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

Mere exposure alters category learning of novel objects

Directory of Open Access Journals (Sweden)

Jonathan R Folstein

2010-08-01

Full Text Available We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Mere exposure alters category learning of novel objects.

Science.gov (United States)

Folstein, Jonathan R; Gauthier, Isabel; Palmeri, Thomas J

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Differences between Neural Activity in Prefrontal Cortex and Striatum during Learning of Novel Abstract Categories

OpenAIRE

Antzoulatos, Evan G.; Miller, Earl K.

2011-01-01

Learning to classify diverse experiences into meaningful groups, like categories, is fundamental to normal cognition. To understand its neural basis, we simultaneously recorded from multiple electrodes in the lateral prefrontal cortex and dorsal striatum, two interconnected brain structures critical for learning. Each day, monkeys learned to associate novel, abstract dot-based categories with a right vs. left saccade. Early on, when they could acquire specific stimulus-response associations, ...

Order of Presentation Effects in Learning Color Categories

Science.gov (United States)

Sandhofer, Catherine M.; Doumas, Leonidas A. A.

2008-01-01

Two studies, an experimental category learning task and a computational simulation, examined how sequencing training instances to maximize comparison and memory affects category learning. In Study 1, 2-year-old children learned color categories with three training conditions that varied in how categories were distributed throughout training and…

Observation versus classification in supervised category learning.

Science.gov (United States)

Levering, Kimery R; Kurtz, Kenneth J

2015-02-01

The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle. We compare classification learning to a supervised observational learning task in which learners are shown labeled examples but make no classification response. The goals of this work sit at two levels: (1) testing for differences in the nature of the category representations that arise from two basic learning modes; and (2) evaluating the generative/discriminative continuum as a theoretical tool for understand learning modes and their outcomes. Specifically, we view the guess-and-correct cycle as consistent with a more discriminative approach and therefore expected it to lead to narrower category knowledge. Across two experiments, the observational mode led to greater sensitivity to distributional properties of features and correlations between features. We conclude that a relatively subtle procedural difference in supervised category learning substantially impacts what learners come to know about the categories. The results demonstrate the value of the generative/discriminative continuum as a tool for advancing the psychology of category learning and also provide a valuable constraint for formal models and associated theories.

Pattern-Induced Covert Category Learning in Songbirds.

Science.gov (United States)

Comins, Jordan A; Gentner, Timothy Q

2015-07-20

Language is uniquely human, but its acquisition may involve cognitive capacities shared with other species. During development, language experience alters speech sound (phoneme) categorization. Newborn infants distinguish the phonemes in all languages but by 10 months show adult-like greater sensitivity to native language phonemic contrasts than non-native contrasts. Distributional theories account for phonetic learning by positing that infants infer category boundaries from modal distributions of speech sounds along acoustic continua. For example, tokens of the sounds /b/ and /p/ cluster around different mean voice onset times. To disambiguate overlapping distributions, contextual theories propose that phonetic category learning is informed by higher-level patterns (e.g., words) in which phonemes normally occur. For example, the vowel sounds /Ι/ and /e/ can occupy similar perceptual spaces but can be distinguished in the context of "with" and "well." Both distributional and contextual cues appear to function in speech acquisition. Non-human species also benefit from distributional cues for category learning, but whether category learning benefits from contextual information in non-human animals is unknown. The use of higher-level patterns to guide lower-level category learning may reflect uniquely human capacities tied to language acquisition or more general learning abilities reflecting shared neurobiological mechanisms. Using songbirds, European starlings, we show that higher-level pattern learning covertly enhances categorization of the natural communication sounds. This observation mirrors the support for contextual theories of phonemic category learning in humans and demonstrates a general form of learning not unique to humans or language. Copyright © 2015 Elsevier Ltd. All rights reserved.

Consider the category: The effect of spacing depends on individual learning histories.

Science.gov (United States)

Slone, Lauren K; Sandhofer, Catherine M

2017-07-01

The spacing effect refers to increased retention following learning instances that are spaced out in time compared with massed together in time. By one account, the advantages of spaced learning should be independent of task particulars and previous learning experiences given that spacing effects have been demonstrated in a variety of tasks across the lifespan. However, by another account, spaced learning should be affected by previous learning because past learning affects the memory and attention processes that form the crux of the spacing effect. The current study investigated whether individuals' learning histories affect the role of spacing in category learning. We examined the effect of spacing on 24 2- to 3.5-year-old children's learning of categories organized by properties to which children's previous learning experiences have biased them to attend (i.e., shape) and properties to which children are less biased to attend (i.e., texture and color). Spaced presentations led to significantly better learning of shape categories, but not of texture or color categories, compared with massed presentations. In addition, generalized estimating equations analyses revealed positive relations between the size of children's "shape-side" productive vocabularies and their shape category learning and between the size of children's "against-the-system" productive vocabularies and their texture category learning. These results suggest that children's attention to and memory for novel object categories are strongly related to their individual word-learning histories. Moreover, children's learned attentional biases affected the types of categories for which spacing facilitated learning. These findings highlight the importance of considering how learners' previous experiences may influence future learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Procedural-Based Category Learning in Patients with Parkinson's Disease: Impact of Category Number and Category Continuity

Directory of Open Access Journals (Sweden)

J. Vincent eFiloteo

2014-02-01

Full Text Available Previously we found that Parkinson's disease (PD patients are impaired in procedural-based category learning when category membership is defined by a nonlinear relationship between stimulus dimensions, but these same patients are normal when the rule is defined by a linear relationship (Filoteo et al., 2005; Maddox & Filoteo, 2001. We suggested that PD patients' impairment was due to a deficit in recruiting ‘striatal units' to represent complex nonlinear rules. In the present study, we further examined the nature of PD patients' procedural-based deficit in two experiments designed to examine the impact of (1 the number of categories, and (2 category discontinuity on learning. Results indicated that PD patients were impaired only under discontinuous category conditions but were normal when the number of categories was increased from two to four. The lack of impairment in the four-category condition suggests normal integrity of striatal medium spiny cells involved in procedural-based category learning. In contrast, and consistent with our previous observation of a nonlinear deficit, the finding that PD patients were impaired in the discontinuous condition suggests that these patients are impaired when they have to associate perceptually distinct exemplars with the same category. Theoretically, this deficit might be related to dysfunctional communication among medium spiny neurons within the striatum, particularly given that these are cholinergic neurons and a cholinergic deficiency could underlie some of PD patients’ cognitive impairment.

When does fading enhance perceptual category learning?

Science.gov (United States)

Pashler, Harold; Mozer, Michael C

2013-07-01

Training that uses exaggerated versions of a stimulus discrimination (fading) has sometimes been found to enhance category learning, mostly in studies involving animals and impaired populations. However, little is known about whether and when fading facilitates learning for typical individuals. This issue was explored in 7 experiments. In Experiments 1 and 2, observers discriminated stimuli based on a single sensory continuum (time duration and line length, respectively). Adaptive fading dramatically improved performance in training (unsurprisingly) but did not enhance learning as assessed in a final test. The same was true for nonadaptive linear fading (Experiment 3). However, when variation in length (predicting category membership) was embedded among other (category-irrelevant) variation, fading dramatically enhanced not only performance in training but also learning as assessed in a final test (Experiments 4 and 5). Fading also helped learners to acquire a color saturation discrimination amid category-irrelevant variation in hue and brightness, although this learning proved transitory after feedback was withdrawn (Experiment 7). Theoretical implications are discussed, and we argue that fading should have practical utility in naturalistic category learning tasks, which involve extremely high dimensional stimuli and many irrelevant dimensions. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Classification versus inference learning contrasted with real-world categories.

Science.gov (United States)

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Dissociation of Category-Learning Systems via Brain Potentials

Directory of Open Access Journals (Sweden)

Robert G Morrison

2015-07-01

Full Text Available Behavioral, neuropsychological, and neuroimaging evidence has suggested that categories can often be learned via either an explicit rule-based mechanism critically dependent on medial temporal and prefrontal brain regions, or via an implicit information-integration mechanism relying on the basal ganglia. In this study, participants viewed sine-wave gratings (i.e., Gabor patches that varied on two dimensions and learned to categorize them via trial-by-trial feedback. Two different stimulus distributions were used; one was intended to encourage an explicit rule-based process and the other an implicit information-integration process. We monitored brain activity with scalp electroencephalography (EEG while each participant (1 passively observed stimuli represented of both distributions, (2 categorized stimuli from one distribution, and, one week later, (3 categorized stimuli from the other distribution. Categorization accuracy was similar for the two distributions. Subtractions of Event-Related Potentials (ERPs for correct and incorrect trials were used to identify neural differences in rule-based and information-integration categorization processes. We identified an occipital brain potential that was differentially modulated by categorization condition accuracy at an early latency (150 - 250 ms, likely reflecting the degree of holistic processing. A stimulus-locked late positive complex associated with explicit memory updating was modulated by accuracy in the rule-based, but not the information-integration task. Likewise, a feedback-locked P300 ERP associated with expectancy was correlated with performance only in the rule-based, but not the information-integration condition. These results provide additional evidence for distinct brain mechanisms supporting rule-based versus implicit information-integration category learning and use.

The impact of category structure and training methodology on learning and generalizing within-category representations.

