach; Pam Wright; Deb Bumpus

2009-01-01

The purpose of monitoring wilderness character is to improve wilderness stewardship by providing managers a tool to assess how selected actions and conditions related to wilderness character are changing over time. Wilderness character monitoring provides information to help answer two key questions about wilderness character and wilderness stewardship: 1. How is...

Selective Attention Enhances Beta-Band Cortical Oscillation to Speech under “Cocktail-Party” Listening Conditions

Science.gov (United States)

Gao, Yayue; Wang, Qian; Ding, Yu; Wang, Changming; Li, Haifeng; Wu, Xihong; Qu, Tianshu; Li, Liang

2017-01-01

Human listeners are able to selectively attend to target speech in a noisy environment with multiple-people talking. Using recordings of scalp electroencephalogram (EEG), this study investigated how selective attention facilitates the cortical representation of target speech under a simulated “cocktail-party” listening condition with speech-on-speech masking. The result shows that the cortical representation of target-speech signals under the multiple-people talking condition was specifically improved by selective attention relative to the non-selective-attention listening condition, and the beta-band activity was most strongly modulated by selective attention. Moreover, measured with the Granger Causality value, selective attention to the single target speech in the mixed-speech complex enhanced the following four causal connectivities for the beta-band oscillation: the ones (1) from site FT7 to the right motor area, (2) from the left frontal area to the right motor area, (3) from the central frontal area to the right motor area, and (4) from the central frontal area to the right frontal area. However, the selective-attention-induced change in beta-band causal connectivity from the central frontal area to the right motor area, but not other beta-band causal connectivities, was significantly correlated with the selective-attention-induced change in the cortical beta-band representation of target speech. These findings suggest that under the “cocktail-party” listening condition, the beta-band oscillation in EEGs to target speech is specifically facilitated by selective attention to the target speech that is embedded in the mixed-speech complex. The selective attention-induced unmasking of target speech may be associated with the improved beta-band functional connectivity from the central frontal area to the right motor area, suggesting a top-down attentional modulation of the speech-motor process. PMID:28239344

Extinction of Conditioned Fear is Better Learned and Recalled in the Morning than in the Evening

OpenAIRE

Pace-Schott, Edward F.; Spencer, Rebecca M.C.; Vijayakumar, Shilpa; Ahmed, Nafis; Verga, Patrick W.; Orr, Scott P.; Pitman, Roger K.; Milad, Mohammed R.

2013-01-01

Sleep helps emotional memories consolidate and may promote generalization of fear extinction memory. We examined whether extinction learning and memory might differ in the morning and evening due, potentially, to circadian and/or sleep-homeostatic factors. Healthy men (N=109) in 6 groups completed a 2-session protocol. In Session 1, fear conditioning was followed by extinction learning. Partial reinforcement with mild electric shock produced conditioned skin conductance responses (SCR) to 2 d...

Selective reminding of prospective memory in Multiple Sclerosis.

Science.gov (United States)

McKeever, Joshua D; Schultheis, Maria T; Sim, Tiffanie; Goykhman, Jessica; Patrick, Kristina; Ehde, Dawn M; Woods, Steven Paul

2017-04-19

Multiple sclerosis (MS) is associated with prospective memory (PM) deficits, which may increase the risk of poor functional/health outcomes such as medication non-adherence. This study examined the potential benefits of selective reminding to enhance PM functioning in persons with MS. Twenty-one participants with MS and 22 healthy adults (HA) underwent a neuropsychological battery including a Selective Reminding PM (SRPM) experimental procedure. Participants were randomly assigned to either: (1) a selective reminding condition in which participants learn (to criterion) eight prospective memory tasks in a Selective Reminding format; or (2) a single trial encoding condition (1T). A significant interaction was demonstrated, with MS participants receiving greater benefit than HAs from the SR procedure in terms of PM performance. Across diagnostic groups, participants in the SR conditions (vs. 1T conditions) demonstrated significantly better PM performance. Individuals with MS were impaired relative to HAs in the 1T condition, but performance was statistically comparable in the SR condition. This preliminary study suggests that selective reminding can be used to enhance PM cue detection and retrieval in MS. The extent to which selective reminding of PM is effective in naturalistic settings and for health-related behaviours in MS remains to be determined.

Mediating Global Reforms Locally: A Study of the Enabling Conditions for Promoting Active Learning in a Maldivian Island School

Science.gov (United States)

Di Biase, Rhonda

2017-01-01

This paper explores active learning reform in the small state of the Maldives. Acknowledging the implementation challenges of active learning approaches globally, the study explored the policy-practice intersection by examining the experiences of one island school and its approach to promoting active learning pedagogy. The school was selected for…

Naïve and Robust: Class-Conditional Independence in Human Classification Learning

Science.gov (United States)

Jarecki, Jana B.; Meder, Björn; Nelson, Jonathan D.

2018-01-01

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…

Learning of conditioned reflexes of the Wistar rat under intermittent action of low CO concentrations

Energy Technology Data Exchange (ETDEWEB)

Zorn, H.

1972-04-01

The influence of an intermittent long-time exposure to a concentration of 150 ppm carbon monoxide on the ability to learn conditioned reflexes was investigated with Wistar rats. Half the 80 rats employed and divided into intelligence groups were exposed to this concentration at night five times for 8 hr weekly. The carboxyhemoglobin level in the blood of these animals increased to 7-13 percent. After an adequate interval for CO elimination, the rats exposed and the control animals were trained to develop a conditioned flight reflex. At a later date, the results were ascertained. With regard to the progress in learning this action, the CO-exposed animals showed a significant reduction in performance (longer learning time, more frequent deficient behavior, and inclination for stupor and anxious denial).

Visual perceptual learning by operant conditioning training follows rules of contingency

Science.gov (United States)

Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo

2015-01-01

Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning. PMID:26028984

Visual perceptual learning by operant conditioning training follows rules of contingency.

Science.gov (United States)

Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo

2015-01-01

Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning.

Conditional Mutual Information Based Feature Selection for Classification Task

Czech Academy of Sciences Publication Activity Database

Novovičová, Jana; Somol, Petr; Haindl, Michal; Pudil, Pavel

2007-01-01

Roč. 45, č. 4756 (2007), s. 417-426 ISSN 0302-9743 R&D Projects: GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Pattern classification * feature selection * conditional mutual information * text categorization Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.402, year: 2005

Effects of sleep on memory for conditioned fear and fear extinction

Science.gov (United States)

Pace-Schott, Edward F.; Germain, Anne; Milad, Mohammed R.

2015-01-01

Learning and memory for extinction of conditioned fear is a basic mammalian mechanism for regulating negative emotion. Sleep promotes both the consolidation of memory and the regulation of emotion. Sleep can influence consolidation and modification of memories associated with both fear and its extinction. After brief overviews of the behavior and neural circuitry associated with fear conditioning, extinction learning and extinction memory in the rodent and human, interactions of sleep with these processes will be examined. Animal and human studies suggest that sleep can serve to consolidate both fear and extinction memory. In humans, sleep also promotes generalization of extinction memory. Time-of-day effects on extinction learning and generalization are also seen. REM may be a sleep stage of particular importance for the consolidation of both fear and extinction memory as evidenced by selective REM deprivation experiments. REM sleep is accompanied by selective activation of the same limbic structures implicated in the learning and memory of fear and extinction. Preliminary evidence also suggests extinction learning can take place during slow wave sleep. Study of low-level processes such as conditioning, extinction and habituation may allow sleep effects on emotional memory to be identified and inform study of sleep’s effects on more complex, emotionally salient declarative memories. Anxiety disorders are marked by impairments of both sleep and extinction memory. Improving sleep quality may ameliorate anxiety disorders by strengthening naturally acquired extinction. Strategically timed sleep may be used to enhance treatment of anxiety by strengthening therapeutic extinction learned via exposure therapy. PMID:25894546

Effects of sleep on memory for conditioned fear and fear extinction.

Science.gov (United States)

Pace-Schott, Edward F; Germain, Anne; Milad, Mohammed R

2015-07-01

Learning and memory for extinction of conditioned fear is a basic mammalian mechanism for regulating negative emotion. Sleep promotes both the consolidation of memory and the regulation of emotion. Sleep can influence consolidation and modification of memories associated with both fear and its extinction. After brief overviews of the behavior and neural circuitry associated with fear conditioning, extinction learning, and extinction memory in the rodent and human, interactions of sleep with these processes will be examined. Animal and human studies suggest that sleep can serve to consolidate both fear and extinction memory. In humans, sleep also promotes generalization of extinction memory. Time-of-day effects on extinction learning and generalization are also seen. Rapid eye movement (REM) may be a sleep stage of particular importance for the consolidation of both fear and extinction memory as evidenced by selective REM deprivation experiments. REM sleep is accompanied by selective activation of the same limbic structures implicated in the learning and memory of fear and extinction. Preliminary evidence also suggests extinction learning can take place during slow wave sleep. Study of low-level processes such as conditioning, extinction, and habituation may allow sleep effects on emotional memory to be identified and inform study of sleep's effects on more complex, emotionally salient declarative memories. Anxiety disorders are marked by impairments of both sleep and extinction memory. Improving sleep quality may ameliorate anxiety disorders by strengthening naturally acquired extinction. Strategically timed sleep may be used to enhance treatment of anxiety by strengthening therapeutic extinction learned via exposure therapy. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Place learning overrides innate behaviors in Drosophila.

Science.gov (United States)

Baggett, Vincent; Mishra, Aditi; Kehrer, Abigail L; Robinson, Abbey O; Shaw, Paul; Zars, Troy

2018-03-01

Animals in a natural environment confront many sensory cues. Some of these cues bias behavioral decisions independent of experience, and action selection can reveal a stimulus-response (S-R) connection. However, in a changing environment it would be a benefit for an animal to update behavioral action selection based on experience, and learning might modify even strong S-R relationships. How animals use learning to modify S-R relationships is a largely open question. Three sensory stimuli, air, light, and gravity sources were presented to individual Drosophila melanogaster in both naïve and place conditioning situations. Flies were tested for a potential modification of the S-R relationships of anemotaxis, phototaxis, and negative gravitaxis by a contingency that associated place with high temperature. With two stimuli, significant S-R relationships were abandoned when the cue was in conflict with the place learning contingency. The role of the dunce ( dnc ) cAMP-phosphodiesterase and the rutabaga ( rut ) adenylyl cyclase were examined in all conditions. Both dnc 1 and rut 2080 mutant flies failed to display significant S-R relationships with two attractive cues, and have characteristically lower conditioning scores under most conditions. Thus, learning can have profound effects on separate native S-R relationships in multiple contexts, and mutation of the dnc and rut genes reveal complex effects on behavior. © 2018 Baggett et al.; Published by Cold Spring Harbor Laboratory Press.

Conditions for Contingent Instructors Engaged in the Scholarship of Teaching and Learning

Science.gov (United States)

Vander Kloet, Marie; Frake-Mistak, Mandy; McGinn, Michelle K.; Caldecott, Marion; Aspenlieder, Erin D.; Beres, Jacqueline L.; Fukuzawa, Sherry; Cassidy, Alice; Gill, Apryl

2017-01-01

An increasingly large number of courses in Canadian postsecondary institutions are taught by contingent instructors who hold full- or part-time positions for contractually limited time periods. Despite strong commitments to advancing teaching and learning, the labour and employment conditions for contingent instructors affect the incentives and…

[Cooperative learning for improving healthy housing conditions in Bogota: a case study].

Science.gov (United States)

Torres-Parra, Camilo A; García-Ubaque, Juan C; García-Ubaque, César A

2014-01-01

This was a community-based effort at constructing an educational proposal orientated towards self-empowerment aimed at improving the target population's sanitary, housing and living conditions through cooperative learning. A constructivist approach was adopted based on a programme called "Habitat community manger". The project involved working with fifteen families living in the Mochuelo Bajo barrio in Ciudad Bolívar in Bogotá, Colombia, for identifying the most relevant sanitary aspects for improving their homes and proposing a methodology and organisation for an educational proposal. Twenty-one poor housing-related epidemiological indicators were identified which formed the basis for defining specific problems and establishing a methodology for designing an educational proposal. The course which emerged from the cooperative learning experience was designed to promote the community's skills and education regarding health aimed at improving households' living conditions and ensuring a healthy environment which would allow them to develop an immediate habitat ensuring their own welfare and dignity.

Conditions for selective degradation of lignin by the fungus Ganoderma australis

Energy Technology Data Exchange (ETDEWEB)

Rios, S.; Eyzaguirre, J. (Universidad Catolica de Chile, Santiago (Chile). Lab. de Bioquimica)

1992-08-01

The white-rot fungus Ganoderma australis selectively degrades lignin in the ecosystem 'palo podrido'. Using conditions that simulate those of 'palo podrido' in the laboratory, it was found that low nitrogen content and low O{sub 2} tension stimulate the production of manganese peroxidase and lignin degradation, and depress cellulose degradation and cellulase production. The inverse is found at high nitrogen concentration and high O{sub 2} tension. This agrees with previous results indicating that low O{sub 2} tension and low nitrogen stimulate selective lignin degradation by this fungus. (orig.).

Dutch care innovation units in elderly care: A qualitative study into students' perspectives and workplace conditions for learning.

Science.gov (United States)

Snoeren, Miranda; Volbeda, Patricia; Niessen, Theo J H; Abma, Tineke A

2016-03-01

To promote workplace learning for staff as well as students, a partnership was formed between a residential care organisation for older people and several nursing faculties in the Netherlands. This partnership took the form of two care innovation units; wards where qualified staff, students and nurse teachers collaborate to integrate care, education, innovation and research. In this article, the care innovation units as learning environments are studied from a student perspective to deepen understandings concerning the conditions that facilitate learning. A secondary analysis of focus groups, held with 216 nursing students over a period of five years, revealed that students are satisfied about the units' learning potential, which is formed by various inter-related and self-reinforcing affordances: co-constructive learning and working, challenging situations and activities, being given responsibility and independence, and supportive and recognisable learning structures. Time constraints had a negative impact on the units' learning potential. It is concluded that the learning potential of the care innovation units was enhanced by realising certain conditions, like learning structures and activities. The learning potential was also influenced, however, by the non-controllable and dynamic interaction of various elements within the context. Suggestions for practice and further research are offered. Copyright © 2015 Elsevier Ltd. All rights reserved.

Skill learning and the evolution of social learning mechanisms.

Science.gov (United States)

van der Post, Daniel J; Franz, Mathias; Laland, Kevin N

2016-08-24

Social learning is potentially advantageous, but evolutionary theory predicts that (i) its benefits may be self-limiting because social learning can lead to information parasitism, and (ii) these limitations can be mitigated via forms of selective copying. However, these findings arise from a functional approach in which learning mechanisms are not specified, and which assumes that social learning avoids the costs of asocial learning but does not produce information about the environment. Whether these findings generalize to all kinds of social learning remains to be established. Using a detailed multi-scale evolutionary model, we investigate the payoffs and information production processes of specific social learning mechanisms (including local enhancement, stimulus enhancement and observational learning) and their evolutionary consequences in the context of skill learning in foraging groups. We find that local enhancement does not benefit foraging success, but could evolve as a side-effect of grouping. In contrast, stimulus enhancement and observational learning can be beneficial across a wide range of environmental conditions because they generate opportunities for new learning outcomes. In contrast to much existing theory, we find that the functional outcomes of social learning are mechanism specific. Social learning nearly always produces information about the environment, and does not always avoid the costs of asocial learning or support information parasitism. Our study supports work emphasizing the value of incorporating mechanistic detail in functional analyses.

Implicit visual learning and the expression of learning.

Science.gov (United States)

Haider, Hilde; Eberhardt, Katharina; Kunde, Alexander; Rose, Michael

2013-03-01

Although the existence of implicit motor learning is now widely accepted, the findings concerning perceptual implicit learning are ambiguous. Some researchers have observed perceptual learning whereas other authors have not. The review of the literature provides different reasons to explain this ambiguous picture, such as differences in the underlying learning processes, selective attention, or differences in the difficulty to express this knowledge. In three experiments, we investigated implicit visual learning within the original serial reaction time task. We used different response devices (keyboard vs. mouse) in order to manipulate selective attention towards response dimensions. Results showed that visual and motor sequence learning differed in terms of RT-benefits, but not in terms of the amount of knowledge assessed after training. Furthermore, visual sequence learning was modulated by selective attention. However, the findings of all three experiments suggest that selective attention did not alter implicit but rather explicit learning processes. Copyright © 2012 Elsevier Inc. All rights reserved.

An improved segmentation-based HMM learning method for Condition-based Maintenance

International Nuclear Information System (INIS)

Liu, T; Lemeire, J; Cartella, F; Meganck, S

2012-01-01

In the domain of condition-based maintenance (CBM), persistence of machine states is a valid assumption. Based on this assumption, we present an improved Hidden Markov Model (HMM) learning algorithm for the assessment of equipment states. By a good estimation of initial parameters, more accurate learning can be achieved than by regular HMM learning methods which start with randomly chosen initial parameters. It is also better in avoiding getting trapped in local maxima. The data is segmented with a change-point analysis method which uses a combination of cumulative sum charts (CUSUM) and bootstrapping techniques. The method determines a confidence level that a state change happens. After the data is segmented, in order to label and combine the segments corresponding to the same states, a clustering technique is used based on a low-pass filter or root mean square (RMS) values of the features. The segments with their labelled hidden state are taken as 'evidence' to estimate the parameters of an HMM. Then, the estimated parameters are served as initial parameters for the traditional Baum-Welch (BW) learning algorithms, which are used to improve the parameters and train the model. Experiments on simulated and real data demonstrate that both performance and convergence speed is improved.

Management of select bacterial and parasitic conditions of raptors.

Science.gov (United States)

Willette, Michelle; Ponder, Julia; Cruz-Martinez, Luis; Arent, Lori; Bueno Padilla, Irene; de Francisco, Olga Nicolas; Redig, Patrick

2009-09-01

Raptors are susceptible to a broad array of established and emerging bacterial and parasitic diseases, including babesiosis, chlamydiosis, clostridiosis, coccidiosis, cryptosporidiosis, malaria, mycobacteriosis, pasteurellosis, salmonellosis, trichomoniasis, and pododermatitis. Many of these conditions are opportunistic and can be easily managed or averted with proper preventive measures related to captive management, husbandry and diet, and veterinary care. Once infected, treatment must be prompt, appropriate, and judicious. This article examines the significance, diagnosis, management, and prevention of select bacterial and parasitic pathogens of raptors.

Extinction of conditioned fear is better learned and recalled in the morning than in the evening.

Science.gov (United States)

Pace-Schott, Edward F; Spencer, Rebecca M C; Vijayakumar, Shilpa; Ahmed, Nafis A K; Verga, Patrick W; Orr, Scott P; Pitman, Roger K; Milad, Mohammed R

2013-11-01

Sleep helps emotional memories consolidate and may promote generalization of fear extinction memory. We examined whether extinction learning and memory might differ in the morning and evening due, potentially, to circadian and/or sleep-homeostatic factors. Healthy men (N = 109) in 6 groups completed a 2-session protocol. In Session 1, fear conditioning was followed by extinction learning. Partial reinforcement with mild electric shock produced conditioned skin conductance responses (SCRs) to 2 differently colored lamps (CS+), but not a third color (CS-), within the computer image of a room (conditioning context). One CS+ (CS + E) but not the other (CS + U) was immediately extinguished by un-reinforced presentations in a different room (extinction context). Delay durations of 3 h (within AM or PM), 12 h (morning-to-evening or evening-to-morning) or 24 h (morning-to-morning or evening-to-evening) followed. In Session 2, extinction recall and contextual fear renewal were tested. We observed no significant effects of the delay interval on extinction memory but did observe an effect of time-of-day. Fear extinction was significantly better if learned in the morning (p = .002). Collapsing across CS + type, there was smaller morning differential SCR at both extinction recall (p = .003) and fear renewal (p = .005). Morning extinction recall showed better generalization from the CS + E to CS + U with the response to the CS + U significantly larger than to the CS + E only in the evening (p = .028). Thus, extinction is learned faster and its memory is better generalized in the morning. Cortisol and testosterone showed the expected greater salivary levels in the morning when higher testosterone/cortisol ratio also predicted better extinction learning. Circadian factors may promote morning extinction. Alternatively, evening homeostatic sleep pressure may impede extinction and favor recall of conditioned fear. Copyright © 2013 Elsevier Ltd. All rights reserved.