Science.gov (United States)

Ell, Shawn W; Smith, David B; Peralta, Gabriela; Hélie, Sébastien

2017-08-01

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

Reward-related learning via multiple memory systems.

Science.gov (United States)

Delgado, Mauricio R; Dickerson, Kathryn C

2012-07-15

The application of a neuroeconomic approach to the study of reward-related processes has provided significant insights in our understanding of human learning and decision making. Much of this research has focused primarily on the contributions of the corticostriatal circuitry, involved in trial-and-error reward learning. As a result, less consideration has been allotted to the potential influence of different neural mechanisms such as the hippocampus or to more common ways in human society in which information is acquired and utilized to reach a decision, such as through explicit instruction rather than trial-and-error learning. This review examines the individual contributions of multiple learning and memory neural systems and their interactions during human decision making in both normal and neuropsychiatric populations. Specifically, the anatomical and functional connectivity across multiple memory systems are highlighted to suggest that probing the role of the hippocampus and its interactions with the corticostriatal circuitry via the application of model-based neuroeconomic approaches may provide novel insights into neuropsychiatric populations that suffer from damage to one of these structures and as a consequence have deficits in learning, memory, or decision making. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

Directory of Open Access Journals (Sweden)

Akira Taniguchi

2017-12-01

Full Text Available In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color. This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.

Automatic plankton image classification combining multiple view features via multiple kernel learning.

Science.gov (United States)

Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

2017-12-28

Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

The Role of Age and Executive Function in Auditory Category Learning

Science.gov (United States)

Reetzke, Rachel; Maddox, W. Todd; Chandrasekaran, Bharath

2015-01-01

Auditory categorization is a natural and adaptive process that allows for the organization of high-dimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesis-testing process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study is two-fold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence, into early adulthood; and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. Sixty participants with normal hearing, 20 children (age range, 7–12), 21 adolescents (age range, 13–19), and 19 young adults (age range, 20–23), learned to categorize novel dynamic ripple sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied Gabor patches in the visual domain. Results revealed that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e. a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning. PMID:26491987

The development of automaticity in short-term memory search: Item-response learning and category learning.

Science.gov (United States)

Cao, Rui; Nosofsky, Robert M; Shiffrin, Richard M

2017-05-01

In short-term-memory (STM)-search tasks, observers judge whether a test probe was present in a short list of study items. Here we investigated the long-term learning mechanisms that lead to the highly efficient STM-search performance observed under conditions of consistent-mapping (CM) training, in which targets and foils never switch roles across trials. In item-response learning, subjects learn long-term mappings between individual items and target versus foil responses. In category learning, subjects learn high-level codes corresponding to separate sets of items and learn to attach old versus new responses to these category codes. To distinguish between these 2 forms of learning, we tested subjects in categorized varied mapping (CV) conditions: There were 2 distinct categories of items, but the assignment of categories to target versus foil responses varied across trials. In cases involving arbitrary categories, CV performance closely resembled standard varied-mapping performance without categories and departed dramatically from CM performance, supporting the item-response-learning hypothesis. In cases involving prelearned categories, CV performance resembled CM performance, as long as there was sufficient practice or steps taken to reduce trial-to-trial category-switching costs. This pattern of results supports the category-coding hypothesis for sufficiently well-learned categories. Thus, item-response learning occurs rapidly and is used early in CM training; category learning is much slower but is eventually adopted and is used to increase the efficiency of search beyond that available from item-response learning. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Transcranial infrared laser stimulation improves rule-based, but not information-integration, category learning in humans.

Science.gov (United States)

Blanco, Nathaniel J; Saucedo, Celeste L; Gonzalez-Lima, F

2017-03-01

This is the first randomized, controlled study comparing the cognitive effects of transcranial laser stimulation on category learning tasks. Transcranial infrared laser stimulation is a new non-invasive form of brain stimulation that shows promise for wide-ranging experimental and neuropsychological applications. It involves using infrared laser to enhance cerebral oxygenation and energy metabolism through upregulation of the respiratory enzyme cytochrome oxidase, the primary infrared photon acceptor in cells. Previous research found that transcranial infrared laser stimulation aimed at the prefrontal cortex can improve sustained attention, short-term memory, and executive function. In this study, we directly investigated the influence of transcranial infrared laser stimulation on two neurobiologically dissociable systems of category learning: a prefrontal cortex mediated reflective system that learns categories using explicit rules, and a striatally mediated reflexive learning system that forms gradual stimulus-response associations. Participants (n=118) received either active infrared laser to the lateral prefrontal cortex or sham (placebo) stimulation, and then learned one of two category structures-a rule-based structure optimally learned by the reflective system, or an information-integration structure optimally learned by the reflexive system. We found that prefrontal rule-based learning was substantially improved following transcranial infrared laser stimulation as compared to placebo (treatment X block interaction: F(1, 298)=5.117, p=0.024), while information-integration learning did not show significant group differences (treatment X block interaction: F(1, 288)=1.633, p=0.202). These results highlight the exciting potential of transcranial infrared laser stimulation for cognitive enhancement and provide insight into the neurobiological underpinnings of category learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Chromatic Perceptual Learning but No Category Effects without Linguistic Input.

Science.gov (United States)

Grandison, Alexandra; Sowden, Paul T; Drivonikou, Vicky G; Notman, Leslie A; Alexander, Iona; Davies, Ian R L

2016-01-01

Perceptual learning involves an improvement in perceptual judgment with practice, which is often specific to stimulus or task factors. Perceptual learning has been shown on a range of visual tasks but very little research has explored chromatic perceptual learning. Here, we use two low level perceptual threshold tasks and a supra-threshold target detection task to assess chromatic perceptual learning and category effects. Experiment 1 investigates whether chromatic thresholds reduce as a result of training and at what level of analysis learning effects occur. Experiment 2 explores the effect of category training on chromatic thresholds, whether training of this nature is category specific and whether it can induce categorical responding. Experiment 3 investigates the effect of category training on a higher level, lateralized target detection task, previously found to be sensitive to category effects. The findings indicate that performance on a perceptual threshold task improves following training but improvements do not transfer across retinal location or hue. Therefore, chromatic perceptual learning is category specific and can occur at relatively early stages of visual analysis. Additionally, category training does not induce category effects on a low level perceptual threshold task, as indicated by comparable discrimination thresholds at the newly learned hue boundary and adjacent test points. However, category training does induce emerging category effects on a supra-threshold target detection task. Whilst chromatic perceptual learning is possible, learnt category effects appear to be a product of left hemisphere processing, and may require the input of higher level linguistic coding processes in order to manifest.

Multiple systems for motor skill learning.

Science.gov (United States)

Clark, Dav; Ivry, Richard B

2010-07-01

Motor learning is a ubiquitous feature of human competence. This review focuses on two particular classes of model tasks for studying skill acquisition. The serial reaction time (SRT) task is used to probe how people learn sequences of actions, while adaptation in the context of visuomotor or force field perturbations serves to illustrate how preexisting movements are recalibrated in novel environments. These tasks highlight important issues regarding the representational changes that occur during the course of motor learning. One important theme is that distinct mechanisms vary in their information processing costs during learning and performance. Fast learning processes may require few trials to produce large changes in performance but impose demands on cognitive resources. Slower processes are limited in their ability to integrate complex information but minimally demanding in terms of attention or processing resources. The representations derived from fast systems may be accessible to conscious processing and provide a relatively greater measure of flexibility, while the representations derived from slower systems are more inflexible and automatic in their behavior. In exploring these issues, we focus on how multiple neural systems may interact and compete during the acquisition and consolidation of new behaviors. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Psychology > Motor Skill and Performance. Copyright © 2010 John Wiley & Sons, Ltd.

Emergence of category-level sensitivities in non-native speech sound learning

Directory of Open Access Journals (Sweden)

Emily eMyers

2014-08-01

Full Text Available Over the course of development, speech sounds that are contrastive in one’s native language tend to become perceived categorically: that is, listeners are unaware of variation within phonetic categories while showing excellent sensitivity to speech sounds that span linguistically meaningful phonetic category boundaries. The end stage of this developmental process is that the perceptual systems that handle acoustic-phonetic information show special tuning to native language contrasts, and as such, category-level information appears to be present at even fairly low levels of the neural processing stream. Research on adults acquiring non-native speech categories offers an avenue for investigating the interplay of category-level information and perceptual sensitivities to these sounds as speech categories emerge. In particular, one can observe the neural changes that unfold as listeners learn not only to perceive acoustic distinctions that mark non-native speech sound contrasts, but also to map these distinctions onto category-level representations. An emergent literature on the neural basis of novel and non-native speech sound learning offers new insight into this question. In this review, I will examine this literature in order to answer two key questions. First, where in the neural pathway does sensitivity to category-level phonetic information first emerge over the trajectory of speech sound learning? Second, how do frontal and temporal brain areas work in concert over the course of non-native speech sound learning? Finally, in the context of this literature I will describe a model of speech sound learning in which rapidly-adapting access to categorical information in the frontal lobes modulates the sensitivity of stable, slowly-adapting responses in the temporal lobes.

Individual differences in attention during category learning

NARCIS (Netherlands)

Lee, M.D.; Wetzels, R.

2010-01-01

A central idea in many successful models of category learning—including the Generalized Context Model (GCM)—is that people selectively attend to those dimensions of stimuli that are relevant for dividing them into categories. We use the GCM to re-examine some previously analyzed category learning

A Bulk Microphysics Parameterization with Multiple Ice Precipitation Categories.

Science.gov (United States)

Straka, Jerry M.; Mansell, Edward R.

2005-04-01

A single-moment bulk microphysics scheme with multiple ice precipitation categories is described. It has 2 liquid hydrometeor categories (cloud droplets and rain) and 10 ice categories that are characterized by habit, size, and density—two ice crystal habits (column and plate), rimed cloud ice, snow (ice crystal aggregates), three categories of graupel with different densities and intercepts, frozen drops, small hail, and large hail. The concept of riming history is implemented for conversions among the graupel and frozen drops categories. The multiple precipitation ice categories allow a range of particle densities and fall velocities for simulating a variety of convective storms with minimal parameter tuning. The scheme is applied to two cases—an idealized continental multicell storm that demonstrates the ice precipitation process, and a small Florida maritime storm in which the warm rain process is important.

Incremental Learning of Perceptual Categories for Open-Domain Sketch Recognition

National Research Council Canada - National Science Library

Lovett, Andrew; Dehghani, Morteza; Forbus, Kenneth

2007-01-01

.... This paper describes an incremental learning technique for opendomain recognition. Our system builds generalizations for categories of objects based upon previous sketches of those objects and uses those generalizations to classify new sketches...

The helpfulness of category labels in semi-supervised learning depends on category structure.

Science.gov (United States)

Vong, Wai Keen; Navarro, Daniel J; Perfors, Amy

2016-02-01

The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a benefit to unlabeled information and sometimes not. In this paper, we frame the problem differently, focusing on when labels might be helpful to a learner who has access to lots of unlabeled information. Using an unconstrained free-sorting categorization experiment, we show that labels are useful to participants only when the category structure is ambiguous and that people's responses are driven by the specific set of labels they see. We present an extension of Anderson's Rational Model of Categorization that captures this effect.

Incidental Learning of Sound Categories is Impaired in Developmental Dyslexia

Science.gov (United States)

Gabay, Yafit; Holt, Lori L.

2015-01-01

Developmental dyslexia is commonly thought to arise from specific phonological impairments. However, recent evidence is consistent with the possibility that phonological impairments arise as symptoms of an underlying dysfunction of procedural learning. The nature of the link between impaired procedural learning and phonological dysfunction is unresolved. Motivated by the observation that speech processing involves the acquisition of procedural category knowledge, the present study investigates the possibility that procedural learning impairment may affect phonological processing by interfering with the typical course of phonetic category learning. The present study tests this hypothesis while controlling for linguistic experience and possible speech-specific deficits by comparing auditory category learning across artificial, nonlinguistic sounds among dyslexic adults and matched controls in a specialized first-person shooter videogame that has been shown to engage procedural learning. Nonspeech auditory category learning was assessed online via within-game measures and also with a post-training task involving overt categorization of familiar and novel sound exemplars. Each measure reveals that dyslexic participants do not acquire procedural category knowledge as effectively as age- and cognitive-ability matched controls. This difference cannot be explained by differences in perceptual acuity for the sounds. Moreover, poor nonspeech category learning is associated with slower phonological processing. Whereas phonological processing impairments have been emphasized as the cause of dyslexia, the current results suggest that impaired auditory category learning, general in nature and not specific to speech signals, could contribute to phonological deficits in dyslexia with subsequent negative effects on language acquisition and reading. Implications for the neuro-cognitive mechanisms of developmental dyslexia are discussed. PMID:26409017

The perceptual effects of learning object categories that predict perceptual goals

Science.gov (United States)

Van Gulick, Ana E.; Gauthier, Isabel

2014-01-01

In classic category learning studies, subjects typically learn to assign items to one of two categories, with no further distinction between how items on each side of the category boundary should be treated. In real life, however, we often learn categories that dictate further processing goals, for instance with objects in only one category requiring further individuation. Using methods from category learning and perceptual expertise, we studied the perceptual consequences of experience with objects in tasks that rely on attention to different dimensions in different parts of the space. In two experiments, subjects first learned to categorize complex objects from a single morphspace into two categories based on one morph dimension, and then learned to perform a different task, either naming or a local feature judgment, for each of the two categories. A same-different discrimination test before and after each training measured sensitivity to feature dimensions of the space. After initial categorization, sensitivity increased along the category-diagnostic dimension. After task association, sensitivity increased more for the category that was named, especially along the non-diagnostic dimension. The results demonstrate that local attentional weights, associated with individual exemplars as a function of task requirements, can have lasting effects on perceptual representations. PMID:24820671

Learning and transfer of category knowledge in an indirect categorization task.

Science.gov (United States)

Helie, Sebastien; Ashby, F Gregory

2012-05-01

Knowledge representations acquired during category learning experiments are 'tuned' to the task goal. A useful paradigm to study category representations is indirect category learning. In the present article, we propose a new indirect categorization task called the "same"-"different" categorization task. The same-different categorization task is a regular same-different task, but the question asked to the participants is about the stimulus category membership instead of stimulus identity. Experiment 1 explores the possibility of indirectly learning rule-based and information-integration category structures using the new paradigm. The results suggest that there is little learning about the category structures resulting from an indirect categorization task unless the categories can be separated by a one-dimensional rule. Experiment 2 explores whether a category representation learned indirectly can be used in a direct classification task (and vice versa). The results suggest that previous categorical knowledge acquired during a direct classification task can be expressed in the same-different categorization task only when the categories can be separated by a rule that is easily verbalized. Implications of these results for categorization research are discussed.

Incidental learning of sound categories is impaired in developmental dyslexia.

Science.gov (United States)

Gabay, Yafit; Holt, Lori L

2015-12-01

Developmental dyslexia is commonly thought to arise from specific phonological impairments. However, recent evidence is consistent with the possibility that phonological impairments arise as symptoms of an underlying dysfunction of procedural learning. The nature of the link between impaired procedural learning and phonological dysfunction is unresolved. Motivated by the observation that speech processing involves the acquisition of procedural category knowledge, the present study investigates the possibility that procedural learning impairment may affect phonological processing by interfering with the typical course of phonetic category learning. The present study tests this hypothesis while controlling for linguistic experience and possible speech-specific deficits by comparing auditory category learning across artificial, nonlinguistic sounds among dyslexic adults and matched controls in a specialized first-person shooter videogame that has been shown to engage procedural learning. Nonspeech auditory category learning was assessed online via within-game measures and also with a post-training task involving overt categorization of familiar and novel sound exemplars. Each measure reveals that dyslexic participants do not acquire procedural category knowledge as effectively as age- and cognitive-ability matched controls. This difference cannot be explained by differences in perceptual acuity for the sounds. Moreover, poor nonspeech category learning is associated with slower phonological processing. Whereas phonological processing impairments have been emphasized as the cause of dyslexia, the current results suggest that impaired auditory category learning, general in nature and not specific to speech signals, could contribute to phonological deficits in dyslexia with subsequent negative effects on language acquisition and reading. Implications for the neuro-cognitive mechanisms of developmental dyslexia are discussed. Copyright © 2015 Elsevier Ltd. All rights

The contribution of temporary storage and executive processes to category learning.

Science.gov (United States)

Wang, Tengfei; Ren, Xuezhu; Schweizer, Karl

2015-09-01

Three distinctly different working memory processes, temporary storage, mental shifting and inhibition, were proposed to account for individual differences in category learning. A sample of 213 participants completed a classic category learning task and two working memory tasks that were experimentally manipulated for tapping specific working memory processes. Fixed-links models were used to decompose data of the category learning task into two independent components representing basic performance and improvement in performance in category learning. Processes of working memory were also represented by fixed-links models. In a next step the three working memory processes were linked to components of category learning. Results from modeling analyses indicated that temporary storage had a significant effect on basic performance and shifting had a moderate effect on improvement in performance. In contrast, inhibition showed no effect on any component of the category learning task. These results suggest that temporary storage and the shifting process play different roles in the course of acquiring new categories. Copyright © 2015 Elsevier B.V. All rights reserved.

Learning about the internal structure of categories through classification and feature inference.

Science.gov (United States)

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Young children’s learning of relational categories:multiple comparisons and their cognitive constraints

Directory of Open Access Journals (Sweden)

Jean-Pierre eThibaut

2015-05-01

Full Text Available Relational categories are notoriously difficult to learn because they are not defined by intrinsic stable properties. We studied the impact of comparisons on relational concept learning with a novel word learning task in 42-month-old children. Capitalizing on Gentner et al. (2011, two, three or four pairs of stimuli were introduced with a novel relational word. In a given trial, the set of pairs was composed of either close or far pairs (e.g., close pair: knife1-watermelon, knife2-orange, knife3-slice of bread and knife4-meat; far pair: ax-evergreen tree, saw-log, cutter-cardboard and knife-slice of bread, for the cutter for relation. Close pairs (2 vs. 3 vs. 4 pairs led to random generalizations whereas comparisons with far pairs gave the expected relational generalization. The 3 pair case gave the best results. It is argued that far pairs promote deeper comparisons than close pairs. As shown by a control experiment, this was the case only when far pairs display well known associations.