New model for selection of applicants at the universities in the conditions Smart-society

Directory of Open Access Journals (Sweden)

Alexandr S. Molchanov

2017-01-01

Full Text Available Smart-society -– a new quality of society. The greatest value to society will be represented by people trained by the new technologies or who require minimal resources to study up to the required level. Universities will use the smarteducational technology, that will require a new level of training the applicant and the other search engines, selection and motivation of applicants. The paper proposes a new model of selection of applicants to universities, which will improve the selection process of students, focusing on the management of individual educational routes learner, since elementary school.The main beneficiaries are the selection system are applicants, potential employer, educational organization. The main core of the system -– its own route management. System functionality includes:– monitoring of the environment (demography, economics, education;– work with targets;– analysis of the previous route and its correlation with the target;– control and fixing the trajectory of learning;– additional control and validation competencies as the demand for an employer or educational institution, and at the request of the trainees;– forecasting and calculation of several route options, with a choice for the student’s request.Taking into account the changes in society and the division of labor, as well as a set of really existing and planned information systems, we can conclude the feasibility of practical implementation of the proposed model. The development of such system of selection of applicants can contribute to:– earlier determining of the future profession with the involvement of employers and educational institutions;– early professional self-determination of applicants;– improve the quality of education at the expense of formation of additional motivation to learn;– possibility of operative management request to the construction or design of the educational program for the educational institution

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

2012-01-10

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Science.gov (United States)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Test-potentiated learning: three independent replications, a disconfirmed hypothesis, and an unexpected boundary condition.

Science.gov (United States)

Wissman, Kathryn T; Rawson, Katherine A

2018-04-01

Arnold and McDermott [(2013). Test-potentiated learning: Distinguishing between direct and indirect effects of testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 940-945] isolated the indirect effects of testing and concluded that encoding is enhanced to a greater extent following more versus fewer practice tests, referred to as test-potentiated learning. The current research provided further evidence for test-potentiated learning and evaluated the covert retrieval hypothesis as an alternative explanation for the observed effect. Learners initially studied foreign language word pairs and then completed either one or five practice tests before restudy occurred. Results of greatest interest concern performance on test trials following restudy for items that were not correctly recalled on the test trials that preceded restudy. Results replicate Arnold and McDermott (2013) by demonstrating that more versus fewer tests potentiate learning when trial time is limited. Results also provide strong evidence against the covert retrieval hypothesis concerning why the effect occurs (i.e., it does not reflect differential covert retrieval during pre-restudy trials). In addition, outcomes indicate that the magnitude of the test-potentiated learning effect decreases as trial length increases, revealing an unexpected boundary condition to test-potentiated learning.

Conditional control in visual selection

NARCIS (Netherlands)

van Zoest, Wieske; Van der Stigchel, Stefan; Donk, Mieke

2017-01-01

Attention and eye movements provide a window into the selective processing of visual information. Evidence suggests that selection is influenced by various factors and is not always under the strategic control of the observer. The aims of this tutorial review are to give a brief introduction to eye

The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.

Science.gov (United States)

Kendal, Jeremy; Giraldeau, Luc-Alain; Laland, Kevin

2009-09-21

Humans and other animals do not use social learning indiscriminately, rather, natural selection has favoured the evolution of social learning rules that make selective use of social learning to acquire relevant information in a changing environment. We present a gene-culture coevolutionary analysis of a small selection of such rules (unbiased social learning, payoff-biased social learning and frequency-dependent biased social learning, including conformism and anti-conformism) in a population of asocial learners where the environment is subject to a constant probability of change to a novel state. We define conditions under which each rule evolves to a genetically polymorphic equilibrium. We find that payoff-biased social learning may evolve under high levels of environmental variation if the fitness benefit associated with the acquired behaviour is either high or low but not of intermediate value. In contrast, both conformist and anti-conformist biases can become fixed when environment variation is low, whereupon the mean fitness in the population is higher than for a population of asocial learners. Our examination of the population dynamics reveals stable limit cycles under conformist and anti-conformist biases and some highly complex dynamics including chaos. Anti-conformists can out-compete conformists when conditions favour a low equilibrium frequency of the learned behaviour. We conclude that evolution, punctuated by the repeated successful invasion of different social learning rules, should continuously favour a reduction in the equilibrium frequency of asocial learning, and propose that, among competing social learning rules, the dominant rule will be the one that can persist with the lowest frequency of asocial learning.

Characterisation of mental health conditions in social media using Informed Deep Learning

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Gkotsis, George; Oellrich, Anika; Velupillai, Sumithra; Liakata, Maria; Hubbard, Tim J. P.; Dobson, Richard J. B.; Dutta, Rina

2017-03-01

The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients’ own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of ‘in the moment’ daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

Feedback-based probabilistic category learning is selectively impaired in attention/hyperactivity deficit disorder.

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Gabay, Yafit; Goldfarb, Liat

2017-07-01

Although Attention-Deficit Hyperactivity Disorder (ADHD) is closely linked to executive function deficits, it has recently been attributed to procedural learning impairments that are quite distinct from the former. These observations challenge the ability of the executive function framework solely to account for the diverse range of symptoms observed in ADHD. A recent neurocomputational model emphasizes the role of striatal dopamine (DA) in explaining ADHD's broad range of deficits, but the link between this model and procedural learning impairments remains unclear. Significantly, feedback-based procedural learning is hypothesized to be disrupted in ADHD because of the involvement of striatal DA in this type of learning. In order to test this assumption, we employed two variants of a probabilistic category learning task known from the neuropsychological literature. Feedback-based (FB) and paired associate-based (PA) probabilistic category learning were employed in a non-medicated sample of ADHD participants and neurotypical participants. In the FB task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of the response. In the PA learning task, participants viewed the cue and its associated outcome simultaneously without receiving an overt response or corrective feedback. In both tasks, participants were trained across 150 trials. Learning was assessed in a subsequent test without a presentation of the outcome or corrective feedback. Results revealed an interesting disassociation in which ADHD participants performed as well as control participants in the PA task, but were impaired compared with the controls in the FB task. The learning curve during FB training differed between the two groups. Taken together, these results suggest that the ability to incrementally learn by feedback is selectively disrupted in ADHD participants. These results are discussed in relation to both

Relational databases for conditions data and event selection in ATLAS

International Nuclear Information System (INIS)

Viegas, F; Hawkings, R; Dimitrov, G

2008-01-01

The ATLAS experiment at LHC will make extensive use of relational databases in both online and offline contexts, running to O(TBytes) per year. Two of the most challenging applications in terms of data volume and access patterns are conditions data, making use of the LHC conditions database, COOL, and the TAG database, that stores summary event quantities allowing a rapid selection of interesting events. Both of these databases are being replicated to regional computing centres using Oracle Streams technology, in collaboration with the LCG 3D project. Database optimisation, performance tests and first user experience with these applications will be described, together with plans for first LHC data-taking and future prospects

Relational databases for conditions data and event selection in ATLAS

Energy Technology Data Exchange (ETDEWEB)

Viegas, F; Hawkings, R; Dimitrov, G [CERN, CH-1211 Geneve 23 (Switzerland)

2008-07-15

The ATLAS experiment at LHC will make extensive use of relational databases in both online and offline contexts, running to O(TBytes) per year. Two of the most challenging applications in terms of data volume and access patterns are conditions data, making use of the LHC conditions database, COOL, and the TAG database, that stores summary event quantities allowing a rapid selection of interesting events. Both of these databases are being replicated to regional computing centres using Oracle Streams technology, in collaboration with the LCG 3D project. Database optimisation, performance tests and first user experience with these applications will be described, together with plans for first LHC data-taking and future prospects.

Example-based learning: comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge.

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Dyer, Joseph-Omer; Hudon, Anne; Montpetit-Tourangeau, Katherine; Charlin, Bernard; Mamede, Sílvia; van Gog, Tamara

2015-03-07

Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of problem-solving skills more than studying worked examples alone. Completion examples are worked examples in which some of the solution steps remain unsolved for learners to complete. Providing learners engaged in example-based learning with self-explanation prompts has been shown to foster increased meaningful learning compared to providing no self-explanation prompts. Concept mapping and concept map study are other instructional activities known to promote meaningful learning. This study compares the effects of self-explaining, completing a concept map and studying a concept map on conceptual knowledge and problem-solving skills among novice learners engaged in example-based learning. Ninety-one physiotherapy students were randomized into three conditions. They performed a pre-test and a post-test to evaluate their gains in conceptual knowledge and problem-solving skills (transfer performance) in intervention selection. They studied three pairs of worked/completion examples in a digital learning environment. Worked examples consisted of a written reasoning process for selecting an optimal physiotherapy intervention for a patient. The completion examples were partially worked out, with the last few problem-solving steps left blank for students to complete. The students then had to engage in additional self-explanation, concept map completion or model concept map study in order to synthesize and deepen their knowledge of the key concepts and problem-solving steps. Pre-test performance did not differ among conditions. Post-test conceptual knowledge was higher (P example and completion example strategies to foster intervention selection.

Imaging learning and memory: classical conditioning.

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Schreurs, B G; Alkon, D L

2001-12-15

The search for the biological basis of learning and memory has, until recently, been constrained by the limits of technology to classic anatomic and electrophysiologic studies. With the advent of functional imaging, we have begun to delve into what, for many, was a "black box." We review several different types of imaging experiments, including steady state animal experiments that image the functional labeling of fixed tissues, and dynamic human studies based on functional imaging of the intact brain during learning. The data suggest that learning and memory involve a surprising conservation of mechanisms and the integrated networking of a number of structures and processes. Copyright 2001 Wiley-Liss, Inc.

The Toyota Production Systems fundamental nature at selected South African organisations A learning perspective

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Nortje, F. D.

2013-05-01

Full Text Available The Toyota Production System (TPS has been cited as being the pinnacle of continuous improvement approaches in manufacturing organisations, and many models of the TPS are well known. However, some authors question the effectiveness of established approaches, and propose Batesons theory of learning [1] to be an effective way to explain phenomena like the TPS. This paper investigates the degree to which TPS elements are found in selected South African organisations. It constructs a model of the TPS using Bateson's theory of learning as a framework. The adoption of TPS elements is investigated through multiple qualitative case studies in seven organisations. The analysis follows a clustering and cross-case approach combined with pattern matching. While elements vary in their use, the selected organisations practise the TPS substantially less than the model advocates, with the model being least practised in low volume job/batch manufacturing. Product-process differences and higher levels of the TPS model may clarify peculiar outcomes.

Pedunculopontine tegmental nucleus lesions impair stimulus--reward learning in autoshaping and conditioned reinforcement paradigms.

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Inglis, W L; Olmstead, M C; Robbins, T W

2000-04-01

The role of the pedunculopontine tegmental nucleus (PPTg) in stimulus-reward learning was assessed by testing the effects of PPTg lesions on performance in visual autoshaping and conditioned reinforcement (CRf) paradigms. Rats with PPTg lesions were unable to learn an association between a conditioned stimulus (CS) and a primary reward in either paradigm. In the autoshaping experiment, PPTg-lesioned rats approached the CS+ and CS- with equal frequency, and the latencies to respond to the two stimuli did not differ. PPTg lesions also disrupted discriminated approaches to an appetitive CS in the CRf paradigm and completely abolished the acquisition of responding with CRf. These data are discussed in the context of a possible cognitive function of the PPTg, particularly in terms of lesion-induced disruptions of attentional processes that are mediated by the thalamus.

Brain mechanisms of flavor learning

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Takashi eYamamoto

2011-09-01

Full Text Available Once the flavor of the ingested food (conditioned stimulus, CS is associated with a preferable (e.g., good taste or nutritive satisfaction or aversive (e.g., malaise with displeasure signal (unconditioned stimulus, US, animals react to its subsequent exposure by increasing or decreasing ingestion to the food. These two types of association learning (preference learning vs. aversion learning are known as classical conditioned reactions which are basic learning and memory phenomena, leading selection of food and proper food intake. Since the perception of flavor is generated by interaction of taste and odor during food intake, taste and/or odor are mainly associated with bodily signals in the flavor learning. After briefly reviewing flavor learning in general, brain mechanisms of conditioned taste aversion is described in more detail. The CS-US association leading to long-term potentiation in the amygdala, especially in its basolateral nucleus, is the basis of establishment of conditioned taste aversion. The novelty of the CS detected by the cortical gustatory area may be supportive in CS-US association. After the association, CS input is conveyed through the amygdala to different brain regions including the hippocampus for contextual fear formation, to the supramammilary and thalamic paraventricular nuclei for stressful anxiety or memory dependent fearful or stressful emotion, to the reward system to induce aversive expression to the CS, or hedonic shift from positive to negative, and to the CS-responsive neurons in the gustatory system to enhance the responsiveness to facilitate to detect the harmful stimulus.

Brain mechanisms of flavor learning.

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Yamamoto, Takashi; Ueji, Kayoko

2011-01-01

Once the flavor of the ingested food (conditioned stimulus, CS) is associated with a preferable (e.g., good taste or nutritive satisfaction) or aversive (e.g., malaise with displeasure) signal (unconditioned stimulus, US), animals react to its subsequent exposure by increasing or decreasing ingestion to the food. These two types of association learning (preference learning vs. aversion learning) are known as classical conditioned reactions which are basic learning and memory phenomena, leading selection of food and proper food intake. Since the perception of flavor is generated by interaction of taste and odor during food intake, taste and/or odor are mainly associated with bodily signals in the flavor learning. After briefly reviewing flavor learning in general, brain mechanisms of conditioned taste aversion is described in more detail. The CS-US association leading to long-term potentiation in the amygdala, especially in its basolateral nucleus, is the basis of establishment of conditioned taste aversion. The novelty of the CS detected by the cortical gustatory area may be supportive in CS-US association. After the association, CS input is conveyed through the amygdala to different brain regions including the hippocampus for contextual fear formation, to the supramammillary and thalamic paraventricular nuclei for stressful anxiety or memory dependent fearful or stressful emotion, to the reward system to induce aversive expression to the CS, or hedonic shift from positive to negative, and to the CS-responsive neurons in the gustatory system to enhance the responsiveness to facilitate to detect the harmful stimulus.

From Reactionary to Responsive: Applying the Internal Environmental Scan Protocol to Lifelong Learning Strategic Planning and Operational Model Selection

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Downing, David L.

2009-01-01

This study describes and implements a necessary preliminary strategic planning procedure, the Internal Environmental Scanning (IES), and discusses its relevance to strategic planning and university-sponsored lifelong learning program model selection. Employing a qualitative research methodology, a proposed lifelong learning-centric IES process…

Examining the Conditions of Using an On-Line Dictionary to Learn Words and Comprehend Texts

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Dilenschneider, Robert Francis

2018-01-01

This study investigated three look-up conditions for language learners to learn unknown target words and comprehend a reading passage when their attention is transferred away to an on-line dictionary. The research questions focused on how each look-up condition impacted the recall and recognition of word forms, word meanings, and passage…

Self-learning basic life support: A randomised controlled trial on learning conditions.

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Pedersen, Tina Heidi; Kasper, Nina; Roman, Hari; Egloff, Mike; Marx, David; Abegglen, Sandra; Greif, Robert

2018-05-01

To investigate whether pure self-learning without instructor support, resulted in the same BLS-competencies as facilitator-led learning, when using the same commercially available video BLS teaching kit. First-year medical students were randomised to either BLS self-learning without supervision or facilitator-led BLS-teaching. Both groups used the MiniAnne kit (Laerdal Medical, Stavanger, Norway) in the students' local language. Directly after the teaching and three months later, all participants were tested on their BLS-competencies in a simulated scenario, using the Resusci Anne SkillReporter™ (Laerdal Medical, Stavanger, Norway). The primary outcome was percentage of correct cardiac compressions three months after the teaching. Secondary outcomes were all other BLS parameters recorded by the SkillReporter and parameters from a BLS-competence rating form. 240 students were assessed at baseline and 152 students participated in the 3-month follow-up. For our primary outcome, the percentage of correct compressions, we found a median of 48% (interquartile range (IQR) 10-83) for facilitator-led learning vs. 42% (IQR 14-81) for self-learning (p = 0.770) directly after the teaching. In the 3-month follow-up, the rate of correct compressions dropped to 28% (IQR 6-59) for facilitator-led learning (p = 0.043) and did not change significantly in the self-learning group (47% (IQR 12-78), p = 0.729). Self-learning is not inferior to facilitator-led learning in the short term. Self-learning resulted in a better retention of BLS-skills three months after training compared to facilitator-led training. Copyright © 2018 Elsevier B.V. All rights reserved.

Fear of negative evaluation biases social evaluation inference: evidence from a probabilistic learning task.

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Button, Katherine S; Kounali, Daphne; Stapinski, Lexine; Rapee, Ronald M; Lewis, Glyn; Munafò, Marcus R

2015-01-01

Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences "I think [you are / George is]…". Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. As FNE increased participants selected fewer positive words (β = -0.4, 95% CI -0.7, -0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health.

Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning.

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Iwata, Kazunori

2016-05-11

Softmax selection is one of the most popular methods for action selection in reinforcement learning. Although various recently proposed methods may be more effective with full parameter tuning, implementing a complicated method that requires the tuning of many parameters can be difficult. Thus, softmax selection is still worth revisiting, considering the cost savings of its implementation and tuning. In fact, this method works adequately in practice with only one parameter appropriately set for the environment. The aim of this paper is to improve the variable setting of this method to extend the bandwidth of good parameters, thereby reducing the cost of implementation and parameter tuning. To achieve this, we take advantage of the asymptotic equipartition property in a Markov decision process to extend the peak bandwidth of softmax selection. Using a variety of episodic tasks, we show that our setting is effective in extending the bandwidth and that it yields a better policy in terms of stability. The bandwidth is quantitatively assessed in a series of statistical tests.

Individual differences in discriminatory fear learning under conditions of ambiguity: A vulnerability factor for anxiety disorders?

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Inna eArnaudova

2013-05-01

Full Text Available Complex fear learning procedures might be better suited than the common differential fear conditioning paradigm for detecting individual differences related to vulnerability for anxiety disorders. Two such procedures are the blocking procedure and the protection-from-overshadowing procedure. Their comparison allows for the examination of discriminatory fear learning under conditions of ambiguity. The present study examined the role of individual differences in such discriminatory fear learning. We hypothesized that heightened trait anxiety would be related to a deficit in discriminatory fear learning. Participants gave US-expectancy ratings as an index for the threat value of individual CSs following blocking and protection-from-overshadowing training. The difference in threat value at test between the protected-from-overshadowing CS and the blocked CS was negatively correlated with scores on a self-report tension-stress scale that approximates facets of generalized anxiety disorder (DASS-S, but not with other individual difference variables. In addition, a behavioral test showed that only participants scoring high on the DASS-S avoided the protected-from-overshadowing CS. This observed deficit in discriminatory fear learning for participants with high levels of tension-stress might be an underlying mechanism for fear overgeneralization in diffuse anxiety disorders such as generalized anxiety disorder.

Selection of the optimum condition for electron capture detector operation

International Nuclear Information System (INIS)

Lasa, J.; Korus, A.