When more is less: Feedback effects in perceptual category learning ☆

Science.gov (United States)

Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

2008-01-01

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed. PMID:18455155

Can Semi-Supervised Learning Explain Incorrect Beliefs about Categories?

Science.gov (United States)

Kalish, Charles W.; Rogers, Timothy T.; Lang, Jonathan; Zhu, Xiaojin

2011-01-01

Three experiments with 88 college-aged participants explored how unlabeled experiences--learning episodes in which people encounter objects without information about their category membership--influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then…

On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.

Science.gov (United States)

Nosofsky, Robert M; Sanders, Craig A; Gerdom, Alex; Douglas, Bruce J; McDaniel, Mark A

2017-01-01

The general view in psychological science is that natural categories obey a coherent, family-resemblance principle. In this investigation, we documented an example of an important exception to this principle: Results of a multidimensional-scaling study of igneous, metamorphic, and sedimentary rocks (Experiment 1) suggested that the structure of these categories is disorganized and dispersed. This finding motivated us to explore what might be the optimal procedures for teaching dispersed categories, a goal that is likely critical to science education in general. Subjects in Experiment 2 learned to classify pictures of rocks into compact or dispersed high-level categories. One group learned the categories through focused high-level training, whereas a second group was required to simultaneously learn classifications at a subtype level. Although high-level training led to enhanced performance when the categories were compact, subtype training was better when the categories were dispersed. We provide an interpretation of the results in terms of an exemplar-memory model of category learning.

Adaptive learning in a compartmental model of visual cortex - how feedback enables stable category learning and refinement

Directory of Open Access Journals (Sweden)

Georg eLayher

2014-12-01

Full Text Available The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, but both belong to the category of felines. In other words, tigers and leopards are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in the computational neurosciences. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of (sub- category representations. We demonstrate the temporal evolution of such learning and show how the approach successully establishes category and subcategory

Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

Science.gov (United States)

Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

2014-01-01

The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

Science.gov (United States)

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

The Role of Feedback Contingency in Perceptual Category Learning

Science.gov (United States)

Ashby, F. Gregory; Vucovich, Lauren E.

2016-01-01

Feedback is highly contingent on behavior if it eventually becomes easy to predict, and weakly contingent on behavior if it remains difficult or impossible to predict even after learning is complete. Many studies have demonstrated that humans and nonhuman animals are highly sensitive to feedback contingency, but no known studies have examined how feedback contingency affects category learning, and current theories assign little or no importance to this variable. Two experiments examined the effects of contingency degradation on rule-based and information-integration category learning. In rule-based tasks, optimal accuracy is possible with a simple explicit rule, whereas optimal accuracy in information-integration tasks requires integrating information from two or more incommensurable perceptual dimensions. In both experiments, participants each learned rule-based or information-integration categories under either high or low levels of feedback contingency. The exact same stimuli were used in all four conditions and optimal accuracy was identical in every condition. Learning was good in both high-contingency conditions, but most participants showed little or no evidence of learning in either low-contingency condition. Possible causes of these effects are discussed, as well as their theoretical implications. PMID:27149393

More Is Generally Better: Higher Working Memory Capacity Does Not Impair Perceptual Category Learning

Science.gov (United States)

Kalish, Michael L.; Newell, Ben R.; Dunn, John C.

2017-01-01

It is sometimes supposed that category learning involves competing explicit and procedural systems, with only the former reliant on working memory capacity (WMC). In 2 experiments participants were trained for 3 blocks on both filtering (often said to be learned explicitly) and condensation (often said to be learned procedurally) category…

Linguistic labels, dynamic visual features, and attention in infant category learning.

Science.gov (United States)

Deng, Wei Sophia; Sloutsky, Vladimir M

2015-06-01

How do words affect categorization? According to some accounts, even early in development words are category markers and are different from other features. According to other accounts, early in development words are part of the input and are akin to other features. The current study addressed this issue by examining the role of words and dynamic visual features in category learning in 8- to 12-month-old infants. Infants were familiarized with exemplars from one category in a label-defined or motion-defined condition and then tested with prototypes from the studied category and from a novel contrast category. Eye-tracking results indicated that infants exhibited better category learning in the motion-defined condition than in the label-defined condition, and their attention was more distributed among different features when there was a dynamic visual feature compared with the label-defined condition. These results provide little evidence for the idea that linguistic labels are category markers that facilitate category learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Compensatory Processing During Rule-Based Category Learning in Older Adults

Science.gov (United States)

Bharani, Krishna L.; Paller, Ken A.; Reber, Paul J.; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G.

2016-01-01

Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex. PMID:26422522

Rich in vitamin C or just a convenient snack? Multiple-category reasoning with cross-classified foods.

Science.gov (United States)

Hayes, Brett K; Kurniawan, Hendy; Newell, Ben R

2011-01-01

Two studies examined multiple category reasoning in property induction with cross-classified foods. Pilot tests identified foods that were more typical of a taxonomic category (e.g., "fruit"; termed 'taxonomic primary') or a script based category (e.g., "snack foods"; termed 'script primary'). They also confirmed that taxonomic categories were perceived as more coherent than script categories. In Experiment 1 participants completed an induction task in which information from multiple categories could be searched and combined to generate a property prediction about a target food. Multiple categories were more often consulted and used in prediction for script primary than for taxonomic primary foods. Experiment 2 replicated this finding across a range of property types but found that multiple category reasoning was reduced in the presence of a concurrent cognitive load. Property type affected which categories were consulted first and how information from multiple categories was weighted. The results show that multiple categories are more likely to be used for property predictions about cross-classified objects when an object is primarily associated with a category that has low coherence.

Criterial noise effects on rule-based category learning: the impact of delayed feedback.

Science.gov (United States)

Ell, Shawn W; Ing, A David; Maddox, W Todd

2009-08-01

Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate. In Experiment 1, we examined the effect of drift by inserting a 5-sec delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. In Experiment 2, we built on a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. In Experiment 3, we confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory, as well as providing important constraints for models of rule-based category learning.

Relative risk of probabilistic category learning deficits in patients with schizophrenia and their siblings

Science.gov (United States)

Weickert, Thomas W.; Goldberg, Terry E.; Egan, Michael F.; Apud, Jose A.; Meeter, Martijn; Myers, Catherine E.; Gluck, Mark A; Weinberger, Daniel R.

2010-01-01

Background While patients with schizophrenia display an overall probabilistic category learning performance deficit, the extent to which this deficit occurs in unaffected siblings of patients with schizophrenia is unknown. There are also discrepant findings regarding probabilistic category learning acquisition rate and performance in patients with schizophrenia. Methods A probabilistic category learning test was administered to 108 patients with schizophrenia, 82 unaffected siblings, and 121 healthy participants. Results Patients with schizophrenia displayed significant differences from their unaffected siblings and healthy participants with respect to probabilistic category learning acquisition rates. Although siblings on the whole failed to differ from healthy participants on strategy and quantitative indices of overall performance and learning acquisition, application of a revised learning criterion enabling classification into good and poor learners based on individual learning curves revealed significant differences between percentages of sibling and healthy poor learners: healthy (13.2%), siblings (34.1%), patients (48.1%), yielding a moderate relative risk. Conclusions These results clarify previous discrepant findings pertaining to probabilistic category learning acquisition rate in schizophrenia and provide the first evidence for the relative risk of probabilistic category learning abnormalities in unaffected siblings of patients with schizophrenia, supporting genetic underpinnings of probabilistic category learning deficits in schizophrenia. These findings also raise questions regarding the contribution of antipsychotic medication to the probabilistic category learning deficit in schizophrenia. The distinction between good and poor learning may be used to inform genetic studies designed to detect schizophrenia risk alleles. PMID:20172502

Large-scale weakly supervised object localization via latent category learning.

Science.gov (United States)

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

Comparing the effects of positive and negative feedback in information-integration category learning.

Science.gov (United States)

Freedberg, Michael; Glass, Brian; Filoteo, J Vincent; Hazeltine, Eliot; Maddox, W Todd

2017-01-01

Categorical learning is dependent on feedback. Here, we compare how positive and negative feedback affect information-integration (II) category learning. Ashby and O'Brien (2007) demonstrated that both positive and negative feedback are required to solve II category problems when feedback was not guaranteed on each trial, and reported no differences between positive-only and negative-only feedback in terms of their effectiveness. We followed up on these findings and conducted 3 experiments in which participants completed 2,400 II categorization trials across three days under 1 of 3 conditions: positive feedback only (PFB), negative feedback only (NFB), or both types of feedback (CP; control partial). An adaptive algorithm controlled the amount of feedback given to each group so that feedback was nearly equated. Using different feedback control procedures, Experiments 1 and 2 demonstrated that participants in the NFB and CP group were able to engage II learning strategies, whereas the PFB group was not. Additionally, the NFB group was able to achieve significantly higher accuracy than the PFB group by Day 3. Experiment 3 revealed that these differences remained even when we equated the information received on feedback trials. Thus, negative feedback appears significantly more effective for learning II category structures. This suggests that the human implicit learning system may be capable of learning in the absence of positive feedback.

Category Learning Research in the Interactive Online Environment Second Life

Science.gov (United States)

Andrews, Jan; Livingston, Ken; Sturm, Joshua; Bliss, Daniel; Hawthorne, Daniel

2011-01-01

The interactive online environment Second Life allows users to create novel three-dimensional stimuli that can be manipulated in a meaningful yet controlled environment. These features suggest Second Life's utility as a powerful tool for investigating how people learn concepts for unfamiliar objects. The first of two studies was designed to establish that cognitive processes elicited in this virtual world are comparable to those tapped in conventional settings by attempting to replicate the established finding that category learning systematically influences perceived similarity . From the perspective of an avatar, participants navigated a course of unfamiliar three-dimensional stimuli and were trained to classify them into two labeled categories based on two visual features. Participants then gave similarity ratings for pairs of stimuli and their responses were compared to those of control participants who did not learn the categories. Results indicated significant compression, whereby objects classified together were judged to be more similar by learning than control participants, thus supporting the validity of using Second Life as a laboratory for studying human cognition. A second study used Second Life to test the novel hypothesis that effects of learning on perceived similarity do not depend on the presence of verbal labels for categories. We presented the same stimuli but participants classified them by selecting between two complex visual patterns designed to be extremely difficult to label. While learning was more challenging in this condition , those who did learn without labels showed a compression effect identical to that found in the first study using verbal labels. Together these studies establish that at least some forms of human learning in Second Life parallel learning in the actual world and thus open the door to future studies that will make greater use of the enriched variety of objects and interactions possible in simulated environments

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

Science.gov (United States)

Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

2014-02-01

This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

Binocular Fusion and Invariant Category Learning due to Predictive Remapping during Scanning of a Depthful Scene with Eye Movements

Directory of Open Access Journals (Sweden)

Stephen eGrossberg

2015-01-01

Full Text Available How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object’s surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements

Science.gov (United States)

Grossberg, Stephen; Srinivasan, Karthik; Yazdanbakhsh, Arash

2015-01-01

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations. PMID:25642198

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements.

Science.gov (United States)

Grossberg, Stephen; Srinivasan, Karthik; Yazdanbakhsh, Arash

2014-01-01

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.

Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.

Science.gov (United States)

Cantwell, George; Riesenhuber, Maximilian; Roeder, Jessica L; Ashby, F Gregory

2017-05-01

The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Attribute conjunctions and the part configuration advantage in object category learning.

Science.gov (United States)

Saiki, J; Hummel, J E

1996-07-01

Five experiments demonstrated that in object category learning people are particularly sensitive to conjunctions of part shapes and relative locations. Participants learned categories defined by a part's shape and color (part-color conjunctions) or by a part's shape and its location relative to another part (part-location conjunctions). The statistical properties of the categories were identical across these conditions, as were the salience of color and relative location. Participants were better at classifying objects defined by part-location conjunctions than objects defined by part-color conjunctions. Subsequent experiments revealed that this effect was not due to the specific color manipulation or the role of location per se. These results suggest that the shape bias in object categorization is at least partly due to sensitivity to part-location conjunctions and suggest a new processing constraint on category learning.

Distributional learning aids linguistic category formation in school-age children.

Science.gov (United States)

Hall, Jessica; Owen VAN Horne, Amanda; Farmer, Thomas

2018-05-01

The goal of this study was to determine if typically developing children could form grammatical categories from distributional information alone. Twenty-seven children aged six to nine listened to an artificial grammar which contained strategic gaps in its distribution. At test, we compared how children rated novel sentences that fit the grammar to sentences that were ungrammatical. Sentences could be distinguished only through the formation of categories of words with shared distributional properties. Children's ratings revealed that they could discriminate grammatical and ungrammatical sentences. These data lend support to the hypothesis that distributional learning is a potential mechanism for learning grammatical categories in a first language.

Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-Based fMRI

Science.gov (United States)

Davis, Tyler; Love, Bradley C.; Preston, Alison R.

2012-01-01

Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and…

Behavioral evidence for differences in social and non-social category learning

Directory of Open Access Journals (Sweden)

Lucile eGamond

2012-08-01

Full Text Available When meeting someone for the very first time one spontaneously categorizes the seen person on the basis of his/her appearance. Categorization is based on the association between some physical features and category labels that can be social (character trait… or non-social (tall, thin. Surprisingly little is known about how such associations are formed, particularly in the social domain. Here, we aimed at testing whether social and non-social category learning may be dissociated. We presented subjects with a large number of faces that had to be rated according to social or non-social labels, and induced an association between a facial feature (inter-eye distance and the category labels using two different procedures. In a first experiment, we used a feedback procedure to reinforce the association; behavioral measures revealed an association between the physical feature manipulated and abstract non-social categories, while no evidence for an association with social labels could be found. In a second experiment, we used passive exposure to the association between physical features and labels; we obtained behavioral evidence for learning of both social and non-social categories. These results support the view of the specificity of social category learning; they suggest that social categories are best acquired through unsupervised procedures that can be considered as a simplified proxy for group transmission.

An Examination of Strategy Implementation during Abstract Nonlinguistic Category Learning in Aphasia

Science.gov (United States)

Vallila-Rohter, Sofia; Kiran, Swathi

2015-01-01

Purpose: Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method: Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases…

[Problem based learning: achievement of educational goals in the information and comprehension sub-categories of Bloom cognitive domain].

Science.gov (United States)

Montecinos, P; Rodewald, A M

1994-06-01

The aim this work was to assess and compare the achievements of medical students, subjected to problem based learning methodology. The information and comprehension categories of Bloom were tested in 17 medical students in four different occasions during the physiopathology course, using a multiple choice knowledge test. There was a significant improvement in the number of correct answers towards the end of the course. It is concluded that these medical students obtained adequate learning achievements in the information subcategory of Bloom using problem based learning methodology, during the physiopathology course.

Test of a potential link between analytic and nonanalytic category learning and automatic, effortful processing.

Science.gov (United States)

Tracy, J I; Pinsk, M; Helverson, J; Urban, G; Dietz, T; Smith, D J

2001-08-01

The link between automatic and effortful processing and nonanalytic and analytic category learning was evaluated in a sample of 29 college undergraduates using declarative memory, semantic category search, and pseudoword categorization tasks. Automatic and effortful processing measures were hypothesized to be associated with nonanalytic and analytic categorization, respectively. Results suggested that contrary to prediction strong criterion-attribute (analytic) responding on the pseudoword categorization task was associated with strong automatic, implicit memory encoding of frequency-of-occurrence information. Data are discussed in terms of the possibility that criterion-attribute category knowledge, once established, may be expressed with few attentional resources. The data indicate that attention resource requirements, even for the same stimuli and task, vary depending on the category rule system utilized. Also, the automaticity emerging from familiarity with analytic category exemplars is very different from the automaticity arising from extensive practice on a semantic category search task. The data do not support any simple mapping of analytic and nonanalytic forms of category learning onto the automatic and effortful processing dichotomy and challenge simple models of brain asymmetries for such procedures. Copyright 2001 Academic Press.

Warping similarity space in category learning by human subjects: the role of task difficulty

OpenAIRE

Pevtzow, Rachel; Harnad, Stevan

1997-01-01

In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with regions of increased within-category similarity (compression) and regions of reduced between-category similarity (separation) enh ancing the category boundaries and making categorisation reliable and all-or-none rather than graded. We show that category learning can likewise warp similarity space, resolving uncertainty near category boundaries. Two Hard and two Easy texture learning tasks were ...

The effect of category learning on attentional modulation of visual cortex.

Science.gov (United States)

Folstein, Jonathan R; Fuller, Kelly; Howard, Dorothy; DePatie, Thomas

2017-09-01

Learning about visual object categories causes changes in the way we perceive those objects. One likely mechanism by which this occurs is the application of attention to potentially relevant objects. Here we test the hypothesis that category membership influences the allocation of attention, allowing attention to be applied not only to object features, but to entire categories. Participants briefly learned to categorize a set of novel cartoon animals after which EEG was recorded while participants distinguished between a target and non-target category. A second identical EEG session was conducted after two sessions of categorization practice. The category structure and task design allowed parametric manipulation of number of target features while holding feature frequency and category membership constant. We found no evidence that category membership influenced attentional selection: a postero-lateral negative component, labeled the selection negativity/N250, increased over time and was sensitive to number of target features, not target categories. In contrast, the right hemisphere N170 was not sensitive to target features. The P300 appeared sensitive to category in the first session, but showed a graded sensitivity to number of target features in the second session, possibly suggesting a transition from rule-based to similarity based categorization. Copyright © 2017. Published by Elsevier Ltd.

An Intrinsic Value System for Developing Multiple Invariant Representations with Incremental Slowness Learning

Directory of Open Access Journals (Sweden)

Matthew David Luciw

2013-05-01

Full Text Available Curiosity Driven Modular Incremental Slow Feature Analysis (CD-MISFA;~cite{cdmisfa} is a recently introduced model of intrinsically-motivated invariance learning, which shows how curiosity enables the orderly formation of multiple stable sensory representations, through which the agent can simplify its complex sensory input. Here, we first discuss the computational properties of the CD-MISFA model itself, followed by a discussion of neurophysiological analogs fulfilling similar functional roles. CD-MISFA combines 1. unsupervised representation learning through the slowness principle, 2. generation of an intrinsic reward signal through the learning progress of the developing features, and 3. balancing of exploration and exploitation in order to maximize learning progress and quickly learn multiple feature sets for perceptual simplification. Experimental results on synthetic observations and on the iCub robot show that the intrinsic value system is an essential component to representation learning, further, the model explores such that the representations are typically learned in order from least to most costly, as predicted by the theory of Artificial Curiosity.

Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.

Science.gov (United States)

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.

Concurrent Dynamics of Category Learning and Metacognitive Judgments

Directory of Open Access Journals (Sweden)

Valnea Žauhar

2016-09-01

Full Text Available In two experiments, we examined the correspondence between the dynamics of metacognitive judgments and classification accuracy when participants were asked to learn category structures of different levels of complexity, i.e., to learn tasks of types I, II and III according to Shepard, Hovland, and Jenkins (1961. The stimuli were simple geometrical figures varying in the following three dimensions: color, shape, and size. In Experiment 1, we found moderate positive correlations between confidence and accuracy in task type II and weaker correlation in task type I and III. Moreover, the trend analysis in the backward learning curves revealed that there is a non-linear trend in accuracy for all three task types, but the same trend was observed in confidence for the task type I and II but not for task type III. In Experiment 2, we found that the feeling-of-warmth judgments (FOWs showed moderate positive correlation with accuracy in all task types. Trend analysis revealed a similar non-linear component in accuracy and metacognitive judgments in task type II and III but not in task type I. Our results suggest that FOWs are a more sensitive measure of the progress of learning than confidence because FOWs capture global knowledge about the category structure, while confidence judgments are given at the level of an individual exemplar.

A deep learning method for classifying mammographic breast density categories.

Science.gov (United States)

Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

2018-01-01

Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples

Facilitating Multiple Intelligences Through Multimodal Learning Analytics

Directory of Open Access Journals (Sweden)

Ayesha PERVEEN

2018-01-01

Full Text Available This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner’s 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as multiple intelligences by online education systems and then suggests a framework of the advanced form of learning analytics i.e., multimodal learning analytics for tracing and facilitating multiple intelligences while they are engaged in online ubiquitous learning. As multimodal learning analytics is still an evolving area, it poses many challenges for technologists, educationists as well as organizational managers. Learning analytics make machines meet humans, therefore, the educationists with an expertise in learning theories can help technologists devise latest technological methods for multimodal learning analytics and organizational managers can implement them for the improvement of online education. Therefore, a careful instructional design based on a deep understanding of students’ learning abilities, is required to develop teaching plans and technological possibilities for monitoring students’ learning paths. This is how learning analytics can help design an adaptive instructional design based on a quick analysis of the data gathered. Based on that analysis, the academicians can critically reflect upon the quick or delayed implementation of the existing instructional design based on students’ cognitive abilities or even about the single or double loop learning design. The researcher concludes that the online education is multimodal in nature, has the capacity to endorse multiliteracies and, therefore, multiple intelligences can be tracked and facilitated through multimodal learning analytics in an online mode. However, online teachers’ training both in technological implementations and

Inference and learning in sparse systems with multiple states

International Nuclear Information System (INIS)

Braunstein, A.; Ramezanpour, A.; Zhang, P.; Zecchina, R.

2011-01-01

We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

Science.gov (United States)

Zhang, Hang; Long, Zhiying; Ge, Ruiyang; Xu, Lele; Jin, Zhen; Yao, Li; Liu, Yijun

2014-01-01

Background Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning. Methodology/Principal Findings We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN. Conclusions/Significance These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning. PMID:24465577

Speeded induction under uncertainty: the influence of multiple categories and feature conjunctions.

Science.gov (United States)

Newell, Ben R; Paton, Helen; Hayes, Brett K; Griffiths, Oren

2010-12-01

When people are uncertain about the category membership of an item (e.g., Is it a dog or a dingo?), research shows that they tend to rely only on the dominant or most likely category when making inductions (e.g., How likely is it to befriend me?). An exception has been reported using speeded induction judgments where participants appeared to use information from multiple categories to make inductions (Verde, Murphy, & Ross, 2005). In two speeded induction studies, we found that participants tended to rely on the frequency with which features co-occurred when making feature predictions, independently of category membership. This pattern held whether categories were considered implicitly (Experiment 1) or explicitly (Experiment 2) prior to feature induction. The results converge with other recent work suggesting that people often rely on feature conjunction information, rather than category boundaries, when making inductions under uncertainty.

The MORPG-Based Learning System for Multiple Courses: A Case Study on Computer Science Curriculum

Science.gov (United States)

Liu, Kuo-Yu

2015-01-01

This study aimed at developing a Multiplayer Online Role Playing Game-based (MORPG) Learning system which enabled instructors to construct a game scenario and manage sharable and reusable learning content for multiple courses. It used the curriculum of "Introduction to Computer Science" as a study case to assess students' learning…

Category learning strategies in younger and older adults: Rule abstraction and memorization.

Science.gov (United States)

Wahlheim, Christopher N; McDaniel, Mark A; Little, Jeri L

2016-06-01

Despite the fundamental role of category learning in cognition, few studies have examined how this ability differs between younger and older adults. The present experiment examined possible age differences in category learning strategies and their effects on learning. Participants were trained on a category determined by a disjunctive rule applied to relational features. The utilization of rule- and exemplar-based strategies was indexed by self-reports and transfer performance. Based on self-reported strategies, the frequencies of rule- and exemplar-based learners were not significantly different between age groups, but there was a significantly higher frequency of intermediate learners (i.e., learners not identifying with a reliance on either rule- or exemplar-based strategies) in the older than younger adult group. Training performance was higher for younger than older adults regardless of the strategy utilized, showing that older adults were impaired in their ability to learn the correct rule or to remember exemplar-label associations. Transfer performance converged with strategy reports in showing higher fidelity category representations for younger adults. Younger adults with high working memory capacity were more likely to use an exemplar-based strategy, and older adults with high working memory capacity showed better training performance. Age groups did not differ in their self-reported memory beliefs, and these beliefs did not predict training strategies or performance. Overall, the present results contradict earlier findings that older adults prefer rule- to exemplar-based learning strategies, presumably to compensate for memory deficits. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control.

Directory of Open Access Journals (Sweden)

Casey Olives

Full Text Available Originally a binary classifier, Lot Quality Assurance Sampling (LQAS has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and <50%, ≥50%, and semi-curtailed sampling has been shown to effectively reduce the number of observations needed to reach a decision. To date the statistical underpinnings for Multiple Category-LQAS (MC-LQAS have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa.We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n=15 and n=25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa.Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87. In three of the studies, the kappa-statistic for a design with n=15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50, the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error.This work provides the needed analytics to understand the properties of MC-LQAS for assessing the prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools.

More than words: Adults learn probabilities over categories and relationships between them.

Science.gov (United States)

Hudson Kam, Carla L

2009-04-01

This study examines whether human learners can acquire statistics over abstract categories and their relationships to each other. Adult learners were exposed to miniature artificial languages containing variation in the ordering of the Subject, Object, and Verb constituents. Different orders (e.g. SOV, VSO) occurred in the input with different frequencies, but the occurrence of one order versus another was not predictable. Importantly, the language was constructed such that participants could only match the overall input probabilities if they were tracking statistics over abstract categories, not over individual words. At test, participants reproduced the probabilities present in the input with a high degree of accuracy. Closer examination revealed that learner's were matching the probabilities associated with individual verbs rather than the category as a whole. However, individual nouns had no impact on word orders produced. Thus, participants learned the probabilities of a particular ordering of the abstract grammatical categories Subject and Object associated with each verb. Results suggest that statistical learning mechanisms are capable of tracking relationships between abstract linguistic categories in addition to individual items.

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Science.gov (United States)

Hauffen, Karin; Bart, Eugene; Brady, Mark; Kersten, Daniel; Hegdé, Jay

2012-01-01

In order to quantitatively study object perception, be it perception by biological systems or by machines, one needs to create objects and object categories with precisely definable, preferably naturalistic, properties1. Furthermore, for studies on perceptual learning, it is useful to create novel objects and object categories (or object classes) with such properties2. Many innovative and useful methods currently exist for creating novel objects and object categories3-6 (also see refs. 7,8). However, generally speaking, the existing methods have three broad types of shortcomings. First, shape variations are generally imposed by the experimenter5,9,10, and may therefore be different from the variability in natural categories, and optimized for a particular recognition algorithm. It would be desirable to have the variations arise independently of the externally imposed constraints. Second, the existing methods have difficulty capturing the shape complexity of natural objects11-13. If the goal is to study natural object perception, it is desirable for objects and object categories to be naturalistic, so as to avoid possible confounds and special cases. Third, it is generally hard to quantitatively measure the available information in the stimuli created by conventional methods. It would be desirable to create objects and object categories where the available information can be precisely measured and, where necessary, systematically manipulated (or 'tuned'). This allows one to formulate the underlying object recognition tasks in quantitative terms. Here we describe a set of algorithms, or methods, that meet all three of the above criteria. Virtual morphogenesis (VM) creates novel, naturalistic virtual 3-D objects called 'digital embryos' by simulating the biological process of embryogenesis14. Virtual phylogenesis (VP) creates novel, naturalistic object categories by simulating the evolutionary process of natural selection9,12,13. Objects and object categories created

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

Directory of Open Access Journals (Sweden)

Stephen eGrossberg

2014-10-01

Full Text Available How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list

The transfer of category knowledge by macaques (Macaca mulatta) and humans (Homo sapiens).