1974-01-01

A method of determination of the optimal work conditions for the electron capture detector is presented in the paper. Physical phenomena which occur in the detector, as well as the energetic dependence of the electron attachment process are taken into consideration. The influence of the kind of carrier gas, temperature, and the parameters of the supplied voltage in both direct and pulse methods on average values of electron energy is described. Dependence of the sensitivity of the electron capture detector on the carrier gas and the polarizing voltage is illustrated for the Model DNW-300 electron capture detector produced in Poland. Practical indications for selecting optimal conditions of electron capture detector operation are given at the end of the paper. (author)

Training Self-Regulated Learning Skills with Video Modeling Examples: Do Task-Selection Skills Transfer?

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Raaijmakers, Steven F.; Baars, Martine; Schaap, Lydia; Paas, Fred; van Merriënboer, Jeroen; van Gog, Tamara

2018-01-01

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and…

Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions

NARCIS (Netherlands)

Alnajar, F.; Shan, C.; Gevers, T.; Geusebroek, J.M.

2012-01-01

In this paper we propose to adopt a learning-based encoding method for age estimation under unconstrained imaging conditions. A similar approach [Cao et al., 2010] is applied to face recognition in real-life face images. However, the feature vectors are encoded in hard manner i.e. each feature

System Quality Characteristics for Selecting Mobile Learning Applications

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Sarrab, Mohamed; Al-Shihi, Hafedh; Al-Manthari, Bader

2015-01-01

The majority of M-learning (Mobile learning) applications available today are developed for the formal learning and education environment. These applications are characterized by the improvement in the interaction between learners and instructors to provide high interaction and flexibility to the learning process. M-learning is gaining increased…

Conditions for excellence in teaching in medical education: The Frankfurt Model to ensure quality in teaching and learning

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Giesler, Marianne

2017-10-01

Full Text Available Background: There is general consensus that the organizational and administrative aspects of academic study programs exert an important influence on teaching and learning. Despite this, no comprehensive framework currently exists to describe the conditions that affect the quality of teaching and learning in medical education. The aim of this paper is to systematically and comprehensively identify these factors to offer academic administrators and decision makers interested in improving teaching a theory-based and, to an extent, empirically founded framework on the basis of which improvements in teaching quality can be identified and implemented.Method: Primarily, the issue was addressed by combining a theory-driven deductive approach with an experience based, “best evidence” one during the course of two workshops held by the GMA Committee on Personnel and Organizational Development in Academic Teaching (POiL in Munich (2013 and Frankfurt (2014. Two models describing the conditions relevant to teaching and learning (Euler/Hahn and Rindermann were critically appraised and synthesized into a new third model. Practical examples of teaching strategies that promote or hinder learning were compiled and added to the categories of this model and, to the extent possible, supported with empirical evidence.Based on this, a checklist with recommendations for optimizing general academic conditions was formulated.Results: The covers six categories: and These categories have been supplemented by the interests, motives and abilities of the actual teachers and students in this particular setting. The categories of this model provide the structure for a checklist in which recommendations for optimizing teaching are given.Conclusions: The checklist derived from the Frankfurt Model for ensuring quality in teaching and learning can be used for quality assurance and to improve the conditions under which teaching and learning take place in medical schools.

[Multilingualism and child psychiatry: on differential diagnoses of language disorder, specific learning disorder, and selective mutism].

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Tamiya, Satoshi

2014-01-01

Multilingualism poses unique psychiatric problems, especially in the field of child psychiatry. The author discusses several linguistic and transcultural issues in relation to Language Disorder, Specific Learning Disorder and Selective Mutism. Linguistic characteristics of multiple language development, including so-called profile effects and code-switching, need to be understood for differential diagnosis. It is also emphasized that Language Disorder in a bilingual person is not different or worse than that in a monolingual person. Second language proficiency, cultural background and transfer from the first language all need to be considered in an evaluation for Specific Learning Disorder. Selective Mutism has to be differentiated from the silent period observed in the normal successive bilingual development. The author concludes the review by remarking on some caveats around methods of language evaluation in a multilingual person.

From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

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Grossberg, Stephen

2015-09-24

This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

The attention habit: how reward learning shapes attentional selection.

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Anderson, Brian A

2016-04-01

There is growing consensus that reward plays an important role in the control of attention. Until recently, reward was thought to influence attention indirectly by modulating task-specific motivation and its effects on voluntary control over selection. Such an account was consistent with the goal-directed (endogenous) versus stimulus-driven (exogenous) framework that had long dominated the field of attention research. Now, a different perspective is emerging. Demonstrations that previously reward-associated stimuli can automatically capture attention even when physically inconspicuous and task-irrelevant challenge previously held assumptions about attentional control. The idea that attentional selection can be value driven, reflecting a distinct and previously unrecognized control mechanism, has gained traction. Since these early demonstrations, the influence of reward learning on attention has rapidly become an area of intense investigation, sparking many new insights. The result is an emerging picture of how the reward system of the brain automatically biases information processing. Here, I review the progress that has been made in this area, synthesizing a wealth of recent evidence to provide an integrated, up-to-date account of value-driven attention and some of its broader implications. © 2015 New York Academy of Sciences.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

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Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

2017-07-01

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Gene selection and classification for cancer microarray data based on machine learning and similarity measures

Directory of Open Access Journals (Sweden)

Liu Qingzhong

2011-12-01

Full Text Available Abstract Background Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. Results To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. Conclusions On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.

Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

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Wei Xue

2016-08-01

Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

NARCIS (Netherlands)

Drachsler, Hendrik; Hummel, Hans; Koper, Rob

2008-01-01

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.

Gene selection for microarray data classification via subspace learning and manifold regularization.

Science.gov (United States)

Tang, Chang; Cao, Lijuan; Zheng, Xiao; Wang, Minhui

2017-12-19

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

Resting heart rate variability predicts safety learning and fear extinction in an interoceptive fear conditioning paradigm.

Directory of Open Access Journals (Sweden)

Meike Pappens

Full Text Available This study aimed to investigate whether interindividual differences in autonomic inhibitory control predict safety learning and fear extinction in an interoceptive fear conditioning paradigm. Data from a previously reported study (N = 40 were extended (N = 17 and re-analyzed to test whether healthy participants' resting heart rate variability (HRV - a proxy of cardiac vagal tone - predicts learning performance. The conditioned stimulus (CS was a slight sensation of breathlessness induced by a flow resistor, the unconditioned stimulus (US was an aversive short-lasting suffocation experience induced by a complete occlusion of the breathing circuitry. During acquisition, the paired group received 6 paired CS-US presentations; the control group received 6 explicitly unpaired CS-US presentations. In the extinction phase, both groups were exposed to 6 CS-only presentations. Measures included startle blink EMG, skin conductance responses (SCR and US-expectancy ratings. Resting HRV significantly predicted the startle blink EMG learning curves both during acquisition and extinction. In the unpaired group, higher levels of HRV at rest predicted safety learning to the CS during acquisition. In the paired group, higher levels of HRV were associated with better extinction. Our findings suggest that the strength or integrity of prefrontal inhibitory mechanisms involved in safety- and extinction learning can be indexed by HRV at rest.

Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

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Yang Sun

2018-01-01

Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data.

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Shah, M; Marchand, M; Corbeil, J

2012-01-01

One of the objectives of designing feature selection learning algorithms is to obtain classifiers that depend on a small number of attributes and have verifiable future performance guarantees. There are few, if any, approaches that successfully address the two goals simultaneously. To the best of our knowledge, such algorithms that give theoretical bounds on the future performance have not been proposed so far in the context of the classification of gene expression data. In this work, we investigate the premise of learning a conjunction (or disjunction) of decision stumps in Occam's Razor, Sample Compression, and PAC-Bayes learning settings for identifying a small subset of attributes that can be used to perform reliable classification tasks. We apply the proposed approaches for gene identification from DNA microarray data and compare our results to those of the well-known successful approaches proposed for the task. We show that our algorithm not only finds hypotheses with a much smaller number of genes while giving competitive classification accuracy but also having tight risk guarantees on future performance, unlike other approaches. The proposed approaches are general and extensible in terms of both designing novel algorithms and application to other domains.

Learning to Learn.

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Weiss, Helen; Weiss, Martin

1988-01-01

The article reviews theories of learning (e.g., stimulus-response, trial and error, operant conditioning, cognitive), considers the role of motivation, and summarizes nine research-supported rules of effective learning. Suggestions are applied to teaching learning strategies to learning-disabled students. (DB)

Selective immunotoxic lesions of basal forebrain cholinergic cells: effects on learning and memory in rats.

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Baxter, Mark G; Bucci, David J; Gorman, Linda K; Wiley, Ronald G; Gallagher, Michela

2013-10-01

Male Long-Evans rats were given injections of either 192 IgG-saporin, an apparently selective toxin for basal forebrain cholinergic neurons (LES), or vehicle (CON) into either the medial septum and vertical limb of the diagonal band (MS/VDB) or bilaterally into the nucleus basalis magnocellularis and substantia innominata (nBM/SI). Place discrimination in the Morris water maze assessed spatial learning, and a trial-unique matching-to-place task in the water maze assessed memory for place information over varying delays. MS/VDB-LES and nBM/SI-LES rats were not impaired relative to CON rats in acquisition of the place discrimination, but were mildly impaired relative to CON rats in performance of the memory task even at the shortest delay, suggesting a nonmnemonic deficit. These results contrast with effects of less selective lesions, which have been taken to support a role for basal forebrain cholinergic neurons in learning and memory. 2013 APA, all rights reserved

The role of conditioning, learning and dopamine in sexual behavior: a narrative review of animal and human studies.

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Brom, Mirte; Both, Stephanie; Laan, Ellen; Everaerd, Walter; Spinhoven, Philip

2014-01-01

Many theories of human sexual behavior assume that sexual stimuli obtain arousing properties through associative learning processes. It is widely accepted that classical conditioning contributes to the etiology of both normal and maladaptive human behaviors. Despite the hypothesized importance of basic learning processes in sexual behavior, research on classical conditioning of the sexual response in humans is scarce. In the present paper, animal studies and studies in humans on the role of pavlovian conditioning on sexual responses are reviewed. Animal research shows robust, direct effects of conditioning processes on partner- and place preference. On the contrast, the empirical research with humans in this area is limited and earlier studies within this field are plagued by methodological confounds. Although recent experimental demonstrations of human sexual conditioning are neither numerous nor robust, sexual arousal showed to be conditionable in both men and women. The present paper serves to highlight the major empirical findings and to renew the insight in how stimuli can acquire sexually arousing value. Hereby also related neurobiological processes in reward learning are discussed. Finally, the connections between animal and human research on the conditionability of sexual responses are discussed, and suggestions for future directions in human research are given. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fear of Negative Evaluation Biases Social Evaluation Inference: Evidence from a Probabilistic Learning Task

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Button, Katherine S.; Kounali, Daphne; Stapinski, Lexine; Rapee, Ronald M.; Lewis, Glyn; Munafò, Marcus R.

2015-01-01

Background Fear of negative evaluation (FNE) defines social anxiety yet the process of inferring social evaluation, and its potential role in maintaining social anxiety, is poorly understood. We developed an instrumental learning task to model social evaluation learning, predicting that FNE would specifically bias learning about the self but not others. Methods During six test blocks (3 self-referential, 3 other-referential), participants (n = 100) met six personas and selected a word from a positive/negative pair to finish their social evaluation sentences “I think [you are / George is]…”. Feedback contingencies corresponded to 3 rules, liked, neutral and disliked, with P[positive word correct] = 0.8, 0.5 and 0.2, respectively. Results As FNE increased participants selected fewer positive words (β = −0.4, 95% CI −0.7, −0.2, p = 0.001), which was strongest in the self-referential condition (FNE × condition 0.28, 95% CI 0.01, 0.54, p = 0.04), and the neutral and dislike rules (FNE × condition × rule, p = 0.07). At low FNE the proportion of positive words selected for self-neutral and self-disliked greatly exceeded the feedback contingency, indicating poor learning, which improved as FNE increased. Conclusions FNE is associated with differences in processing social-evaluative information specifically about the self. At low FNE this manifests as insensitivity to learning negative self-referential evaluation. High FNE individuals are equally sensitive to learning positive or negative evaluation, which although objectively more accurate, may have detrimental effects on mental health. PMID:25853835

Evolution of individual versus social learning on social networks.

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Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Multiagent -Learning for Aloha-Like Spectrum Access in Cognitive Radio Systems

Directory of Open Access Journals (Sweden)

Li Husheng

2010-01-01

Full Text Available An Aloha-like spectrum access scheme without negotiation is considered for multiuser and multichannel cognitive radio systems. To avoid collisions incurred by the lack of coordination, each secondary user learns how to select channels according to its experience. Multiagent reinforcement leaning (MARL is applied for the secondary users to learn good strategies of channel selection. Specifically, the framework of -learning is extended from single user case to multiagent case by considering other secondary users as a part of the environment. The dynamics of the -learning are illustrated using a Metrick-Polak plot, which shows the traces of -values in the two-user case. For both complete and partial observation cases, rigorous proofs of the convergence of multiagent -learning without communications, under certain conditions, are provided using the Robins-Monro algorithm and contraction mapping, respectively. The learning performance (speed and gain in utility is evaluated by numerical simulations.

Dress Nicer = Know More? Young Children's Knowledge Attribution and Selective Learning Based on How Others Dress.

Directory of Open Access Journals (Sweden)

Kyla P McDonald

Full Text Available This research explored whether children judge the knowledge state of others and selectively learn novel information from them based on how they dress. The results indicated that 4- and 6-year-olds identified a formally dressed individual as more knowledgeable about new things in general than a casually dressed one (Study 1. Moreover, children displayed an overall preference to seek help from a formally dressed individual rather than a casually dressed one when learning about novel objects and animals (Study 2. These findings are discussed in relation to the halo effect, and may have important implications for child educators regarding how instructor dress might influence young students' knowledge attribution and learning preferences.

Altering spatial priority maps via statistical learning of target selection and distractor filtering.

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Ferrante, Oscar; Patacca, Alessia; Di Caro, Valeria; Della Libera, Chiara; Santandrea, Elisa; Chelazzi, Leonardo

2018-05-01

The cognitive system has the capacity to learn and make use of environmental regularities - known as statistical learning (SL), including for the implicit guidance of attention. For instance, it is known that attentional selection is biased according to the spatial probability of targets; similarly, changes in distractor filtering can be triggered by the unequal spatial distribution of distractors. Open questions remain regarding the cognitive/neuronal mechanisms underlying SL of target selection and distractor filtering. Crucially, it is unclear whether the two processes rely on shared neuronal machinery, with unavoidable cross-talk, or they are fully independent, an issue that we directly addressed here. In a series of visual search experiments, participants had to discriminate a target stimulus, while ignoring a task-irrelevant salient distractor (when present). We systematically manipulated spatial probabilities of either one or the other stimulus, or both. We then measured performance to evaluate the direct effects of the applied contingent probability distribution (e.g., effects on target selection of the spatial imbalance in target occurrence across locations) as well as its indirect or "transfer" effects (e.g., effects of the same spatial imbalance on distractor filtering across locations). By this approach, we confirmed that SL of both target and distractor location implicitly bias attention. Most importantly, we described substantial indirect effects, with the unequal spatial probability of the target affecting filtering efficiency and, vice versa, the unequal spatial probability of the distractor affecting target selection efficiency across locations. The observed cross-talk demonstrates that SL of target selection and distractor filtering are instantiated via (at least partly) shared neuronal machinery, as further corroborated by strong correlations between direct and indirect effects at the level of individual participants. Our findings are compatible

The Impact of Preparation: Conditions for Developing Professional Knowledge through Simulations

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Sjöberg, David; Karp, Staffan; Söderström, Tor

2015-01-01

This article examines simulations of critical incidents in police education by investigating how activities in the preparation phase influence participants' actions and thus the conditions for learning professional knowledge. The study is based on interviews in two stages (traditional and stimulated recall interviews) with six selected students…

Geotechnical conditions of Bulgaria and site selection for radioactive waste repository

International Nuclear Information System (INIS)

Iliev, I.; Tacheva, E.

1993-01-01

A comparative study of the complex structure of the Bulgarian lands and the engineering geological criteria for site selection of national repositories for high level radwastes is made. A detailed description of the following geotechnical conditions of Bulgaria's territory is given: genetic, lithological and engineering-geological types of rocks; physico-mechanical parameters of the most widespread rocky and semi-rocky engineering geological types; fissuring of the rocks; rock massifs; geodynamic processes. The number of promising variants for repositories have been classified according to the structure of the rock massif and the engineering-geological properties of the layers which are promising for the purpose. The following sites are investigated: 1) sites in one-type homogeneous rock massifs of high strength and elasticity; 2) sites of various type massifs with a promising layer of rocks with medium strength and elasticity; 3) sites in various type massifs with a promising layer of plastic rocks of low strength. It is concluded that the complexity of the geotechnical and other conditions in the territory of Bulgaria would predetermine the deficiency of the list of the properties required for the selected sites. The building up of engineering defence will be needed to offset that deficiency and their problems will be resolved after the specific site have been chosen. Geotechnical elements should be likewise envisaged within the general pattern of the monitoring needed. The designing, installing and putting into operation of the monitoring systems should be accomplished as early as the stage of the detailed investigation of the site selected. 19 refs., 2 suppls. (author)

Self-regulated learning of important information under sequential and simultaneous encoding conditions.

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Middlebrooks, Catherine D; Castel, Alan D

2018-05-01

Learners make a number of decisions when attempting to study efficiently: they must choose which information to study, for how long to study it, and whether to restudy it later. The current experiments examine whether documented impairments to self-regulated learning when studying information sequentially, as opposed to simultaneously, extend to the learning of and memory for valuable information. In Experiment 1, participants studied lists of words ranging in value from 1-10 points sequentially or simultaneously at a preset presentation rate; in Experiment 2, study was self-paced and participants could choose to restudy. Although participants prioritized high-value over low-value information, irrespective of presentation, those who studied the items simultaneously demonstrated superior value-based prioritization with respect to recall, study selections, and self-pacing. The results of the present experiments support the theory that devising, maintaining, and executing efficient study agendas is inherently different under sequential formatting than simultaneous. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Can a student learn optimally from two different teachers?

International Nuclear Information System (INIS)

Neirotti, J P

2010-01-01

We explore the effects of over-specificity in learning algorithms by investigating the behavior of a student, suited to learn optimally from a teacher B, learning from a teacher B' ≠ B. We only considered the supervised, on-line learning scenario with teachers selected from a particular family. We found that, in the general case, the application of the optimal algorithm to the wrong teacher produces a residual generalization error, even if the right teacher is harder. By imposing mild conditions to the learning algorithm form, we obtained an approximation for the residual generalization error. Simulations carried out in finite networks validate the estimate found.

Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory

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Koenig, Stephan; Uengoer, Metin; Lachnit, Harald

2017-01-01

We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy, respectively. The cues differed with respect to the strength of their shock association (L H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues. After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically. Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention. PMID:28588466

Predicting memory performance under conditions of proactive interference: immediate and delayed judgments of learning.

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Wahlheim, Christopher N

2011-07-01

Four experiments examined the monitoring accuracy of immediate and delayed judgments of learning (JOLs) under conditions of proactive interference (PI). PI was produced using paired-associate learning tasks that conformed to variations of classic A-B, A-D paradigms. Results revealed that the relative monitoring accuracy of interference items was better for delayed than for immediate JOLs. However, delayed JOLs were overconfident for interference items, but not for items devoid of interference. Intrusions retrieved prior to delayed JOLs produced inflated predictions of performance. These results show that delayed JOLs enhance monitoring accuracy in PI situations, except when intrusions are mistaken for target responses.

Effect of encapsulation of selected probiotic cell on survival in simulated gastrointestinal tract condition

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Hasiah Ayama

2014-06-01

Full Text Available The health benefits of probiotic bacteria have been led to their increasing use in foods. Encapsulation has been investigated to improve their survival. In this study, the selection, encapsulation and viability of lactic acid bacteria (LAB with probiotic properties in simulated gastrointestinal tract (GIT condition were investigated. One hundred and fifty isolates of LAB were obtained from 30 samples of raw cow and goat milk and some fermented foods. Nine isolates could survive under GIT condition and only 3 isolates exhibited an antimicrobial activity against all food-borne pathogenic bacteria. Among them, 2 isolates (CM21 and CM53 exhibited bile salt hydrolase activity on glycocholate and glycodeoxycholate agar plates and were identified as Lactobacillus plantarum. CM53 was selected for encapsulation using 1-3% alginate and 2% Hi-maize resistant starch by emulsion system. Viability and releasing ability of encapsulated CM53 in simulated GIT condition was increased in accordance to the alginate concentration and incubation time, respectively.

A decision-making support system to select forages according to environmental conditions in Colombia

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Blanca Aurora Arce Barboza

2013-07-01

Full Text Available Low food supply is a major problem affecting a large percentage of the livestock population in Colombia and is largely associated to inappropriate choice of forage species; and thus not well adapted to the environmental conditions of a specific region. To mitigate this problem, without incurring increasing costs associated to changing environmental conditions, it is possible to match the adaptive capacity of species to the environment in which they grow. A decision support system was developed to select suitable forage species for a given environment. The system is based on the use of existing information about requirements of the species rather than specific experimentation. From the information gathered, a database was generated and implemented on ASP.NET in C # and SQL Server database. This system allows users to search and select pastures and forage species for specific soil and climatic conditions of a particular farm or region, through a user-friendly web platform.

Duration of the Unconditioned Stimulus in Appetitive Conditioning of Honeybees Differentially Impacts Learning, Long-Term Memory Strength, and the Underlying Protein Synthesis

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Marter, Kathrin; Grauel, M. Katharina; Lewa, Carmen; Morgenstern, Laura; Buckemüller, Christina; Heufelder, Karin; Ganz, Marion; Eisenhardt, Dorothea

2014-01-01

This study examines the role of stimulus duration in learning and memory formation of honeybees ("Apis mellifera"). In classical appetitive conditioning honeybees learn the association between an initially neutral, conditioned stimulus (CS) and the occurrence of a meaningful stimulus, the unconditioned stimulus (US). Thereby the CS…

When does social learning become cultural learning?

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Heyes, Cecilia

2017-03-01

Developmental research on selective social learning, or 'social learning strategies', is currently a rich source of information about when children copy behaviour, and who they prefer to copy. It also has the potential to tell us when and how human social learning becomes cultural learning; i.e. mediated by psychological mechanisms that are specialized, genetically or culturally, to promote cultural inheritance. However, this review article argues that, to realize its potential, research on the development of selective social learning needs more clearly to distinguish functional from mechanistic explanation; to achieve integration with research on attention and learning in adult humans and 'dumb' animals; and to recognize that psychological mechanisms can be specialized, not only by genetic evolution, but also by associative learning and cultural evolution. © 2015 John Wiley & Sons Ltd.

Learning to selectively attend from context-specific attentional histories: A demonstration and some constraints.

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Crump, Matthew J C

2016-03-01

Multiple lines of evidence from the attention and performance literature show that attention filtering can be controlled by higher level voluntary processes and lower-level cue-driven processes (for recent reviews see Bugg, 2012; Bugg & Crump, 2012; Egner, 2008). The experiments were designed to test a general hypothesis that cue-driven control learns from context-specific histories of prior acts of selective attention. Several web-based flanker studies were conducted via Amazon Mechanical Turk. Attention filtering demands were induced by a secondary one-back memory task after each trial prompting recall of the last target or distractor letter. Blocking recall demands produced larger flanker effects for the distractor than target recall conditions. Mixing recall demands and associating them with particular stimulus-cues (location, colour, letter, and font) sometimes showed rapid, contextual control of flanker interference, and sometimes did not. The results show that subtle methodological parameters can influence whether or not contextual control is observed. More generally, the results show that contextual control phenomena can be influenced by other sources of control, including other cue-driven sources competing for control. (c) 2016 APA, all rights reserved).

How Select Groups of Preservice Science Teachers with Inquiry Orientations View Teaching and Learning Science through Inquiry

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Ward, Peggy

Although hailed as a powerful form of instruction, in most teaching and learning contexts, inquiry-based instruction is fraught with ambiguous and conflicting definitions and descriptions. Yet little has been written about the experiences preservice science teacher have regarding their learning to teach science through inquiry. This project sought to understand how select preservice secondary science teachers enrolled in three UTeach programs in Arkansas conceptualize inquiry instruction and how they rationalize its value in a teaching and learning context. The three teacher education programs investigated in this study are adoption sites aligned with the UTeach Program in Austin, TX that distinguishes itself in part by its inquiry emphasis. Using a mixed method investigation design, this study utilized two sources of data to explore the preservice science teachers' thinking. In the first phase, a modified version of the Pedagogy of Science teaching Tests (POSTT) was used to identify select program participants who indicated preferences for inquiry instruction over other instructional strategies. Secondly, the study used an open-ended questionnaire to explore the selected subjects' beliefs and conceptions of teaching and learning science in an inquiry context. The study also focused on identifying particular junctures in the prospective science teachers' education preparation that might impact their understanding about inquiry. Using a constant comparative approach, this study explored 19 preservice science teachers' conceptions about inquiry. The results indicate that across all levels of instruction, the prospective teachers tended to have strong student-centered teaching orientations. Except subjects in for the earliest courses, subjects' definitions and descriptions of inquiry tended toward a few of the science practices. More advanced subjects, however, expressed more in-depth descriptions. Excluding the subjects who have completed the program, multiple

A fully automated Drosophila olfactory classical conditioning and testing system for behavioral learning and memory assessment.

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Jiang, Hui; Hanna, Eriny; Gatto, Cheryl L; Page, Terry L; Bhuva, Bharat; Broadie, Kendal

2016-03-01

Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24h) are comparable to traditional manual experiments, while minimizing experimenter involvement. The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ∼$500US, making it affordable to a wide range of investigators. This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays. Copyright © 2015 Elsevier B.V. All rights reserved.

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

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Imbalzano, Giulio; Anelli, Andrea; Giofré, Daniele; Klees, Sinja; Behler, Jörg; Ceriotti, Michele

2018-06-01

Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs. The accuracy, speed, and reliability of machine learning potentials, however, depend strongly on the way atomic configurations are represented, i.e., the choice of descriptors used as input for the machine learning method. The raw Cartesian coordinates are typically transformed in "fingerprints," or "symmetry functions," that are designed to encode, in addition to the structure, important properties of the potential energy surface like its invariances with respect to rotation, translation, and permutation of like atoms. Here we discuss automatic protocols to select a number of fingerprints out of a large pool of candidates, based on the correlations that are intrinsic to the training data. This procedure can greatly simplify the construction of neural network potentials that strike the best balance between accuracy and computational efficiency and has the potential to accelerate by orders of magnitude the evaluation of Gaussian approximation potentials based on the smooth overlap of atomic positions kernel. We present applications to the construction of neural network potentials for water and for an Al-Mg-Si alloy and to the prediction of the formation energies of small organic molecules using Gaussian process regression.

Active learning: a step towards automating medical concept extraction.

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Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

2016-03-01

This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Enriching behavioral ecology with reinforcement learning methods.

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Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

2018-02-13

This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Butterfly Learning and the Diversification of Plant Leaf Shape

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Denise Dalbosco Dell'aglio

2016-07-01

Full Text Available Visual cues are important for insects to find flowers and host plants. It has been proposed that the diversity of leaf shape in Passiflora vines could be a result of negative frequency dependent selection driven by visual searching behavior among their butterfly herbivores. Here we tested the hypothesis that Heliconius butterflies use leaf shape as a cue to initiate approach towards a host plant. We first tested for the ability to recognize shapes using a food reward conditioning experiment. Butterflies showed an innate preference for flowers with three and five petals. However, they could be trained to increase the frequency of visits to a non-preferred flower with two petals, indicating an ability to learn to associate shape with a reward. Next we investigated shape learning specifically in the context of oviposition by conditioning females to lay eggs on two shoots associated with different artificial leaf shapes: their own host plant, Passiflora biflora, and a lanceolate non-biflora leaf shape. The conditioning treatment had a significant effect on the approach of butterflies to the two leaf shapes, consistent with a role for shape learning in oviposition behavior. This study is the first to show that Heliconius butterflies use shape as a cue for feeding and oviposition, and can learn shape preference for both flowers and leaves. This demonstrates the potential for Heliconius to drive negative frequency dependent selection on the leaf shape of their Passiflora host plants.

Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

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Hodson, Derek

2014-01-01

This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

Blockade of Dopamine Activity in the Nucleus Accumbens Impairs Learning Extinction of Conditioned Fear

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Holtzman-Assif, Orit; Laurent, Vincent; Westbrook, R. Frederick

2010-01-01

Three experiments used rats to investigate the role of dopamine activity in learning to inhibit conditioned fear responses (freezing) in extinction. In Experiment 1, rats systemically injected with the D2 dopamine antagonist, haloperidol, froze more across multiple extinction sessions and on a drug-free retention test than control rats. In…

Impaired Value Learning for Faces in Preschoolers With Autism Spectrum Disorder.

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Wang, Quan; DiNicola, Lauren; Heymann, Perrine; Hampson, Michelle; Chawarska, Katarzyna

2018-01-01

One of the common findings in autism spectrum disorder (ASD) is limited selective attention toward social objects, such as faces. Evidence from both human and nonhuman primate studies suggests that selection of objects for processing is guided by the appraisal of object values. We hypothesized that impairments in selective attention in ASD may reflect a disruption of a system supporting learning about object values in the social domain. We examined value learning in social (faces) and nonsocial (fractals) domains in preschoolers with ASD (n = 25) and typically developing (TD) controls (n = 28), using a novel value learning task implemented on a gaze-contingent eye-tracking platform consisting of value learning and a selective attention choice test. Children with ASD performed more poorly than TD controls on the social value learning task, but both groups performed similarly on the nonsocial task. Within-group comparisons indicated that value learning in TD children was enhanced on the social compared to the nonsocial task, but no such enhancement was seen in children with ASD. Performance in the social and nonsocial conditions was correlated in the ASD but not in the TD group. The study provides support for a domain-specific impairment in value learning for faces in ASD, and suggests that, in ASD, value learning in social and nonsocial domains may rely on a shared mechanism. These findings have implications both for models of selective social attention deficits in autism and for identification of novel treatment targets. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Locus-dependent selection in crop-wild hybrids of lettuce under field conditions and its implication for GM crop development

Science.gov (United States)

Hooftman, Danny A P; Flavell, Andrew J; Jansen, Hans; den Nijs, Hans C M; Syed, Naeem H; Sørensen, Anker P; Orozco-ter Wengel, Pablo; van de Wiel, Clemens C M

2011-01-01

Gene escape from crops has gained much attention in the last two decades, as transgenes introgressing into wild populations could affect the latter's ecological characteristics. However, different genes have different likelihoods of introgression. The mixture of selective forces provided by natural conditions creates an adaptive mosaic of alleles from both parental species. We investigated segregation patterns after hybridization between lettuce (Lactuca sativa) and its wild relative, L. serriola. Three generations of hybrids (S1, BC1, and BC1S1) were grown in habitats mimicking the wild parent's habitat. As control, we harvested S1 seedlings grown under controlled conditions, providing very limited possibility for selection. We used 89 AFLP loci, as well as more recently developed dominant markers, 115 retrotransposon markers (SSAP), and 28 NBS loci linked to resistance genes. For many loci, allele frequencies were biased in plants exposed to natural field conditions, including over-representation of crop alleles for various loci. Furthermore, Linkage disequilibrium was locally changed, allegedly by selection caused by the natural field conditions, providing ample opportunity for genetic hitchhiking. Our study indicates that when developing genetically modified crops, a judicious selection of insertion sites, based on knowledge of selective (dis)advantages of the surrounding crop genome under field conditions, could diminish transgene persistence. PMID:25568012

Locus-dependent selection in crop-wild hybrids of lettuce under field conditions and its implication for GM crop development.

Science.gov (United States)

Hooftman, Danny A P; Flavell, Andrew J; Jansen, Hans; den Nijs, Hans C M; Syed, Naeem H; Sørensen, Anker P; Orozco-Ter Wengel, Pablo; van de Wiel, Clemens C M

2011-09-01

Gene escape from crops has gained much attention in the last two decades, as transgenes introgressing into wild populations could affect the latter's ecological characteristics. However, different genes have different likelihoods of introgression. The mixture of selective forces provided by natural conditions creates an adaptive mosaic of alleles from both parental species. We investigated segregation patterns after hybridization between lettuce (Lactuca sativa) and its wild relative, L. serriola. Three generations of hybrids (S1, BC1, and BC1S1) were grown in habitats mimicking the wild parent's habitat. As control, we harvested S1 seedlings grown under controlled conditions, providing very limited possibility for selection. We used 89 AFLP loci, as well as more recently developed dominant markers, 115 retrotransposon markers (SSAP), and 28 NBS loci linked to resistance genes. For many loci, allele frequencies were biased in plants exposed to natural field conditions, including over-representation of crop alleles for various loci. Furthermore, Linkage disequilibrium was locally changed, allegedly by selection caused by the natural field conditions, providing ample opportunity for genetic hitchhiking. Our study indicates that when developing genetically modified crops, a judicious selection of insertion sites, based on knowledge of selective (dis)advantages of the surrounding crop genome under field conditions, could diminish transgene persistence.

Speech perception in older listeners with normal hearing:conditions of time alteration, selective word stress, and length of sentences.

Science.gov (United States)

Cho, Soojin; Yu, Jyaehyoung; Chun, Hyungi; Seo, Hyekyung; Han, Woojae

2014-04-01

Deficits of the aging auditory system negatively affect older listeners in terms of speech communication, resulting in limitations to their social lives. To improve their perceptual skills, the goal of this study was to investigate the effects of time alteration, selective word stress, and varying sentence lengths on the speech perception of older listeners. Seventeen older people with normal hearing were tested for seven conditions of different time-altered sentences (i.e., ±60%, ±40%, ±20%, 0%), two conditions of selective word stress (i.e., no-stress and stress), and three different lengths of sentences (i.e., short, medium, and long) at the most comfortable level for individuals in quiet circumstances. As time compression increased, sentence perception scores decreased statistically. Compared to a natural (or no stress) condition, the selectively stressed words significantly improved the perceptual scores of these older listeners. Long sentences yielded the worst scores under all time-altered conditions. Interestingly, there was a noticeable positive effect for the selective word stress at the 20% time compression. This pattern of results suggests that a combination of time compression and selective word stress is more effective for understanding speech in older listeners than using the time-expanded condition only.

Methodology for selection of attributes and operating conditions for SVM-Based fault locator's

Directory of Open Access Journals (Sweden)

Debbie Johan Arredondo Arteaga

2017-01-01

Full Text Available Context: Energy distribution companies must employ strategies to meet their timely and high quality service, and fault-locating techniques represent and agile alternative for restoring the electric service in the power distribution due to the size of distribution services (generally large and the usual interruptions in the service. However, these techniques are not robust enough and present some limitations in both computational cost and the mathematical description of the models they use. Method: This paper performs an analysis based on a Support Vector Machine for the evaluation of the proper conditions to adjust and validate a fault locator for distribution systems; so that it is possible to determine the minimum number of operating conditions that allow to achieve a good performance with a low computational effort. Results: We tested the proposed methodology in a prototypical distribution circuit, located in a rural area of Colombia. This circuit has a voltage of 34.5 KV and is subdivided in 20 zones. Additionally, the characteristics of the circuit allowed us to obtain a database of 630.000 records of single-phase faults and different operating conditions. As a result, we could determine that the locator showed a performance above 98% with 200 suitable selected operating conditions. Conclusions: It is possible to improve the performance of fault locators based on Support Vector Machine. Specifically, these improvements are achieved by properly selecting optimal operating conditions and attributes, since they directly affect the performance in terms of efficiency and the computational cost.

Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory

Directory of Open Access Journals (Sweden)

Stephan Koenig

2017-05-01

Full Text Available We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L, partial (P, and high (H shock expectancy, respectively. The cues differed with respect to the strength of their shock association (L < P < H and the uncertainty of their prediction (L < P > H. During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues. After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically. Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention.

Food approach conditioning and discrimination learning using sound cues in benthic sharks.

Science.gov (United States)

Vila Pouca, Catarina; Brown, Culum

2018-07-01

The marine environment is filled with biotic and abiotic sounds. Some of these sounds predict important events that influence fitness while others are unimportant. Individuals can learn specific sound cues and 'soundscapes' and use them for vital activities such as foraging, predator avoidance, communication and orientation. Most research with sounds in elasmobranchs has focused on hearing thresholds and attractiveness to sound sources, but very little is known about their abilities to learn about sounds, especially in benthic species. Here we investigated if juvenile Port Jackson sharks could learn to associate a musical stimulus with a food reward, discriminate between two distinct musical stimuli, and whether individual personality traits were linked to cognitive performance. Five out of eight sharks were successfully conditioned to associate a jazz song with a food reward delivered in a specific corner of the tank. We observed repeatable individual differences in activity and boldness in all eight sharks, but these personality traits were not linked to the learning performance assays we examined. These sharks were later trained in a discrimination task, where they had to distinguish between the same jazz and a novel classical music song, and swim to opposite corners of the tank according to the stimulus played. The sharks' performance to the jazz stimulus declined to chance levels in the discrimination task. Interestingly, some sharks developed a strong side bias to the right, which in some cases was not the correct side for the jazz stimulus.

Dress Nicer = Know More? Young Children’s Knowledge Attribution and Selective Learning Based on How Others Dress

Science.gov (United States)

McDonald, Kyla P.; Ma, Lili

2015-01-01

This research explored whether children judge the knowledge state of others and selectively learn novel information from them based on how they dress. The results indicated that 4- and 6-year-olds identified a formally dressed individual as more knowledgeable about new things in general than a casually dressed one (Study 1). Moreover, children displayed an overall preference to seek help from a formally dressed individual rather than a casually dressed one when learning about novel objects and animals (Study 2). These findings are discussed in relation to the halo effect, and may have important implications for child educators regarding how instructor dress might influence young students’ knowledge attribution and learning preferences. PMID:26636980

Optimizing learning path selection through memetic algorithms

NARCIS (Netherlands)

Acampora, G.; Gaeta, M.; Loia, V.; Ritrovato, P.; Salerno, S.

2008-01-01

e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering

Selection of Learning Media Mathematics for Junior School Students

Science.gov (United States)

Widodo, Sri Adi; Wahyudin

2018-01-01

One of the factors that determine the success of mathematics learning is the learning media used. Learning media can help students to create mathematical abstract mathematics that is abstract. In addition to media, meaningful learning is a learning that is adapted to the students' cognitive development. According to Piaget, junior high school…

Words can slow down category learning.

Science.gov (United States)

Brojde, Chandra L; Porter, Chelsea; Colunga, Eliana

2011-08-01

Words have been shown to influence many cognitive tasks, including category learning. Most demonstrations of these effects have focused on instances in which words facilitate performance. One possibility is that words augment representations, predicting an across the-board benefit of words during category learning. We propose that words shift attention to dimensions that have been historically predictive in similar contexts. Under this account, there should be cases in which words are detrimental to performance. The results from two experiments show that words impair learning of object categories under some conditions. Experiment 1 shows that words hurt performance when learning to categorize by texture. Experiment 2 shows that words also hurt when learning to categorize by brightness, leading to selectively attending to shape when both shape and hue could be used to correctly categorize stimuli. We suggest that both the positive and negative effects of words have developmental origins in the history of word usage while learning categories. [corrected

Selectively Distracted: Divided Attention and Memory for Important Information.