Science.gov (United States)

Zakrzewski, Alexandria C; Church, Barbara A; Smith, J David

2018-02-01

Cognitive psychologists distinguish implicit, procedural category learning (stimulus-response associations learned outside declarative cognition) from explicit-declarative category learning (conscious category rules). These systems are dissociated by category learning tasks with either a multidimensional, information-integration (II) solution or a unidimensional, rule-based (RB) solution. In the present experiments, humans and two monkeys learned II and RB category tasks fostering implicit and explicit learning, respectively. Then they received occasional transfer trials-never directly reinforced-drawn from untrained regions of the stimulus space. We hypothesized that implicit-procedural category learning-allied to associative learning-would transfer weakly because it is yoked to the training stimuli. This result was confirmed for humans and monkeys. We hypothesized that explicit category learning-allied to abstract category rules-would transfer robustly. This result was confirmed only for humans. That is, humans displayed explicit category knowledge that transferred flawlessly. Monkeys did not. This result illuminates the distinctive abstractness, stimulus independence, and representational portability of humans' explicit category rules. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The application of multiple intelligence approach to the learning of human circulatory system

Science.gov (United States)

Kumalasari, Lita; Yusuf Hilmi, A.; Priyandoko, Didik

2017-11-01

The purpose of this study is to offer an alternative teaching approach or strategies which able to accommodate students’ different ability, intelligence and learning style. Also can gives a new idea for the teacher as a facilitator for exploring how to teach the student in creative ways and more student-center activities, for a lesson such as circulatory system. This study was carried out at one private school in Bandung involved eight students to see their responses toward the lesson that delivered by using Multiple Intelligence approach which is include Linguistic, Logical-Mathematical, Visual-Spatial, Musical, Bodily-Kinesthetic, Interpersonal, Intrapersonal, and Naturalistic. Students were test by using MI test based on Howard Gardner’s MI model to see their dominant intelligence. The result showed the percentage of top three ranks of intelligence are Bodily-Kinesthetic (73%), Visual-Spatial (68%), and Logical-Mathematical (61%). The learning process is given by using some different multimedia and activities to engaged their learning style and intelligence such as mini experiment, short clip, and questions. Student response is given by using self-assessment and the result is all students said the lesson gives them a knowledge and skills that useful for their life, they are clear with the explanation given, they didn’t find difficulties to understand the lesson and can complete the assignment given. At the end of the study, it is reveal that the students who are learned by Multiple Intelligence instructional approach have more enhance to the lesson given. It’s also found out that the students participated in the learning process which Multiple Intelligence approach was applied enjoyed the activities and have great fun.

Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning.

Science.gov (United States)

Jarvers, Christian; Brosch, Tobias; Brechmann, André; Woldeit, Marie L; Schulz, Andreas L; Ohl, Frank W; Lommerzheim, Marcel; Neumann, Heiko

2016-01-01

Biologically plausible modeling of behavioral reinforcement learning tasks has seen great improvements over the past decades. Less work has been dedicated to tasks involving contingency reversals, i.e., tasks in which the original behavioral goal is reversed one or multiple times. The ability to adjust to such reversals is a key element of behavioral flexibility. Here, we investigate the neural mechanisms underlying contingency-reversal tasks. We first conduct experiments with humans and gerbils to demonstrate memory effects, including multiple reversals in which subjects (humans and animals) show a faster learning rate when a previously learned contingency re-appears. Motivated by recurrent mechanisms of learning and memory for object categories, we propose a network architecture which involves reinforcement learning to steer an orienting system that monitors the success in reward acquisition. We suggest that a model sensory system provides feature representations which are further processed by category-related subnetworks which constitute a neural analog of expert networks. Categories are selected dynamically in a competitive field and predict the expected reward. Learning occurs in sequentialized phases to selectively focus the weight adaptation to synapses in the hierarchical network and modulate their weight changes by a global modulator signal. The orienting subsystem itself learns to bias the competition in the presence of continuous monotonic reward accumulation. In case of sudden changes in the discrepancy of predicted and acquired reward the activated motor category can be switched. We suggest that this subsystem is composed of a hierarchically organized network of dis-inhibitory mechanisms, dubbed a dynamic control network (DCN), which resembles components of the basal ganglia. The DCN selectively activates an expert network, corresponding to the current behavioral strategy. The trace of the accumulated reward is monitored such that large sudden

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Development and validity of mathematical learning assessment instruments based on multiple intelligence

Directory of Open Access Journals (Sweden)

Helmiah Suryani

2017-06-01

Full Text Available This study was aimed to develop and produce an assessment instrument of mathematical learning results based on multiple intelligence. The methods in this study used Borg & Gall-Research and Development approach (Research & Development. The subject of research was 289 students. The results of research: (1 Result of Aiken Analysis showed 58 valid items were between 0,714 to 0,952. (2 Result of the Exploratory on factor analysis indicated the instrument consist of three factors i.e. mathematical logical intelligence-spatial intelligence-and linguistic intelligence. KMO value was 0.661 df 0.780 sig. 0.000 with valid category. This research succeeded to developing the assessment instrument of mathematical learning results based on multiple intelligence of second grade in elementary school with characteristics of logical intelligence of mathematics, spatial intelligence, and linguistic intelligence.

From Perceptual Categories to Concepts: What Develops?

Science.gov (United States)

Sloutsky, Vladimir M.

2010-01-01

People are remarkably smart: they use language, possess complex motor skills, make non-trivial inferences, develop and use scientific theories, make laws, and adapt to complex dynamic environments. Much of this knowledge requires concepts and this paper focuses on how people acquire concepts. It is argued that conceptual development progresses from simple perceptual grouping to highly abstract scientific concepts. This proposal of conceptual development has four parts. First, it is argued that categories in the world have different structure. Second, there might be different learning systems (sub-served by different brain mechanisms) that evolved to learn categories of differing structures. Third, these systems exhibit differential maturational course, which affects how categories of different structures are learned in the course of development. And finally, an interaction of these components may result in the developmental transition from perceptual groupings to more abstract concepts. This paper reviews a large body of empirical evidence supporting this proposal. PMID:21116483

Rule-based category learning in children: the role of age and executive functioning.

Directory of Open Access Journals (Sweden)

Rahel Rabi

Full Text Available Rule-based category learning was examined in 4-11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy being used to solve the categorization task, demonstrating that the use of the task appropriate strategy increased with age. When children and adults who identified the correct categorization rule were compared, the performance deficit was no longer evident. Executive functions were also measured. While both working memory and inhibitory control were related to rule-based categorization and improved with age, working memory specifically was found to marginally mediate the age-related improvements in categorization. When analyses focused only on the sample of children, results showed that working memory ability and inhibitory control were associated with categorization performance and strategy use. The current findings track changes in categorization performance across childhood, demonstrating at which points performance begins to mature and resemble that of adults. Additionally, findings highlight the potential role that working memory and inhibitory control may play in rule-based category learning.

Deep convolutional neural network based antenna selection in multiple-input multiple-output system

Science.gov (United States)

Cai, Jiaxin; Li, Yan; Hu, Ying

2018-03-01

Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

An interplay of fusiform gyrus and hippocampus enables prototype- and exemplar-based category learning.

Science.gov (United States)

Lech, Robert K; Güntürkün, Onur; Suchan, Boris

2016-09-15

The aim of the present study was to examine the contributions of different brain structures to prototype- and exemplar-based category learning using functional magnetic resonance imaging (fMRI). Twenty-eight subjects performed a categorization task in which they had to assign prototypes and exceptions to two different families. This test procedure usually produces different learning curves for prototype and exception stimuli. Our behavioral data replicated these previous findings by showing an initially superior performance for prototypes and typical stimuli and a switch from a prototype-based to an exemplar-based categorization for exceptions in the later learning phases. Since performance varied, we divided participants into learners and non-learners. Analysis of the functional imaging data revealed that the interaction of group (learners vs. non-learners) and block (Block 5 vs. Block 1) yielded an activation of the left fusiform gyrus for the processing of prototypes, and an activation of the right hippocampus for exceptions after learning the categories. Thus, successful prototype- and exemplar-based category learning is associated with activations of complementary neural substrates that constitute object-based processes of the ventral visual stream and their interaction with unique-cue representations, possibly based on sparse coding within the hippocampus. Copyright © 2016 Elsevier B.V. All rights reserved.

Prototype learning and dissociable categorization systems in Alzheimer's disease.

Science.gov (United States)

Heindel, William C; Festa, Elena K; Ott, Brian R; Landy, Kelly M; Salmon, David P

2013-08-01

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer's disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of

Re-thinking typologies of multiple murders: the missing category of serial-mass murder and its theoretical and practical implications.