Science.gov (United States)

Middlebrooks, Catherine D; Kerr, Tyson; Castel, Alan D

2017-08-01

Distractions and multitasking are generally detrimental to learning and memory. Nevertheless, people often study while listening to music, sitting in noisy coffee shops, or intermittently checking their e-mail. The current experiments examined how distractions and divided attention influence one's ability to selectively remember valuable information. Participants studied lists of words that ranged in value from 1 to 10 points while completing a digit-detection task, while listening to music, or without distractions. Though participants recalled fewer words following digit detection than in the other conditions, there were no significant differences between conditions in terms of selectively remembering the most valuable words. Similar results were obtained across a variety of divided-attention tasks that stressed attention and working memory to different degrees, which suggests that people may compensate for divided-attention costs by selectively attending to the most valuable items and that factors that worsen memory do not necessarily impair the ability to selectively remember important information.

Selection of tomato mutants (lycopersicon esculentum mill) under conditions of drought stress

International Nuclear Information System (INIS)

Gonzalez, Maria Caridad; Mansoor, Ali; Suarez, Lorenzo; Mukandama, Jean P.; Rodriguez, Yanet

2001-01-01

At the National Institute of Agricultural Sciences were evaluated under conditions of drought estres an M5 population obtained starting from the irradiation of seeds of the Amalia and INCA 9-1varieties with dose of 300 and 500 Gy of rays gamma of 60 Co. The number of clusters for plant, mass average of the fruits, number of fruits for plant and yield for plant, the content of total soluble solids and the acidity of the fruits was evaluated observing differ highly significant among the different ones lines and the respective donating studied. Promissory genotipos of high productive potential was selected under this condition

Design Criteria, Operating Conditions, and Nickel-Iron Hydroxide Catalyst Materials for Selective Seawater Electrolysis.

Science.gov (United States)

Dionigi, Fabio; Reier, Tobias; Pawolek, Zarina; Gliech, Manuel; Strasser, Peter

2016-05-10

Seawater is an abundant water resource on our planet and its direct electrolysis has the advantage that it would not compete with activities demanding fresh water. Oxygen selectivity is challenging when performing seawater electrolysis owing to competing chloride oxidation reactions. In this work we propose a design criterion based on thermodynamic and kinetic considerations that identifies alkaline conditions as preferable to obtain high selectivity for the oxygen evolution reaction. The criterion states that catalysts sustaining the desired operating current with an overpotential seawater-mimicking electrolyte. The catalyst was synthesized by a solvothermal method and the activity, surface redox chemistry, and stability were tested electrochemically in alkaline and near-neutral conditions (borate buffer at pH 9.2) and under both fresh seawater conditions. The Tafel slope at low current densities is not influenced by pH or presence of chloride. On the other hand, the addition of chloride ions has an influence in the temporal evolution of the nickel reduction peak and on both the activity and stability at high current densities at pH 9.2. Faradaic efficiency close to 100 % under the operating conditions predicted by our design criteria was proven using in situ electrochemical mass spectrometry. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learning to Promote Health at an Emergency Care Department: Identifying Expansive and Restrictive Conditions

Science.gov (United States)

Gustavsson, Maria; Ekberg, Kerstin

2015-01-01

This article reports on the findings of a planned workplace health promotion intervention, and the aim is to identify conditions that facilitated or restricted the learning to promote health at an emergency care department in a Swedish hospital. The study had a longitudinal design, with interviews before and after the intervention and follow-up…

Research on ration selection of mixed absorbent solution for membrane air-conditioning system

International Nuclear Information System (INIS)

Li, Xiu-Wei; Zhang, Xiao-Song; Wang, Fang; Zhao, Xiao; Zhang, Zhuo

2015-01-01

Highlights: • We derive models of the membrane air-conditioning system with mixed absorbents. • We make analysis on system COP, cost-effectiveness and economy. • The paper provides a new method for ideal absorbent selection. • The solutes concentration of 50% achieves the best cost-effectiveness and the economy. - Abstract: Absorption air-conditioning system is a good alternative to vapor compression system for developing low carbon society. To improve the performance of the traditional absorption system, the membrane air-conditioning system is configured and its COP can reach as high as 6. Mixed absorbents are potential for cost reduction of the membrane system while maintaining a high COP. On the purpose of finding ideal mixed absorbent groups, this paper makes analysis on COP, cost-effectiveness and economy of the membrane system with mixed LiBr–CaCl 2 absorbent solution. The models of the system have been developed for the analysis. The results show the COP is higher for the absorbent groups with lower concentration of the total solute and higher concentration ratio of LiBr. It also reveals when the total solutes concentration is about 50%, it achieves the best cost-effectiveness and the economy. The process of the analysis provides a useful method for mixed absorbents selection

The Research of Self-Management Team and Superior-Direction Team in Team Learning Influential Factors

OpenAIRE

Zhang Wei

2013-01-01

Team learning is a cure for bureaucracy; it facilitates team innovation and team performance. But team learning occurs only when necessary conditions were met. This research focused on differences of team learning influential factors between self-management team and superior-direction team. Four variables were chosen as predictors of team learning though literature review and pilot interview. The 4 variables are team motivation, team trust, team conflict and team leadership. Selected 54 self ...

Neuropsychology of learning and memory in teleost fish.

Science.gov (United States)

Salas, Cosme; Broglio, Cristina; Durán, Emilio; Gómez, Antonia; Ocaña, Francisco M; Jiménez-Moya, Fernando; Rodríguez, Fernando

2006-01-01

Traditionally, brain and behavior evolution was viewed as an anagenetic process that occurred in successive stages of increasing complexity and advancement. Fishes, considered the most primitive vertebrates, were supposed to have a scarcely differentiated telencephalon, and limited learning capabilities. However, recent developmental, neuroanatomical, and functional data indicate that the evolution of brain and behavior may have been more conservative than previously thought. Experimental data suggest that the properties and neural basis of learning and memory are notably similar among teleost fish and land vertebrates. For example, lesion studies show that the teleost cerebellum is essential in classical conditioning of discrete motor responses. The lateral telencephalic pallium of the teleost fish, proposed as homologous to the hippocampus, is selectively involved in spatial learning and memory, and in trace classical conditioning. In contrast, the medial pallium, considered homologous to the amygdala, is involved in emotional conditioning in teleost fish. The data reviewed here show a remarkable parallelism between mammals and teleost fish concerning the role of different brain centers in learning and memory and cognitive processes. These evidences suggest that these separate memory systems could have appeared early during the evolution of vertebrates, having been conserved through phylogenesis.

Properties, promotive and obstructive conditions of multi-professional teaching and learning of health professions and non-health professions: an explorative survey from the perspective of teachers.

Science.gov (United States)

Schmitz, Daniela; Höhmann, Ulrike

2016-01-01

Care for people with dementia is considered a multi-professional challenge that requires a collaborative approach between health professionals and non-health professionals. Didactic strategies to ensure the same qualifications across these occupational groups are lacking. This article presents the joint learning of selected properties and promotive and obstructive conditions, using the example of a multi-professional Master's programme. It subsequently draws conclusions for didactic concepts. The perceptions of 12 teachers on this Master's programme, all representing different professions, were determined by using a qualitative exploratory survey on the three stated dimensions. With the aid of a summarising content analysis, their statements were condensed and abstracted so as to deduce appropriate requirements for methodical and didactic learning scenarios. In view of the fact that the students have very varied previous knowledge, the main challenge is finding a balance between expertise and tediousness. Establishing essential and common expertise, as well as sensitivity for different perspectives, is made particularly difficult by the fact that health and non-health professions differ greatly in terms of methods and approaches. For a successful outcome, the content focal points and didactic and methodical concepts for a learning group need to take into account the composition of that specific group. Recourse to didactic standard concepts is only possible to a limited extent. The aim of joint teaching and learning of health and non-health professionals is to enhance the understanding of a profession: This is done by making individuals aware of their role in the chain of care, so they can recognise and organise the mutual conditionality of their own and external professional contributions.

Do children go for the nice guys? The influence of speaker benevolence and certainty on selective word learning.

Science.gov (United States)

Bergstra, Myrthe; DE Mulder, Hannah N M; Coopmans, Peter

2018-04-06

This study investigated how speaker certainty (a rational cue) and speaker benevolence (an emotional cue) influence children's willingness to learn words in a selective learning paradigm. In two experiments four- to six-year-olds learnt novel labels from two speakers and, after a week, their memory for these labels was reassessed. Results demonstrated that children retained the label-object pairings for at least a week. Furthermore, children preferred to learn from certain over uncertain speakers, but they had no significant preference for nice over nasty speakers. When the cues were combined, children followed certain speakers, even if they were nasty. However, children did prefer to learn from nice and certain speakers over nasty and certain speakers. These results suggest that rational cues regarding a speaker's linguistic competence trump emotional cues regarding a speaker's affective status in word learning. However, emotional cues were found to have a subtle influence on this process.

Learners' experiences of learning support in selected Western Cape ...

African Journals Online (AJOL)

The learning support assisted in meeting learners' academic, social and emotional needs by addressing barriers to learning, creating conducive learning environments, enhancing learners' self-esteem and improving learners' academic performance. Keywords: academic needs; academic performance; barriers to learning; ...

Computer Mathematics Games and Conditions for Enhancing Young Children's Learning of Number Sense

Science.gov (United States)

Kermani, Hengameh

2017-01-01

Purpose: The present study was designed to examine whether mathematics computer games improved young children's learning of number sense under three different conditions: when used individually, with a peer, and with teacher facilitation. Methodology: This study utilized a mixed methodology, collecting both quantitative and qualitative data. A…

Selective visual attention and motivation: the consequences of value learning in an attentional blink task.

Science.gov (United States)

Raymond, Jane E; O'Brien, Jennifer L

2009-08-01

Learning to associate the probability and value of behavioral outcomes with specific stimuli (value learning) is essential for rational decision making. However, in demanding cognitive conditions, access to learned values might be constrained by limited attentional capacity. We measured recognition of briefly presented faces seen previously in a value-learning task involving monetary wins and losses; the recognition task was performed both with and without constraints on available attention. Regardless of available attention, recognition was substantially enhanced for motivationally salient stimuli (i.e., stimuli highly predictive of outcomes), compared with equally familiar stimuli that had weak or no motivational salience, and this effect was found regardless of valence (win or loss). However, when attention was constrained (because stimuli were presented during an attentional blink, AB), valence determined recognition; win-associated faces showed no AB, but all other faces showed large ABs. Motivational salience acts independently of attention to modulate simple perceptual decisions, but when attention is limited, visual processing is biased in favor of reward-associated stimuli.

Comparing Patterns of Natural Selection across Species Using Selective Signatures

Energy Technology Data Exchange (ETDEWEB)

Shapiro, Jesse; Alm, Eric J.

2007-12-01

Comparing gene expression profiles over many different conditions has led to insights that were not obvious from single experiments. In the same way, comparing patterns of natural selection across a set of ecologically distinct species may extend what can be learned from individual genome-wide surveys. Toward this end, we show how variation in protein evolutionary rates, after correcting for genome-wide effects such as mutation rate and demographic factors, can be used to estimate the level and types of natural selection acting on genes across different species. We identify unusually rapidly and slowly evolving genes, relative to empirically derived genome-wide and gene family-specific background rates for 744 core protein families in 30 c-proteobacterial species. We describe the pattern of fast or slow evolution across species as the"selective signature" of a gene. Selective signatures represent aprofile of selection across species that is predictive of gene function: pairs of genes with correlated selective signatures are more likely to share the same cellular function, and genes in the same pathway can evolve in concert. For example,glycolysis and phenylalanine metabolism genes evolve rapidly in Idiomarina loihiensis, mirroring an ecological shift in carbon source from sugars to amino acids. In a broader context, our results suggest that the genomic landscape is organized into functional modules even at the level of natural selection, and thus it may be easier than expected to understand the complex evolutionary pressures on a cell.

Comparing Patterns of Natural Selection Across Species Using Selective Signatures

Energy Technology Data Exchange (ETDEWEB)

Alm, Eric J.; Shapiro, B. Jesse; Alm, Eric J.

2007-12-18

Comparing gene expression profiles over many different conditions has led to insights that were not obvious from single experiments. In the same way, comparing patterns of natural selection across a set of ecologically distinct species may extend what can be learned from individual genome-wide surveys. Toward this end, we show how variation in protein evolutionary rates, after correcting for genome-wide effects such as mutation rate and demographic factors, can be used to estimate the level and types of natural selection acting on genes across different species. We identify unusually rapidly and slowly evolving genes, relative to empirically derived genome-wide and gene family-specific background rates for 744 core protein families in 30 gamma-proteobacterial species. We describe the pattern of fast or slow evolution across species as the 'selective signature' of a gene. Selective signatures represent a profile of selection across species that is predictive of gene function: pairs of genes with correlated selective signatures are more likely to share the same cellular function, and genes in the same pathway can evolve in concert. For example, glycolysis and phenylalanine metabolism genes evolve rapidly in Idiomarina loihiensis, mirroring an ecological shift in carbon source from sugars to amino acids. In a broader context, our results suggest that the genomic landscape is organized into functional modules even at the level of natural selection, and thus it may be easier than expected to understand the complex evolutionary pressures on a cell.

Using Deep Learning for Targeted Data Selection, Improving Satellite Observation Utilization for Model Initialization

Science.gov (United States)

Lee, Y. J.; Bonfanti, C. E.; Trailovic, L.; Etherton, B.; Govett, M.; Stewart, J.

2017-12-01

At present, a fraction of all satellite observations are ultimately used for model assimilation. The satellite data assimilation process is computationally expensive and data are often reduced in resolution to allow timely incorporation into the forecast. This problem is only exacerbated by the recent launch of Geostationary Operational Environmental Satellite (GOES)-16 satellite and future satellites providing several order of magnitude increase in data volume. At the NOAA Earth System Research Laboratory (ESRL) we are researching the use of machine learning the improve the initial selection of satellite data to be used in the model assimilation process. In particular, we are investigating the use of deep learning. Deep learning is being applied to many image processing and computer vision problems with great success. Through our research, we are using convolutional neural network to find and mark regions of interest (ROI) to lead to intelligent extraction of observations from satellite observation systems. These targeted observations will be used to improve the quality of data selected for model assimilation and ultimately improve the impact of satellite data on weather forecasts. Our preliminary efforts to identify the ROI's are focused in two areas: applying and comparing state-of-art convolutional neural network models using the analysis data from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) weather model, and using these results as a starting point to optimize convolution neural network model for pattern recognition on the higher resolution water vapor data from GOES-WEST and other satellite. This presentation will provide an introduction to our convolutional neural network model to identify and process these ROI's, along with the challenges of data preparation, training the model, and parameter optimization.

New Learning - The IPP Programme: Improvements in Learning and Self Esteem by Changing the Organization of Learning

Science.gov (United States)

Garber, Klaus; Ausserer, Oskar; Giacomuzzi, Salvatore

"New learning" is basically an individualized learning style. "New learning" starts by the individual itself. The individual is the basis for conditions, learning contents, rhythm, duration and intensity of the teaching. The appropriate slogan is: fetch the individual at his personal conditions.

Reinforcement learning modulates the stability of cognitive control settings for object selection

Directory of Open Access Journals (Sweden)

Anthony William Sali

2013-12-01

Full Text Available Cognitive flexibility reflects both a trait that reliably differs between individuals and a state that can fluctuate moment-to-moment. Whether individuals can undergo persistent changes in cognitive flexibility as a result of reward learning is less understood. Here, we investigated whether reinforcing a periodic shift in an object selection strategy can make an individual more prone to switch strategies in a subsequent unrelated task. Participants completed two different choice tasks in which they selected one of four objects in an attempt to obtain a hidden reward on each trial. During a training phase, objects were defined by color. Participants received either consistent reward contingencies in which one color was more often rewarded, or contingencies in which the color that was more often rewarded changed periodically and without warning. Following the training phase, all participants completed a test phase in which reward contingencies were defined by spatial location and the location that was more often rewarded remained constant across the entire task. Those participants who received inconsistent contingencies during training continued to make more variable selections during the test phase in comparison to those who received the consistent training. Furthermore, a difference in the likelihood to switch selections on a trial-by-trial basis emerged between training groups: participants who received consistent contingencies during training were less likely to switch object selections following an unrewarded trial and more likely to repeat a selection following reward. Our findings provide evidence that the extent to which priority shifting is reinforced modulates the stability of cognitive control settings in a persistent manner, such that individuals become generally more or less prone to shifting priorities in the future.

Individual differences in discriminatory fear learning under conditions of ambiguity: a vulnerability factor for anxiety disorders?

NARCIS (Netherlands)

Arnaudova, I.; Krypotos, A.M.; Effting, M.; Boddez, Y.; Kindt, M.; Beckers, T.

2013-01-01

Complex fear learning procedures might be better suited than the common differential fear-conditioning paradigm for detecting individual differences related to vulnerability for anxiety disorders. Two such procedures are the blocking procedure and the protection-from-overshadowing procedure. Their

Selected Lessons Learned through the ISS Design, Development, Assembly, and Operations: Applicability to International Cooperation for Standardization

Science.gov (United States)

Hirsch, David B.

2009-01-01

This slide presentation reviews selected lessons that were learned during the design, development, assembly and operation of the International Space Station. The critical importance of standards and common interfaces is emphasized to create a common operation environment that can lead to flexibility and adaptability.

Simultaneous and Sequential Feature Negative Discriminations: Elemental Learning and Occasion Setting in Human Pavlovian Conditioning

Science.gov (United States)

Baeyens, Frank; Vervliet, Bram; Vansteenwegen, Debora; Beckers, Tom; Hermans, Dirk; Eelen, Paul

2004-01-01

Using a conditioned suppression task, we investigated simultaneous (XA-/A+) vs. sequential (X [right arrow] A-/A+) Feature Negative (FN) discrimination learning in humans. We expected the simultaneous discrimination to result in X (or alternatively the XA configuration) becoming an inhibitor acting directly on the US, and the sequential…

Corticostriatal circuit mechanisms of value-based action selection: Implementation of reinforcement learning algorithms and beyond.

Science.gov (United States)

Morita, Kenji; Jitsev, Jenia; Morrison, Abigail

2016-09-15

Value-based action selection has been suggested to be realized in the corticostriatal local circuits through competition among neural populations. In this article, we review theoretical and experimental studies that have constructed and verified this notion, and provide new perspectives on how the local-circuit selection mechanisms implement reinforcement learning (RL) algorithms and computations beyond them. The striatal neurons are mostly inhibitory, and lateral inhibition among them has been classically proposed to realize "Winner-Take-All (WTA)" selection of the maximum-valued action (i.e., 'max' operation). Although this view has been challenged by the revealed weakness, sparseness, and asymmetry of lateral inhibition, which suggest more complex dynamics, WTA-like competition could still occur on short time scales. Unlike the striatal circuit, the cortical circuit contains recurrent excitation, which may enable retention or temporal integration of information and probabilistic "soft-max" selection. The striatal "max" circuit and the cortical "soft-max" circuit might co-implement an RL algorithm called Q-learning; the cortical circuit might also similarly serve for other algorithms such as SARSA. In these implementations, the cortical circuit presumably sustains activity representing the executed action, which negatively impacts dopamine neurons so that they can calculate reward-prediction-error. Regarding the suggested more complex dynamics of striatal, as well as cortical, circuits on long time scales, which could be viewed as a sequence of short WTA fragments, computational roles remain open: such a sequence might represent (1) sequential state-action-state transitions, constituting replay or simulation of the internal model, (2) a single state/action by the whole trajectory, or (3) probabilistic sampling of state/action. Copyright © 2016. Published by Elsevier B.V.

Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans?

Science.gov (United States)

Holdstock, J S; Mayes, A R; Roberts, N; Cezayirli, E; Isaac, C L; O'Reilly, R C; Norman, K A

2002-01-01

The claim that recognition memory is spared relative to recall after focal hippocampal damage has been disputed in the literature. We examined this claim by investigating object and object-location recall and recognition memory in a patient, YR, who has adult-onset selective hippocampal damage. Our aim was to identify the conditions under which recognition was spared relative to recall in this patient. She showed unimpaired forced-choice object recognition but clearly impaired recall, even when her control subjects found the object recognition task to be numerically harder than the object recall task. However, on two other recognition tests, YR's performance was not relatively spared. First, she was clearly impaired at an equivalently difficult yes/no object recognition task, but only when targets and foils were very similar. Second, YR was clearly impaired at forced-choice recognition of object-location associations. This impairment was also unrelated to difficulty because this task was no more difficult than the forced-choice object recognition task for control subjects. The clear impairment of yes/no, but not of forced-choice, object recognition after focal hippocampal damage, when targets and foils are very similar, is predicted by the neural network-based Complementary Learning Systems model of recognition. This model postulates that recognition is mediated by hippocampally dependent recollection and cortically dependent familiarity; thus hippocampal damage should not impair item familiarity. The model postulates that familiarity is ineffective when very similar targets and foils are shown one at a time and subjects have to identify which items are old (yes/no recognition). In contrast, familiarity is effective in discriminating which of similar targets and foils, seen together, is old (forced-choice recognition). Independent evidence from the remember/know procedure also indicates that YR's familiarity is normal. The Complementary Learning Systems model can

LEARNING AND ENVIRONMENTAL DESIGN: Softer Learning Spaces

Directory of Open Access Journals (Sweden)

E. Ümran TOPÇU

2013-07-01

Full Text Available Learning is a central part of everyone’s life that is often associated with school and  classrooms. Today’ classroom looks and functions like the classroom of an earlier century. Desks lined up in neat rows, facing the teacher and a board or screen is the general condition in many educational institutions. Most of us have sat through classes in plain, hard rooms. Although they did not look very pleasant, we all coped with them. If they could be designed slightly more tolerable, would they help in the betterment of education and learning in any measurable way? This paper aims at describing an attempt to design an alternative classroom. Based on several years of experience, it is observed that there is a demand among students for softer, warmer and more intimate instructional spaces. Students of “People and Environment” Course were asked to select a suitable space to redesign as a “Soft Classroom” within Bahçeşehir University Besiktas Campus  premises. This case study presented a potential research project to etter understand,  how student engagement can be increased by changing learning spaces.

The effects of the selective 5-HT(2C) receptor antagonist SB 242084 on learned helplessness in male Fischer 344 rats.

Science.gov (United States)

Strong, Paul V; Greenwood, Benjamin N; Fleshner, Monika

2009-05-01

Rats exposed to an uncontrollable stressor demonstrate a constellation of behaviors such as exaggerated freezing and deficits in shuttle box escape learning. These behaviors in rats have been called learned helplessness and have been argued to model human stress-related mood disorders. Learned helplessness is thought to be caused by hyperactivation of serotonin (5-HT) neurons in the dorsal raphe nucleus (DRN) and a subsequent exaggerated release of 5-HT in DRN projection sites. Blocking 5-HT(2C) receptors in the face of an increase in serotonin can alleviate anxiety behaviors in some animal models. However, specific 5-HT receptor subtypes involved in learned helplessness remain unknown. The current experiments tested the hypothesis that 5-HT(2C) receptor activation is necessary and sufficient for the expression of learned helplessness. The selective 5-HT(2C) receptor antagonist SB 242084 (1.0 mg/kg) administered i.p. to adult male Fischer 344 rats prior to shuttle box behavioral testing, but not before stress, blocked stress-induced deficits in escape learning but had no effect on the exaggerated shock-elicited freezing. The selective 5-HT(2C) receptor agonist CP-809101 was sufficient to produce learned helplessness-like behaviors in the absence of prior stress and these effects were blocked by pretreatment with SB 242084. Results implicate the 5-HT(2C) receptor subtype in mediating the shuttle box escape deficits produced by exposure to uncontrollable stress and suggest that different postsynaptic 5-HT receptor subtypes underlie the different learned helplessness behaviors.

Formation of imines by selective gold-catalysed aerobic oxidative coupling of alcohols and amines under ambient conditions

DEFF Research Database (Denmark)

Kegnæs, Søren; Mielby, Jerrik Jørgen; Mentzel, Uffe Vie

2010-01-01

with excellent selectivity (above 98%) at moderate conversion under optimized conditions. The effect of catalytic amounts of different bases was studied, along with reaction temperature and time. Utilisation of a selective catalyst system that uses dioxygen as an oxidant and only produces water as by...

A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions

International Nuclear Information System (INIS)

Feng, Ke; Wang, KeSheng; Zhang, Mian; Ni, Qing; Zuo, Ming J

2017-01-01

The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold–Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis. (paper)

5-HT2C receptors in the BNST are necessary for the enhancement of fear learning by selective serotonin reuptake inhibitors.

Science.gov (United States)

Pelrine, Eliza; Pasik, Sara Diana; Bayat, Leyla; Goldschmiedt, Debora; Bauer, Elizabeth P

2016-12-01

Selective serotonin reuptake inhibitors (SSRIs) are widely prescribed to treat anxiety and depression, yet they paradoxically increase anxiety during initial treatment. Acute administration of these drugs prior to learning can also enhance Pavlovian cued fear conditioning. This potentiation has been previously reported to depend upon the bed nucleus of the stria terminalis (BNST). Here, using temporary inactivation, we confirmed that the BNST is not necessary for the acquisition of cued or contextual fear memory. Systemic administration of the SSRI citalopram prior to fear conditioning led to an upregulation of the immediate early gene Arc (activity-regulated cytoskeleton-associated protein) in the oval nucleus of the BNST, and a majority of these neurons expressed the 5-HT2C receptor. Finally, local infusions of a 5-HT2C receptor antagonist directly into the oval nucleus of the BNST prevented the fear memory-enhancing effects of citalopram. These findings highlight the ability of the BNST circuitry to be recruited into gating fear and anxiety-like behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

Relation Between Motility, Accelerated Aging and Gene Expression in Selected Drosophila Strains under Hypergravity Conditions

Science.gov (United States)

Serrano, Paloma; van Loon, Jack J. W. A.; Medina, F. Javier; Herranz, Raúl

2013-02-01

Motility and aging in Drosophila have proven to be highly modified under altered gravity conditions (both in space and ground simulation facilities). In order to find out how closely connected they are, five strains with altered geotactic response or survival rates were selected and exposed to an altered gravity environment of 2 g. By analysing the different motile and behavioural patterns and the median survival rates, we show that altered gravity leads to changes in motility, which will have a negative impact on the flies' survival. Previous results show a differential gene expression between sessile samples and adults and confirm that environmentally-conditioned behavioural patterns constrain flies' gene expression and life span. Therefore, hypergravity is considered an environmental stress factor and strains that do not respond to this new environment experience an increment in motility, which is the major cause for the observed increased mortality also under microgravity conditions. The neutral-geotaxis selected strain (strain M) showed the most severe phenotype, unable to respond to variations in the gravitational field. Alternatively, the opposite phenotype was observed in positive-geotaxis and long-life selected flies (strains B and L, respectively), suggesting that these populations are less sensitive to alterations in the gravitational load. We conclude that the behavioural response has a greater contribution to aging than the modified energy consumption in altered gravity environments.

A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization.

Science.gov (United States)

Vafaee Sharbaf, Fatemeh; Mosafer, Sara; Moattar, Mohammad Hossein

2016-06-01

This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy. CLA is applied due to its capability to learn and model complicated relationships. The selected features from the last phase are evaluated using ROC curve and the most effective while smallest feature subset is determined. The classifiers which are evaluated in the proposed framework are K-nearest neighbor; support vector machine and naïve Bayes. The proposed approach is evaluated on 4 microarray datasets. The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

A Model for Predicting Learning Flow and Achievement in Corporate e-Learning

Science.gov (United States)

Joo, Young Ju; Lim, Kyu Yon; Kim, Su Mi

2012-01-01

The primary objective of this study was to investigate the determinants of learning flow and achievement in corporate online training. Self-efficacy, intrinsic value, and test anxiety were selected as learners' motivational factors, while perceived usefulness and ease of use were also selected as learning environmental factors. Learning flow was…

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

Science.gov (United States)

Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

2014-11-01

Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

Dumb and Lazy? A Comparison of Color Learning and Memory Retrieval in Drones and Workers of the Buff-Tailed Bumblebee, Bombus terrestris, by Means of PER Conditioning.

Directory of Open Access Journals (Sweden)

Leonie Lichtenstein

Full Text Available More than 100 years ago, Karl von Frisch showed that honeybee workers learn and discriminate colors. Since then, many studies confirmed the color learning capabilities of females from various hymenopteran species. Yet, little is known about visual learning and memory in males despite the fact that in most bee species males must take care of their own needs and must find rewarding flowers to obtain food. Here we used the proboscis extension response (PER paradigm to study the color learning capacities of workers and drones of the bumblebee, Bombus terrestris. Light stimuli were paired with sucrose reward delivered to the insects' antennae and inducing a reflexive extension of the proboscis. We evaluated color learning (i.e. conditioned PER to color stimuli in absolute and differential conditioning protocols and mid-term memory retention was measured two hours after conditioning. Different monochromatic light stimuli in combination with neutral density filters were used to ensure that the bumblebees could only use chromatic and not achromatic (e.g. brightness information. Furthermore, we tested if bees were able to transfer the learned information from the PER conditioning to a novel discrimination task in a Y-maze. Both workers and drones were capable of learning and discriminating between monochromatic light stimuli and retrieved the learned stimulus after two hours. Drones performed as well as workers during conditioning and in the memory test, but failed in the transfer test in contrast to workers. Our data clearly show that bumblebees can learn to associate a color stimulus with a sugar reward in PER conditioning and that both workers and drones reach similar acquisition and mid-term retention performances. Additionally, we provide evidence that only workers transfer the learned information from a Pavlovian to an operant situation.

Dumb and Lazy? A Comparison of Color Learning and Memory Retrieval in Drones and Workers of the Buff-Tailed Bumblebee, Bombus terrestris, by Means of PER Conditioning.

Science.gov (United States)

Lichtenstein, Leonie; Sommerlandt, Frank M J; Spaethe, Johannes

2015-01-01

More than 100 years ago, Karl von Frisch showed that honeybee workers learn and discriminate colors. Since then, many studies confirmed the color learning capabilities of females from various hymenopteran species. Yet, little is known about visual learning and memory in males despite the fact that in most bee species males must take care of their own needs and must find rewarding flowers to obtain food. Here we used the proboscis extension response (PER) paradigm to study the color learning capacities of workers and drones of the bumblebee, Bombus terrestris. Light stimuli were paired with sucrose reward delivered to the insects' antennae and inducing a reflexive extension of the proboscis. We evaluated color learning (i.e. conditioned PER to color stimuli) in absolute and differential conditioning protocols and mid-term memory retention was measured two hours after conditioning. Different monochromatic light stimuli in combination with neutral density filters were used to ensure that the bumblebees could only use chromatic and not achromatic (e.g. brightness) information. Furthermore, we tested if bees were able to transfer the learned information from the PER conditioning to a novel discrimination task in a Y-maze. Both workers and drones were capable of learning and discriminating between monochromatic light stimuli and retrieved the learned stimulus after two hours. Drones performed as well as workers during conditioning and in the memory test, but failed in the transfer test in contrast to workers. Our data clearly show that bumblebees can learn to associate a color stimulus with a sugar reward in PER conditioning and that both workers and drones reach similar acquisition and mid-term retention performances. Additionally, we provide evidence that only workers transfer the learned information from a Pavlovian to an operant situation.

Dopamine selectively remediates 'model-based' reward learning: a computational approach.

Science.gov (United States)

Sharp, Madeleine E; Foerde, Karin; Daw, Nathaniel D; Shohamy, Daphna

2016-02-01

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Hyperresponsiveness of the Neural Fear Network During Fear Conditioning and Extinction Learning in Male Cocaine Users

NARCIS (Netherlands)

Kaag, A.M.; Levar, N.; Woutersen, K.; Homberg, J.R.; Brink, W. van den; Reneman, L.; Wingen, G. van

2016-01-01

OBJECTIVE: The authors investigated whether cocaine use disorder is associated with abnormalities in the neural underpinnings of aversive conditioning and extinction learning, as these processes may play an important role in the development and persistence of drug abuse. METHOD: Forty male regular

Using VARK Approach for Assessing Preferred Learning Styles of First Year Medical Sciences Students: A Survey from Iran

OpenAIRE

Peyman, Hadi; Sadeghifar, Jamil; Khajavikhan, Javaher; Yasemi, Masood; Rasool, Mohammad; Yaghoubi, Yasemi Monireh; Nahal, Monireh Mohammad Hassan; Karim, Hemati

2014-01-01

Background: Preferred learning styles of learners are different, which depend on tastes, mentality preparedness, as well as physical condition, in terms of sensory modalities. Identifying and employing appropriate learning styles could play an important role in selecting teaching styles, which can improve education ultimately.

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Science.gov (United States)

Zdravevski, Eftim; Risteska Stojkoska, Biljana; Standl, Marie; Schulz, Holger

2017-01-01

Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Directory of Open Access Journals (Sweden)

Eftim Zdravevski

Full Text Available Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers.The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be

The XRF spectrometer and the selection of analysis conditions (instrumental variables)

International Nuclear Information System (INIS)

Willis, J.P.

2002-01-01

Full text: This presentation will begin with a brief discussion of EDXRF and flat- and curved-crystal WDXRF spectrometers, contrasting the major differences between the three types. The remainder of the presentation will contain a detailed overview of the choice and settings of the many instrumental variables contained in a modern WDXRF spectrometer, and will discuss critically the choices facing the analyst in setting up a WDXRF spectrometer for different elements and applications. In particular it will discuss the choice of tube target (when a choice is possible), the kV and mA settings, tube filters, collimator masks, collimators, analyzing crystals, secondary collimators, detectors, pulse height selection, X-ray path medium (air, nitrogen, vacuum or helium), counting times for peak and background positions and their effect on counting statistics and lower limit of detection (LLD). The use of Figure of Merit (FOM) calculations to objectively choose the best combination of instrumental variables also will be discussed. This presentation will be followed by a shorter session on a subsequent day entitled - A Selection of XRF Conditions - Practical Session, where participants will be given the opportunity to discuss in groups the selection of the best instrumental variables for three very diverse applications. Copyright (2002) Australian X-ray Analytical Association Inc

Interfering effects of retrieval in learning new information.

Science.gov (United States)

Finn, Bridgid; Roediger, Henry L

2013-11-01

In 7 experiments, we explored the role of retrieval in associative updating, that is, in incorporating new information into an associative memory. We tested the hypothesis that retrieval would facilitate incorporating a new contextual detail into a learned association. Participants learned 3 pieces of information-a person's face, name, and profession (in Experiments 1-5). In the 1st phase, participants in all conditions learned faces and names. In the 2nd phase, participants either restudied the face-name pair (the restudy condition) or were given the face and asked to retrieve the name (the test condition). In the 3rd phase, professions were presented for study just after restudy or testing. Our prediction was that the new information (the profession) would be more readily learned following retrieval of the face-name association compared to restudy of the face-name association. However, we found that the act of retrieval generally undermined acquisition of new associations rather than facilitating them. This detrimental effect emerged on both immediate and delayed tests. Further, the effect was not due to selective attention to feedback because we found impairment whether or not feedback was provided after the Phase 2 test. The data are novel in showing that the act of retrieving information can inhibit the ability to learn new information shortly thereafter. The results are difficult to accommodate within current theories that mostly emphasize benefits of retrieval for learning. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Reinforcement learning for microgrid energy management

International Nuclear Information System (INIS)

Kuznetsova, Elizaveta; Li, Yan-Fu; Ruiz, Carlos; Zio, Enrico; Ault, Graham; Bell, Keith

2013-01-01

We consider a microgrid for energy distribution, with a local consumer, a renewable generator (wind turbine) and a storage facility (battery), connected to the external grid via a transformer. We propose a 2 steps-ahead reinforcement learning algorithm to plan the battery scheduling, which plays a key role in the achievement of the consumer goals. The underlying framework is one of multi-criteria decision-making by an individual consumer who has the goals of increasing the utilization rate of the battery during high electricity demand (so as to decrease the electricity purchase from the external grid) and increasing the utilization rate of the wind turbine for local use (so as to increase the consumer independence from the external grid). Predictions of available wind power feed the reinforcement learning algorithm for selecting the optimal battery scheduling actions. The embedded learning mechanism allows to enhance the consumer knowledge about the optimal actions for battery scheduling under different time-dependent environmental conditions. The developed framework gives the capability to intelligent consumers to learn the stochastic environment and make use of the experience to select optimal energy management actions. - Highlights: • A consumer exploits a 2 steps-ahead reinforcement learning for battery scheduling. • The Q-learning based mechanism is fed by the predictions of available wind power. • Wind speed state evolutions are modeled with a Markov chain model. • Optimal scheduling actions are learned through the occurrence of similar scenarios. • The consumer manifests a continuous enhance of his knowledge about optimal actions

Selected engagement factors and academic learning outcomes of undergraduate engineering students

Science.gov (United States)

Justice, Patricia J.

The concept of student engagement and its relationship to successful student performance and learning outcomes has a long history in higher education (Kuh, 2007). Attention to faculty and student engagement has only recently become of interest to the engineering education community. This interest can be attributed to long-standing research by George Kuh's, National Survey of Student Engagement (NSSE) at the Indiana University Center for Postsecondary Research. In addition, research projects sponsored by the National Science Foundation, the Academic Pathway Study (APS) at the Center for the Advancement of Engineering Education (CAEE) and the Center for the Advancement of Scholarship on Engineering Education (CASEE), Measuring Student and Faculty Engagement in Engineering Education, at the National Academy of Engineering. These research studies utilized the framework and data from the Engineering Change study by the Center for the Study of Higher Education, Pennsylvania State, that evaluated the impact of the new Accreditation Board of Engineering and Technology (ABET) EC2000 "3a through k" criteria identify 11 learning outcomes expected of engineering graduates. The purpose of this study was to explore the extent selected engagement factors of 1. institution, 2. social, 3. cognitive, 4. finance, and 5. technology influence undergraduate engineering students and quality student learning outcomes. Through the descriptive statistical analysis indicates that there maybe problems in the engineering program. This researcher would have expected at least 50% of the students to fall in the Strongly Agree and Agree categories. The data indicated that the there maybe problems in the engineering program problems in the data. The problems found ranked in this order: 1). Dissatisfaction with faculty instruction methods and quality of instruction and not a clear understanding of engineering majors , 2). inadequate Engineering faculty and advisors availability especially applicable

Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines

Directory of Open Access Journals (Sweden)

Wenna Zhang

2016-04-01

Full Text Available Data collected from the supervisory control and data acquisition (SCADA system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring.

The Relationship Between Selected Subtests of the Detroit Tests of Learning Aptitude and Second Grade Reading Achievement.

Science.gov (United States)

Sherwood, Charles; Chambless, Martha

Relationships between reading achievement and perceptual skills as measured by selected subtests of the Detroit Tests of Learning Aptitude were investigated in a sample of 73 second graders. Verbal opposites, visual memory for designs, and visual attention span for letters were significantly correlated with both word meaning and vocabulary…

Schwarzian conditions for linear differential operators with selected differential Galois groups

International Nuclear Information System (INIS)

Abdelaziz, Y; Maillard, J-M

2017-01-01

We show that non-linear Schwarzian differential equations emerging from covariance symmetry conditions imposed on linear differential operators with hypergeometric function solutions can be generalized to arbitrary order linear differential operators with polynomial coefficients having selected differential Galois groups. For order three and order four linear differential operators we show that this pullback invariance up to conjugation eventually reduces to symmetric powers of an underlying order-two operator. We give, precisely, the conditions to have modular correspondences solutions for such Schwarzian differential equations, which was an open question in a previous paper. We analyze in detail a pullbacked hypergeometric example generalizing modular forms, that ushers a pullback invariance up to operator homomorphisms. We finally consider the more general problem of the equivalence of two different order-four linear differential Calabi–Yau operators up to pullbacks and conjugation, and clarify the cases where they have the same Yukawa couplings. (paper)

Schwarzian conditions for linear differential operators with selected differential Galois groups

Science.gov (United States)

Abdelaziz, Y.; Maillard, J.-M.

2017-11-01

We show that non-linear Schwarzian differential equations emerging from covariance symmetry conditions imposed on linear differential operators with hypergeometric function solutions can be generalized to arbitrary order linear differential operators with polynomial coefficients having selected differential Galois groups. For order three and order four linear differential operators we show that this pullback invariance up to conjugation eventually reduces to symmetric powers of an underlying order-two operator. We give, precisely, the conditions to have modular correspondences solutions for such Schwarzian differential equations, which was an open question in a previous paper. We analyze in detail a pullbacked hypergeometric example generalizing modular forms, that ushers a pullback invariance up to operator homomorphisms. We finally consider the more general problem of the equivalence of two different order-four linear differential Calabi-Yau operators up to pullbacks and conjugation, and clarify the cases where they have the same Yukawa couplings.

1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0

CERN Document Server

Beyerer, Jürgen

2016-01-01

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Resource selection by the California condor (Gymnogyps californianus relative to terrestrial-based habitats and meteorological conditions.

Directory of Open Access Journals (Sweden)

James W Rivers

Full Text Available Condors and vultures are distinct from most other terrestrial birds because they use extensive soaring flight for their daily movements. Therefore, assessing resource selection by these avian scavengers requires quantifying the availability of terrestrial-based habitats, as well as meteorological variables that influence atmospheric conditions necessary for soaring. In this study, we undertook the first quantitative assessment of habitat- and meteorological-based resource selection in the endangered California condor (Gymnogyps californianus within its California range and across the annual cycle. We found that condor use of terrestrial areas did not change markedly within the annual cycle, and that condor use was greatest for habitats where food resources and potential predators could be detected and where terrain was amenable for taking off from the ground in flight (e.g., sparse habitats, coastal areas. Condors originating from different release sites differed in their use of habitat, but this was likely due in part to variation in habitats surrounding release sites. Meteorological conditions were linked to condor use of ecological subregions, with thermal height, thermal velocity, and wind speed having both positive (selection and negative (avoidance effects on condor use in different areas. We found little evidence of systematic effects between individual characteristics (i.e., sex, age, breeding status or components of the species management program (i.e., release site, rearing method relative to meteorological conditions. Our findings indicate that habitat type and meteorological conditions can interact in complex ways to influence condor resource selection across landscapes, which is noteworthy given the extent of anthropogenic stressors that may impact condor populations (e.g., lead poisoning, wind energy development. Additional studies will be valuable to assess small-scale condor movements in light of these stressors to help minimize

CHANGES IN SELECTIVITY OF GAMMA-AMINOBUTYRIC ACID FORMATION EFFECTED BY FERMENTATION CONDITIONS AND MICROORGANISMS RESOURCES

Directory of Open Access Journals (Sweden)

Kamila Kovalovská

2011-10-01

Full Text Available In this study we observe the effect of fermentation conditions and resources of microorganisms for production of γ-aminobutyric acid (GABA. The content of produced GABA depends on various conditions such as the amount of precursor, an addition of salt, enzyme and the effect of pH. The highest selectivity of GABA (74.0 % from the precursor (L-monosodium glutamate has been determinate in the follow conditions: in the presence of pre-cultured microorganisms from Encián cheese in amount 1.66 % (w/v the source of microorganisms/volume of the fermentation mixture, after the addition of 0.028 % (w/v of CaCl2/volume of the fermentation mixture, 100 μM of pyridoxal-5-phosphate (P-5-P and the GABA precursor concentration in the fermentation mixture 2.6 mg ml-1 in an atmosphere of gas nitrogen. Pure cultures of lactic acid bacteria increased the selectivity of GABA by an average of 20 % compared with bacteria from the path of Encián.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection.

Science.gov (United States)

Baston, Chiara; Ursino, Mauro

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

Directory of Open Access Journals (Sweden)

Chiara Baston

2015-01-01

Full Text Available The basal ganglia (BG are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go, indirect (NoGo, and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges, synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication. Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.

Social learning in cooperative dilemmas.

Science.gov (United States)

Lamba, Shakti

2014-07-22

Helping is a cornerstone of social organization and commonplace in human societies. A major challenge for the evolutionary sciences is to explain how cooperation is maintained in large populations with high levels of migration, conditions under which cooperators can be exploited by selfish individuals. Cultural group selection models posit that such large-scale cooperation evolves via selection acting on populations among which behavioural variation is maintained by the cultural transmission of cooperative norms. These models assume that individuals acquire cooperative strategies via social learning. This assumption remains empirically untested. Here, I test this by investigating whether individuals employ conformist or payoff-biased learning in public goods games conducted in 14 villages of a forager-horticulturist society, the Pahari Korwa of India. Individuals did not show a clear tendency to conform or to be payoff-biased and are highly variable in their use of social learning. This variation is partly explained by both individual and village characteristics. The tendency to conform decreases and to be payoff-biased increases as the value of the modal contribution increases. These findings suggest that the use of social learning in cooperative dilemmas is contingent on individuals' circumstances and environments, and question the existence of stably transmitted cultural norms of cooperation. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Selecting Native Arbuscular Mycorrhizal Fungi to Promote Cassava Growth and Increase Yield under Field Conditions

Science.gov (United States)

Séry, D. Jean-Marc; Kouadjo, Z. G. Claude; Voko, B. R. Rodrigue; Zézé, Adolphe

2016-01-01

The use of arbuscular mycorrhizal fungal (AMF) inoculation in sustainable agriculture is now widespread worldwide. Although the use of inoculants consisting of native AMF is highly recommended as an alternative to commercial ones, there is no strategy to allow the selection of efficient fungal species from natural communities. The objective of this study was (i) to select efficient native AMF species (ii) evaluate their impact on nematode and water stresses, and (iii) evaluate their impact on cassava yield, an important food security crop in tropical and subtropical regions. Firstly, native AMF communities associated with cassava rhizospheres in fields were collected from different areas and 7 AMF species were selected, based upon their ubiquity and abundance. Using these criteria, two morphotypes (LBVM01 and LBVM02) out of the seven AMF species selected were persistently dominant when cassava was used as a trap plant. LBVM01 and LBVM02 were identified as Acaulospora colombiana (most abundant) and Ambispora appendicula, respectively, after phylogenetic analyses of LSU-ITS-SSU PCR amplified products. Secondly, the potential of these two native AMF species to promote growth and enhance tolerance to root-knot nematode and water stresses of cassava (Yavo variety) was evaluated using single and dual inoculation in greenhouse conditions. Of the two AMF species, it was shown that A. colombiana significantly improved the growth of the cassava and enhanced tolerance to water stress. However, both A. colombiana and A. appendicula conferred bioprotective effects to cassava plants against the nematode Meloidogyne spp., ranging from resistance (suppression or reduction of the nematode reproduction) or tolerance (low or no suppression in cassava growth). Thirdly, the potential of these selected native AMF to improve cassava growth and yield was evaluated under field conditions, compared to a commercial inoculant. In these conditions, the A. colombiana single inoculation and the

Learning and Memory

OpenAIRE

1999-01-01

Under various circumstances and in different species the outward expression of learning varies considerably, and this has led to the classification of different categories of learning. Just as there is no generally agreed on definition of learning, there is no one system of classification. Types of learning commonly recognized are: Habituation, sensitization, classical conditioning, operant conditioning, trial and error, taste aversion, latent learning, cultural learning, imprinting, insight ...

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Science.gov (United States)

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf-Rayet stars

Science.gov (United States)

Morello, Giuseppe; Morris, P. W.; Van Dyk, S. D.; Marston, A. P.; Mauerhan, J. C.

2018-01-01

We have investigated and applied machine-learning algorithms for infrared colour selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the Spitzer Galactic Legacy Infrared Midplane Survey Extraordinaire (GLIMPSE) catalogue of the infrared objects in the Galactic plane can be classified into different stellar populations based on the colours inferred from their broad-band photometric magnitudes [J, H and Ks from 2 Micron All Sky Survey (2MASS), and the four Spitzer/IRAC bands]. The algorithms tested in this pilot study are variants of the k-nearest neighbours approach, which is ideal for exploratory studies of classification problems where interrelations between variables and classes are complicated. The aims of this study are (1) to provide an automated tool to select reliable WR candidates and potentially other classes of objects, (2) to measure the efficiency of infrared colour selection at performing these tasks and (3) to lay the groundwork for statistically inferring the total number of WR stars in our Galaxy. We report the performance results obtained over a set of known objects and selected candidates for which we have carried out follow-up spectroscopic observations, and confirm the discovery of four new WR stars.

Serotonergic Modulation of Conditioned Fear

Directory of Open Access Journals (Sweden)

Judith R. Homberg

2012-01-01

Full Text Available Conditioned fear plays a key role in anxiety disorders as well as depression and other neuropsychiatric conditions. Understanding how neuromodulators drive the associated learning and memory processes, including memory consolidation, retrieval/expression, and extinction (recall, is essential in the understanding of (individual differences in vulnerability to these disorders and their treatment. The human and rodent studies I review here together reveal, amongst others, that acute selective serotonin reuptake inhibitor (SSRI treatment facilitates fear conditioning, reduces contextual fear, and increases cued fear, chronic SSRI treatment reduces both contextual and cued fear, 5-HT1A receptors inhibit the acquisition and expression of contextual fear, 5-HT2A receptors facilitates the consolidation of cued and contextual fear, inactivation of 5-HT2C receptors facilitate the retrieval of cued fear memory, the 5-HT3 receptor mediates contextual fear, genetically induced increases in serotonin levels are associated with increased fear conditioning, impaired cued fear extinction, or impaired extinction recall, and that genetically induced 5-HT depletion increases fear conditioning and contextual fear. Several explanations are presented to reconcile seemingly paradoxical relationships between serotonin levels and conditioned fear.

Feature selection for domain knowledge representation through multitask learning

CSIR Research Space (South Africa)

Rosman, Benjamin S

2014-10-01

Full Text Available represent stimuli of interest, and rich feature sets which increase the dimensionality of the space and thus the difficulty of the learning problem. We focus on a multitask reinforcement learning setting, where the agent is learning domain knowledge...

The Conditional Scope of Selective Exposure to Political Television Media, 1996-2012

DEFF Research Database (Denmark)

Robison, Joshua; Leeper, Thomas

Pew Research Center data from 1996 to 2012, we document that exposure to ideological or partisan media is heavily conditioned by time, audience size, and individuals’ interest in national politics. Selective exposure seems to be limited to partisans with a high interest in politics viewing a handful......A considerable amount of research documents an ideological or partisan bias in media exposure: liberals and Democrats are more likely to be exposed to liberal-leaning media while conservatives and Republicans are more likely to be exposed to conservative-leaning media. Much of this research......, however, was conducted in the mid-2000’s, a politically contentious period in American politics. We argue that there are many reasons to expect this political context to be a period that encouraged high degrees of selective exposure, especially among partisans and those with high political interest. Using...

Effects of OEF/OIF-Related Physical and Emotional Co-Morbidities on Associative Learning: Concurrent Delay and Trace Eyeblink Classical Conditioning

Directory of Open Access Journals (Sweden)

Regina E. McGlinchey

2014-03-01

Full Text Available This study examined the performance of veterans and active duty personnel who served in Operation Enduring Freedom and/or Operation Iraqi Freedom (OEF/OIF on a basic associative learning task. Eighty-eight individuals participated in this study. All received a comprehensive clinical evaluation to determine the presence and severity of posttraumatic stress disorder (PTSD and traumatic brain injury (TBI. The eyeblink conditioning task was composed of randomly intermixed delay and trace conditioned stimulus (CS and unconditioned stimulus (US pairs (acquisition followed by a series of CS only trials (extinction. Results revealed that those with a clinical diagnosis of PTSD or a diagnosis of PTSD with comorbid mTBI acquired delay and trace conditioned responses (CRs to levels and at rates similar to a deployed control group, thus suggesting intact basic associative learning. Differential extinction impairment was observed in the two clinical groups. Acquisition of CRs for both delay and trace conditioning, as well as extinction of trace CRs, was associated with alcoholic behavior across all participants. These findings help characterize the learning and memory function of individuals with PTSD and mTBI from OEF/OIF and raise the alarming possibility that the use of alcohol in this group may lead to more significant cognitive dysfunction.

Self-ordered pointing and visual conditional associative learning tasks in drug-free schizophrenia spectrum disorder patients

Directory of Open Access Journals (Sweden)

Galluzzo Alessandro

2008-01-01

Full Text Available Abstract Background There is evidence of a link between schizophrenia and a deficit of working memory, but this has been derived from tasks not specifically developed to probe working memory per se. Our aim was to investigate whether working memory deficits may be detected across different paradigms using the self-ordered pointing task (SOPT and the visual conditional associative learning task (VCALT in patients with schizophrenia spectrum disorders and healthy controls. The current literature suggests deficits in schizophrenia spectrum disorder patients versus healthy controls but these studies frequently involved small samples, broad diagnostic criteria, inclusion of patients on antipsychotic medications, and were not controlled for symptom domains, severity of the disorder, etc. To overcome some of these limitations, we investigated the self-monitoring and conditional associative learning abilities of a numerically representative sample of healthy controls and a group of non-deteriorated, drug-free patients hospitalized for a schizophrenia spectrum disorder with florid, mainly positive psychotic symptoms. Methods Eighty-five patients with a schizophrenia spectrum disorder (DSM-IV-TR diagnosis of schizophrenia (n = 71 or schizophreniform disorder (n = 14 and 80 healthy controls entered the study. The clinical picture was dominated by positive symptoms. The healthy control group had a negative personal and family history of schizophrenia or mood disorder and satisfied all the inclusion and exclusion criteria other than variables related to schizophrenia spectrum disorders. Results Compared to controls, patients had worse performances on SOPT, VCALT and higher SOPT/VCALT ratios, not affected by demographic or clinical variables. ROC curves showed that SOPT, VCALT, and SOPT/VCALT ratio had good accuracy in discriminating patients from controls. The SOPT and VCALT scores were inter-correlated in controls but not in patients. Conclusion The

Machine-Learning Techniques for the Determination of Attrition of Forces Due to Atmospheric Conditions

Science.gov (United States)

2018-02-01

selected as a proof of concept due to its vast number of data points. While this report does note some trends associated with temperature and dew...separate data sets for helicopters and airplanes, while selectively requesting the event IDs, descriptions of events, light conditions, temperature , dew...weather events) and the error rate for that class . The rows are labeled for the actual occurrence of those events. Thus, for every row–column

The experiences of patients with Duchenne muscular dystrophy in facing and learning about their clinical conditions.

Science.gov (United States)

Fujino, Haruo; Iwata, Yuko; Saito, Toshio; Matsumura, Tsuyoshi; Fujimura, Harutoshi; Imura, Osamu

2016-01-01

Patients experience extreme difficulty when facing an intractable genetic disease. Herein, we examine the experiences of patients with Duchenne muscular dystrophy in facing and learning about their disease. A total of seven patients with Duchenne muscular dystrophy (age range: 20-48) participated. We conducted in-depth interviews with them about how they learned of their disease and how their feelings regarding the disease changed over time. Transcribed data were analysed using thematic analysis. The following themes emerged from this analysis: "experiences before receiving the diagnosis," "experiences when they learned of their condition and progression of the disease," "supports," and "desired explanations." Anxiety and worry were most pronounced when they had to transition to using wheelchairs or respirators due to disease progression; indeed, such transitions affect the patients psychological adjustment. In such times, support from significant others in their lives helped patients adjust.

Contextual Change After Fear Acquisition Affects Conditioned Responding and the Time Course of Extinction Learning-Implications for Renewal Research.

Science.gov (United States)

Sjouwerman, Rachel; Niehaus, Johanna; Lonsdorf, Tina B

2015-01-01

Context plays a central role in retrieving (fear) memories. Accordingly, context manipulations are inherent to most return of fear (ROF) paradigms (in particular renewal), involving contextual changes after fear extinction. Context changes are, however, also often embedded during earlier stages of ROF experiments such as context changes between fear acquisition and extinction (e.g., in ABC and ABA renewal). Previous studies using these paradigms have however focused exclusively on the context switch after extinction (i.e., renewal). Thus, the possibility of a general effect of context switch on conditioned responding that may not be conditional to preceding extinction learning remains unstudied. Hence, the current study investigated the impact of a context switch between fear acquisition and extinction on immediate conditioned responding and on the time-course of extinction learning by using a multimodal approach. A group that underwent contextual change after fear conditioning (AB; n = 36) was compared with a group without a contextual change from acquisition to extinction (AA; n = 149), while measuring physiological (skin conductance and fear potentiated startle) measures and subjective fear ratings. Contextual change between fear acquisition and extinction had a pronounced effect on both immediate conditioned responding and on the time course of extinction learning in skin conductance responses and subjective fear ratings. This may have important implications for the mechanisms underlying and the interpretation of the renewal effect (i.e., contextual switch after extinction). Consequently, future studies should incorporate designs and statistical tests that disentangle general effects of contextual change from genuine ROF effects.

Providing QoS through machine-learning-driven adaptive multimedia applications.

Science.gov (United States)

Ruiz, Pedro M; Botía, Juan A; Gómez-Skarmeta, Antonio

2004-06-01

We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.

The Effects of Hypertext Gloss on Comprehension and Vocabulary Retention under Incidental and Intentional Learning Conditions

Science.gov (United States)

Zandieh, Zeinab; Jafarigohar, Manoochehr

2012-01-01

The present study investigated comprehension, immediate and delayed vocabulary retention under incidental and intentional learning conditions via computer mediated hypertext gloss. One hundred and eighty four (N = 184) intermediate students of English as a foreign language at an English school participated in the study. They were randomly assigned…

An introduction to machine learning with Scikit-Learn

CERN Multimedia

CERN. Geneva

2015-01-01

This tutorial gives an introduction to the scientific ecosystem for data analysis and machine learning in Python. After a short introduction of machine learning concepts, we will demonstrate on High Energy Physics data how a basic supervised learning analysis can be carried out using the Scikit-Learn library. Topics covered include data loading facilities and data representation, supervised learning algorithms, pipelines, model selection and evaluation, and model introspection.

Selective N-alkylation of amines using nitriles under hydrogenation conditions: facile synthesis of secondary and tertiary amines.

Science.gov (United States)

Ikawa, Takashi; Fujita, Yuki; Mizusaki, Tomoteru; Betsuin, Sae; Takamatsu, Haruki; Maegawa, Tomohiro; Monguchi, Yasunari; Sajiki, Hironao

2012-01-14

Nitriles were found to be highly effective alkylating reagents for the selective N-alkylation of amines under catalytic hydrogenation conditions. For the aromatic primary amines, the corresponding secondary amines were selectively obtained under Pd/C-catalyzed hydrogenation conditions. Although the use of electron poor aromatic amines or bulky nitriles showed a lower reactivity toward the reductive alkylation, the addition of NH(4)OAc enhanced the reactivity to give secondary aromatic amines in good to excellent yields. Under the same reaction conditions, aromatic nitro compounds instead of the aromatic primary amines could be directly transformed into secondary amines via a domino reaction involving the one-pot hydrogenation of the nitro group and the reductive alkylation of the amines. While aliphatic amines were effectively converted to the corresponding tertiary amines under Pd/C-catalyzed conditions, Rh/C was a highly effective catalyst for the N-monoalkylation of aliphatic primary amines without over-alkylation to the tertiary amines. Furthermore, the combination of the Rh/C-catalyzed N-monoalkylation of the aliphatic primary amines and additional Pd/C-catalyzed alkylation of the resulting secondary aliphatic amines could selectively prepare aliphatic tertiary amines possessing three different alkyl groups. According to the mechanistic studies, it seems reasonable to conclude that nitriles were reduced to aldimines before the nucleophilic attack of the amine during the first step of the reaction.

Do personality traits predict individual differences in excitatory and inhibitory learning?

Directory of Open Access Journals (Sweden)

Zhimin eHe

2013-05-01

Full Text Available Conditioned inhibition (CI is demonstrated in classical conditioning when a stimulus is used to signal the omission of an otherwise expected outcome. This basic learning ability is involved in a wide range of normal behaviour - and thus its disruption could produce a correspondingly wide range of behavioural deficits. The present study employed a computer-based task to measure conditioned excitation and inhibition in the same discrimination procedure. Conditioned inhibition by summation test was clearly demonstrated. Additionally summary measures of excitatory and inhibitory learning (difference scores were calculated in order to explore how performance related to individual differences in a large sample of normal participants (n=176 following exclusion of those not meeting the basic learning criterion. The individual difference measures selected derive from two biologically-based personality theories, Gray’s reinforcement sensitivity theory (1982 and Eysenck’s psychoticism, extraversion and neuroticism theory (1991. Following the behavioural tasks, participants completed the behavioural inhibition system/behavioural activation system scales (BIS/BAS and the Eysenck personality questionnaire revised short scale (EPQ-RS. Analyses of the relationship between scores on each of the scales and summary measures of excitatory and inhibitory learning suggested that those with higher BAS (specifically the drive sub-scale and higher EPQ-RS neuroticism showed reduced levels of excitatory conditioning. Inhibitory conditioning was similarly attenuated in those with higher EPQ-RS neuroticism, as well as in those with higher BIS scores. Thus the findings are consistent with higher levels of neuroticism being accompanied by generally impaired associative learning, both inhibitory and excitatory. There was also evidence for some dissociation in the effects of behavioural activation and behavioural inhibition on excitatory and inhibitory learning respectively.

The use of EMG biofeedback for learning of selective activation of intra-muscular parts within the serratus anterior muscle

DEFF Research Database (Denmark)

Holtermann, A; Mork, P J; Andersen, L L

2010-01-01

the serratus anterior with visual EMG biofeedback, while the activity of four parts of the serratus anterior and four parts of the trapezius muscle was recorded. One subject was able to selectively activate both the upper and the lower serratus anterior respectively. Moreover, three subjects managed...... to selectively activate the lower serratus anterior, and two subjects learned to selectively activate the upper serratus anterior. During selective activation of the lower serratus anterior, the activity of this muscle part was 14.4+/-10.3 times higher than the upper serratus anterior activity (P....05). The corresponding ratio for selective upper serratus vs. lower serratus anterior activity was 6.4+/-1.7 (Ptimes higher synergistic activity of the lower trapezius compared with the upper trapezius (P

Lack of effect of Pitressin on the learning ability of Brattleboro rats with diabetes insipidus using positively reinforced operant conditioning.

Science.gov (United States)

Laycock, J F; Gartside, I B

1985-08-01

Brattleboro rats with hereditary hypothalamic diabetes insipidus (BDI) received daily subcutaneous injections of vasopressin in the form of Pitressin tannate (0.5 IU/24 hr). They were initially deprived of food and then trained to work for food reward in a Skinner box to a fixed ratio of ten presses for each pellet received. Once this schedule had been learned the rats were given a discrimination task daily for seven days. The performances of these BDI rats were compared with those of rats of the parent Long Evans (LE) strain receiving daily subcutaneous injections of vehicle (arachis oil). Comparisons were also made between these two groups of treated animals and untreated BDI and LE rats studied under similar conditions. In the initial learning trial, both control and Pitressin-treated BDI rats performed significantly better, and manifested less fear initially, than the control or vehicle-injected LE rats when first placed in the Skinner box. Once the initial task had been learned there was no marked difference in the discrimination learning between control or treated BDI and LE animals. These results support the view that vasopressin is not directly involved in all types of learning behaviour, particularly those involving positively reinforced operant conditioning.

Value conditioning modulates visual working memory processes.

Science.gov (United States)

Thomas, Paul M J; FitzGibbon, Lily; Raymond, Jane E

2016-01-01

Learning allows the value of motivationally salient events to become associated with stimuli that predict those events. Here, we asked whether value associations could facilitate visual working memory (WM), and whether such effects would be valence dependent. Our experiment was specifically designed to isolate value-based effects on WM from value-based effects on selective attention that might be expected to bias encoding. In a simple associative learning task, participants learned to associate the color of tinted faces with gaining or losing money or neither. Tinted faces then served as memoranda in a face identity WM task for which previously learned color associations were irrelevant and no monetary outcomes were forthcoming. Memory was best for faces with gain-associated tints, poorest for faces with loss-associated tints, and average for faces with no-outcome-associated tints. Value associated with 1 item in the WM array did not modulate memory for other items in the array. Eye movements when studying faces did not depend on the valence of previously learned color associations, arguing against value-based biases being due to differential encoding. This valence-sensitive value-conditioning effect on WM appears to result from modulation of WM maintenance processes. (c) 2015 APA, all rights reserved).

Experience during Early Adulthood Shapes the Learning Capacities and the Number of Synaptic Boutons in the Mushroom Bodies of Honey Bees ("Apis mellifera")

Science.gov (United States)

Cabirol, Amélie; Brooks, Rufus; Groh, Claudia; Barron, Andrew B.; Devaud, Jean-Marc

2017-01-01

The honey bee mushroom bodies (MBs) are brain centers required for specific learning tasks. Here, we show that environmental conditions experienced as young adults affect the maturation of MB neuropil and performance in a MB-dependent learning task. Specifically, olfactory reversal learning was selectively impaired following early exposure to an…

Learning objects and interactive whiteboards: a evaluation proposal of learning objects for mathematics teaching

Directory of Open Access Journals (Sweden)

Silvio Henrique Fiscarelli

2016-05-01

Full Text Available The current conditions of the classroom learning tend to be a one-way process based in teacher exposition, this make a negative impact on learning make it a mechanical and not meaningful activity. One possibility to improve the quality of teaching is to innovate methodologies and varying forms of presenting information to students, such as the use of technology in the teaching process. The Interactive Whiteboard (IBW is one of the technologies that are being implemented in Brazilian schools. One of the promising possibilities to add value to the use of LDI in classroom are "learning objects" (LO. However, one problem is that often the LO are not fully suited to the dynamics of IWB, whether functional or pedagogical point of view. The objective of this study is to analyze and propose a set of indicators that evaluate the learning objects for use in conjunction with Interactive Whiteboards. The selection and definition of evaluation indicators was carried from the literature review on the subject and based on LDI experiences of use in Municipal Elementary School. After defining the set of indicators was conducted a evaluation of a sample of 30 OA utilized to teaching mathematics in 3rd grade of elementary school. The results of the evaluation indicate that the proposed indicators are suitable for a pre-analysis of OA and assisting in the process of selection of these.

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

Energy Technology Data Exchange (ETDEWEB)

Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2014-11-01

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

International Nuclear Information System (INIS)

Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

2014-01-01

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

A board game to assist pharmacy students in learning metabolic pathways.

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Rose, Tyler M

2011-11-10

To develop and evaluate a board game designed to increase students' enjoyment of learning metabolic pathways; their familiarity with pathway reactions, intermediates, and regulation; and, their understanding of how pathways relate to one another and to selected biological conditions. The board game, entitled Race to Glucose, was created as a team activity for first-year pharmacy students in the biochemistry curriculum. A majority of respondents agreed that the game was helpful for learning regulation, intermediates, and interpathway relationships but not for learning reactions, formation of energetic molecules, or relationships, to biological conditions. There was a significant increase in students' scores on game-related examination questions (68.8% pretest vs. 81.3% posttest), but the improvement was no greater than that for examination questions not related to the game (12.5% vs. 10.9%). First-year pharmacy students considered Race to Glucose to be an enjoyable and helpful tool for learning intermediates, regulation, and interpathway relationships.

Experience and Lessons Learned from Conditioning of Spent Sealed Sources in Singapore - 13107

Energy Technology Data Exchange (ETDEWEB)

Hong, Dae-Seok; Kang, Il-Sik; Jang, Kyung-Duk; Jang, Won-Hyuk [Korea Atomic Energy Research Institute, 1045 Daedeokdaero, Yuseong, Daejeon (Korea, Republic of); Hoo, Wee-Teck [National Environment Agency, 40 Scotts Road 228231 (Singapore)

2013-07-01

In 2010, IAEA requested KAERI (Korea Atomic Energy Research Institute) to support Singapore for conditioning spent sealed sources. Those that had been used for a lightning conductor, check source, or smoke detector, various sealed sources had been collected and stored by the NEA (National Environment Agency) in Singapore. Based on experiences for the conditioning of Ra-226 sources in some Asian countries since 2000, KAERI sent an expert team to Singapore for the safe management of spent sealed sources in 2011. As a result of the conditioning, about 575.21 mCi of Am-241, Ra-226, Co-60, and Sr-90 were safely conditioned in 3 concrete lining drums with the cooperation of the KAERI expert team, the IAEA supervisor, the NEA staff and local laborers in Singapore. Some lessons were learned during the operation: (1) preparations by a local authority are very helpful for an efficient operation, (2) a preliminary inspection by an expert team is helpful for the operation, (3) brief reports before and after daily operation are useful for communication, and (4) a training opportunity is required for the sustainability of the expert team. (authors)

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

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Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

Conditional maximum-entropy method for selecting prior distributions in Bayesian statistics

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Abe, Sumiyoshi

2014-11-01

The conditional maximum-entropy method (abbreviated here as C-MaxEnt) is formulated for selecting prior probability distributions in Bayesian statistics for parameter estimation. This method is inspired by a statistical-mechanical approach to systems governed by dynamics with largely separated time scales and is based on three key concepts: conjugate pairs of variables, dimensionless integration measures with coarse-graining factors and partial maximization of the joint entropy. The method enables one to calculate a prior purely from a likelihood in a simple way. It is shown, in particular, how it not only yields Jeffreys's rules but also reveals new structures hidden behind them.

Object learning improves feature extraction but does not improve feature selection.

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Linus Holm

Full Text Available A single glance at your crowded desk is enough to locate your favorite cup. But finding an unfamiliar object requires more effort. This superiority in recognition performance for learned objects has at least two possible sources. For familiar objects observers might: 1 select more informative image locations upon which to fixate their eyes, or 2 extract more information from a given eye fixation. To test these possibilities, we had observers localize fragmented objects embedded in dense displays of random contour fragments. Eight participants searched for objects in 600 images while their eye movements were recorded in three daily sessions. Performance improved as subjects trained with the objects: The number of fixations required to find an object decreased by 64% across the 3 sessions. An ideal observer model that included measures of fragment confusability was used to calculate the information available from a single fixation. Comparing human performance to the model suggested that across sessions information extraction at each eye fixation increased markedly, by an amount roughly equal to the extra information that would be extracted following a 100% increase in functional field of view. Selection of fixation locations, on the other hand, did not improve with practice.

Social learning and evolution: the cultural intelligence hypothesis

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van Schaik, Carel P.; Burkart, Judith M.

2011-01-01

If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer. PMID:21357223

Social learning and evolution: the cultural intelligence hypothesis.

Science.gov (United States)

van Schaik, Carel P; Burkart, Judith M

2011-04-12

If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer.

Catalog Learning: Carabid Beetles Learn to Manipulate with Innate Coherent Behavioral Patterns

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Zhanna Reznikova

2013-07-01

Full Text Available One of the most fascinating problems in comparative psychology is how learning contributes to solving specific functional problems in animal life, and which forms of learning our species shares with non-human animals. Simulating a natural situation of territorial conflicts between predatory carabids and red wood ants in field and laboratory experiments, we have revealed a relatively simple and quite natural form of learning that has been overlooked. We call it catalog learning, the name we give to the ability of animals to establish associations between stimuli and coherent behavioral patterns (patterns consist of elementary motor acts that have a fixed order. Instead of budgeting their motor acts gradually, from chaotic to rational sequences in order to learn something new, which is characteristic for a conditioning response, animals seem to be “cataloguing” their repertoire of innate coherent behavioral patterns in order to optimize their response to a certain repetitive event. This form of learning can be described as “stimulus-pattern” learning. In our experiments four “wild” carabid species, whose cognitive abilities have never been studied before, modified their behavior in a rather natural manner in order to avoid damage from aggressive ants. Beetles learned to select the relevant coherent behavioral patterns from the set of seven patterns, which are common to all four species and apparently innate. We suggest that this form of learning differs from the known forms of associative learning, and speculate that it is quite universal and can be present in a wide variety of species, both invertebrate and vertebrate. This study suggests a new link between the concepts of cognition and innateness.

Relation between motility, accelerated aging and gene expression in selected Drosophila strains under hypergravity conditions

NARCIS (Netherlands)

Serrano, P.; van Loon, J.J.W.A.; Javier Medina, F.; Herranz, R.

2013-01-01

Motility and aging in Drosophila have proven to be highly modified under altered gravity conditions (both in space and ground simulation facilities). In order to find out how closely connected they are, five strains with altered geotactic response or survival rates were selected and exposed to an

When a Fly Has to Fly to Reproduce: Selection against Conditional Recessive Lethals in "Drosophila"

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Plunkett, Andrea D.; Yampolsky, Lev Y.

2010-01-01

We propose an experimental model suitable for demonstrating allele frequency change in Drosophila melanogaster populations caused by selection against an easily scorable conditional lethal, namely recessive flightless alleles such as apterous and vestigial. Homozygotes for these alleles are excluded from reproduction because the food source used…

Bias correction for selecting the minimal-error classifier from many machine learning models.

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Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Social makes smart: rearing conditions affect learning and social behaviour in jumping spiders.

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Liedtke, J; Schneider, J M

2017-11-01

There is a long-standing debate as to whether social or physical environmental aspects drive the evolution and development of cognitive abilities. Surprisingly few studies make use of developmental plasticity to compare the effects of these two domains during development on behaviour later in life. Here, we present rearing effects on the development of learning abilities and social behaviour in the jumping spider Marpissa muscosa. These spiders are ideally suited for this purpose because they possess the ability to learn and can be reared in groups but also in isolation without added stress. This is a critical but rarely met requirement for experimentally varying the social environment to test its impact on cognition. We split broods of spiders and reared them either in a physically or in a socially enriched environment. A third group kept under completely deprived conditions served as a 'no-enrichment' control. We tested the spiders' learning abilities by using a modified T-maze. Social behaviour was investigated by confronting spiders with their own mirror image. Results show that spiders reared in groups outperform their conspecifics from the control, i.e. 'no-enrichment', group in both tasks. Physical enrichment did not lead to such an increased performance. We therefore tentatively suggest that growing up in contact with conspecifics induces the development of cognitive abilities in this species.

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

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Yue Hu

2018-01-01

Full Text Available An energy management strategy (EMS is important for hybrid electric vehicles (HEVs since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs.

An experimental model for the study of cognitive disorders: the hippocampus and associative learning in mice.

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Delgado-García, José M; Gruart, Agnès

2008-12-01

The availability of transgenic mice mimicking selective human neurodegenerative and psychiatric disorders calls for new electrophysiological and microstimulation techniques capable of being applied in vivo in this species. In this article, we will concentrate on experiments and techniques developed in our laboratory during the past few years. Thus we have developed different techniques for the study of learning and memory capabilities of wild-type and transgenic mice with deficits in cognitive functions, using classical conditioning procedures. These techniques include different trace (tone/SHOCK and shock/SHOCK) conditioning procedures ? that is, a classical conditioning task involving the cerebral cortex, including the hippocampus. We have also developed implantation and recording techniques for evoking long-term potentiation (LTP) in behaving mice and for recording the evolution of field excitatory postsynaptic potentials (fEPSP) evoked in the hippocampal CA1 area by the electrical stimulation of the commissural/Schaffer collateral pathway across conditioning sessions. Computer programs have also been developed to quantify the appearance and evolution of eyelid conditioned responses and the slope of evoked fEPSPs. According to the present results, the in vivo recording of the electrical activity of selected hippocampal sites during classical conditioning of eyelid responses appears to be a suitable experimental procedure for studying learning capabilities in genetically modified mice, and an excellent model for the study of selected neuropsychiatric disorders compromising cerebral cortex functioning.

Learning-induced Dependence of Neuronal Activity in Primary Motor Cortex on Motor Task Condition.

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Cai, X; Shimansky, Y; He, Jiping

2005-01-01

A brain-computer interface (BCI) system such as a cortically controlled robotic arm must have a capacity of adjusting its function to a specific environmental condition. We studied this capacity in non-human primates based on chronic multi-electrode recording from the primary motor cortex of a monkey during the animal's performance of a center-out 3D reaching task and adaptation to external force perturbations. The main condition-related feature of motor cortical activity observed before the onset of force perturbation was a phasic raise of activity immediately before the perturbation onset. This feature was observed during a series of perturbation trials, but were absent under no perturbations. After adaptation has been completed, it usually was taking the subject only one trial to recognize a change in the condition to switch the neuronal activity accordingly. These condition-dependent features of neuronal activity can be used by a BCI for recognizing a change in the environmental condition and making corresponding adjustments, which requires that the BCI-based control system possess such advanced properties of the neural motor control system as capacity to learn and adapt.

When bigger is not better: selection against large size, high condition and fast growth in juvenile lemon sharks.

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Dibattista, J D; Feldheim, K A; Gruber, S H; Hendry, A P

2007-01-01

Selection acting on large marine vertebrates may be qualitatively different from that acting on terrestrial or freshwater organisms, but logistical constraints have thus far precluded selection estimates for the former. We overcame these constraints by exhaustively sampling and repeatedly recapturing individuals in six cohorts of juvenile lemon sharks (450 age-0 and 255 age-1 fish) at an enclosed nursery site (Bimini, Bahamas). Data on individual size, condition factor, growth rate and inter-annual survival were used to test the 'bigger is better', 'fatter is better' and 'faster is better' hypotheses of life-history theory. For age-0 sharks, selection on all measured traits was weak, and generally acted against large size and high condition. For age-1 sharks, selection was much stronger, and consistently acted against large size and fast growth. These results suggest that selective pressures at Bimini may be constraining the evolution of large size and fast growth, an observation that fits well with the observed small size and low growth rate of juveniles at this site. Our results support those of some other recent studies in suggesting that bigger/fatter/faster is not always better, and may often be worse.

The Impact of Learning Curve Model Selection and Criteria for Cost Estimation Accuracy in the DoD

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2016-04-30

different in the future due to machines” • Heightened scrutiny of cost estimates • Budget Control Act of 2011 seeks to reduce federal deficit ...qÜáêíÉÉåíÜ=^ååì~ä= ^Åèìáëáíáçå=oÉëÉ~êÅÜ= póãéçëáìã= qÜìêëÇ~ó=pÉëëáçåë= sçäìãÉ=ff= = The Impact of Learning Curve Model Selection and Criteria for Cost...Assistant Division Director, Institute for Defense Analyses Bruce Harmon, Research Staff Member, Institute for Defense Analyses The Impact of Learning