Science.gov (United States)

Edelstein, Arnon

2014-01-01

The concept of multiple murders (mm) is as old as humanity itself but it has only become prevalent in academic thought within the last three decades. Over this period scholars have introduced two main attitudes regarding multiple murders. Some argue that multiple murders are, theoretically and empirically, one concept that includes different sub-types: mass murder, spree murder, and serial murder. Other scholars claim that those "sub categories", are a whole different phenomenon, which are worthy and needed a separate examination and discussion because its uniqueness. To my opinion, this argument is more a semantic one than a fundamental one, as long as we consider each type of these murders as a unique phenomenon, with its own and unique characteristics. In addition both parties agree that the concept of multiple murders is differentiated into the same three main sub-categories. My argument is that a fourth sub-category of mm exists which goes unrecognized by most scholars. This sub-category, named "serial-mass murder," will help to differentiate the sub-categories more accurately and will more clearly define each of the remaining sub-categories.

Comparing the neural basis of monetary reward and cognitive feedback during information-integration category learning.

Science.gov (United States)

Daniel, Reka; Pollmann, Stefan

2010-01-06

The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.

Models as Relational Categories

Science.gov (United States)

Kokkonen, Tommi

2017-11-01

Model-based learning (MBL) has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL (using ready-made models, constructive modeling, and generative modeling) and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students' mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students' mental representations of models in terms of evolving relational structures in greater detail than previously done.

Category-based attentional guidance can operate in parallel for multiple target objects.

Science.gov (United States)

Jenkins, Michael; Grubert, Anna; Eimer, Martin

2018-04-30

The question whether the control of attention during visual search is always feature-based or can also be based on the category of objects remains unresolved. Here, we employed the N2pc component as an on-line marker for target selection processes to compare the efficiency of feature-based and category-based attentional guidance. Two successive displays containing pairs of real-world objects (line drawings of kitchen or clothing items) were separated by a 10 ms SOA. In Experiment 1, target objects were defined by their category. In Experiment 2, one specific visual object served as target (exemplar-based search). On different trials, targets appeared either in one or in both displays, and participants had to report the number of targets (one or two). Target N2pc components were larger and emerged earlier during exemplar-based search than during category-based search, demonstrating the superior efficiency of feature-based attentional guidance. On trials where target objects appeared in both displays, both targets elicited N2pc components that overlapped in time, suggesting that attention was allocated in parallel to these target objects. Critically, this was the case not only in the exemplar-based task, but also when targets were defined by their category. These results demonstrate that attention can be guided by object categories, and that this type of category-based attentional control can operate concurrently for multiple target objects. Copyright © 2018 Elsevier B.V. All rights reserved.

A connectionist model of category learning by individuals with high-functioning autism spectrum disorder.

Science.gov (United States)

Dovgopoly, Alexander; Mercado, Eduardo

2013-06-01

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

The Survival Processing Effect with Intentional Learning of Ad Hoc Categories

Directory of Open Access Journals (Sweden)

Anastasiya Savchenko

2014-04-01

Full Text Available Previous studies have shown that memory is adapted to remember information when it is processed in a survival context. This study investigates how procedural changes in Marinho (2012 study might have led to her failure to replicate the survival mnemonic advantage. In two between-subjects design experiments, participants were instructed to learn words from ad hoc categories and to rate their relevance to a survival or a control scenario. No survival advantage was obtained in either experiment. The Adjusted Ratio of Clustering (ARC scores revealed that including the category labels made the participants rely more on the category structure of the list. Various procedural aspects of the conducted experiments are discussed as possible reasons underlying the absence of the survival effect.

Two Pathways to Stimulus Encoding in Category Learning?

Science.gov (United States)

Davis, Tyler; Love, Bradley C.; Maddox, W. Todd

2008-01-01

Category learning theorists tacitly assume that stimuli are encoded by a single pathway. Motivated by theories of object recognition, we evaluate a dual-pathway account of stimulus encoding. The part-based pathway establishes mappings between sensory input and symbols that encode discrete stimulus features, whereas the image-based pathway applies holistic templates to sensory input. Our experiments use rule-plus-exception structures in which one exception item in each category violates a salient regularity and must be distinguished from other items. In Experiment 1, we find that discrete representations are crucial for recognition of exceptions following brief training. Experiments 2 and 3 involve multi-session training regimens designed to encourage either part or image-based encoding. We find that both pathways are able to support exception encoding, but have unique characteristics. We speculate that one advantage of the part-based pathway is the ability to generalize across domains, whereas the image-based pathway provides faster and more effortless recognition. PMID:19460948

Prototype Learning and Dissociable Categorization Systems in Alzheimer’s Disease

Science.gov (United States)

Heindel, William C.; Festa, Elena K.; Ott, Brian R.; Landy, Kelly M.; Salmon, David P.

2015-01-01

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer’s disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of

Bifurcation and category learning in network models of oscillating cortex

Science.gov (United States)

Baird, Bill

1990-06-01

A genetic model of oscillating cortex, which assumes “minimal” coupling justified by known anatomy, is shown to function as an associative memory, using previously developed theory. The network has explicit excitatory neurons with local inhibitory interneuron feedback that forms a set of nonlinear oscillators coupled only by long-range excitatory connections. Using a local Hebb-like learning rule for primary and higher-order synapses at the ends of the long-range connections, the system learns to store the kinds of oscillation amplitude patterns observed in olfactory and visual cortex. In olfaction, these patterns “emerge” during respiration by a pattern forming phase transition which we characterize in the model as a multiple Hopf bifurcation. We argue that these bifurcations play an important role in the operation of real digital computers and neural networks, and we use bifurcation theory to derive learning rules which analytically guarantee CAM storage of continuous periodic sequences-capacity: N/2 Fourier components for an N-node network-no “spurious” attractors.

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

Science.gov (United States)

Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

Information-integration category learning and the human uncertainty response.

Science.gov (United States)

Paul, Erick J; Boomer, Joseph; Smith, J David; Ashby, F Gregory

2011-04-01

The human response to uncertainty has been well studied in tasks requiring attention and declarative memory systems. However, uncertainty monitoring and control have not been studied in multi-dimensional, information-integration categorization tasks that rely on non-declarative procedural memory. Three experiments are described that investigated the human uncertainty response in such tasks. Experiment 1 showed that following standard categorization training, uncertainty responding was similar in information-integration tasks and rule-based tasks requiring declarative memory. In Experiment 2, however, uncertainty responding in untrained information-integration tasks impaired the ability of many participants to master those tasks. Finally, Experiment 3 showed that the deficit observed in Experiment 2 was not because of the uncertainty response option per se, but rather because the uncertainty response provided participants a mechanism via which to eliminate stimuli that were inconsistent with a simple declarative response strategy. These results are considered in the light of recent models of category learning and metacognition.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum.

Science.gov (United States)

Grossberg, Stephen; Palma, Jesse; Versace, Massimiliano

2015-01-01

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine.

Resonant cholinergic dynamics in cognitive and motor decision-making:Attention, category learning, and choice in neocortex, superior colliculus, and optic tectum

Directory of Open Access Journals (Sweden)

Stephen eGrossberg

2016-01-01

Full Text Available Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance or concrete (high vigilance. Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the avian brain and how such learning may be modulated by acetycholine.

The Validity of the earth and space science learning materials with orientation on multiple intelligences and character education

Science.gov (United States)

Liliawati, W.; Utama, J. A.; Ramalis, T. R.; Rochman, A. A.

2018-03-01

Validation of the Earth and Space Science learning the material in the chapter of the Earth's Protector based on experts (media & content expert and practitioners) and junior high school students' responses are presented. The data came from the development phase of the 4D method (Define, Design, Develop, Dissemination) which consist of two steps: expert appraisal and developmental testing. The instrument employed is rubric of suitability among the book contents with multiple intelligences activities, character education, a standard of book assessment, a questionnaires and close procedure. The appropriateness of the book contents with multiple intelligences, character education and standard of book assessment is in a good category. Meanwhile, students who used the book in their learning process gave a highly positive response; the book was easy to be understood. In general, the result of cloze procedure indicates high readability of the book. As our conclusion is the book chapter of the Earth's Protector can be used as a learning material accommodating students’ multiple intelligences and character internalization.

Facilitating Multiple Intelligences through Multimodal Learning Analytics

Science.gov (United States)

Perveen, Ayesha

2018-01-01

This paper develops a theoretical framework for employing learning analytics in online education to trace multiple learning variations of online students by considering their potential of being multiple intelligences based on Howard Gardner's 1983 theory of multiple intelligences. The study first emphasizes the need to facilitate students as…

Visual variability affects early verb learning.

Science.gov (United States)

Twomey, Katherine E; Lush, Lauren; Pearce, Ruth; Horst, Jessica S

2014-09-01

Research demonstrates that within-category visual variability facilitates noun learning; however, the effect of visual variability on verb learning is unknown. We habituated 24-month-old children to a novel verb paired with an animated star-shaped actor. Across multiple trials, children saw either a single action from an action category (identical actions condition, for example, travelling while repeatedly changing into a circle shape) or multiple actions from that action category (variable actions condition, for example, travelling while changing into a circle shape, then a square shape, then a triangle shape). Four test trials followed habituation. One paired the habituated verb with a new action from the habituated category (e.g., 'dacking' + pentagon shape) and one with a completely novel action (e.g., 'dacking' + leg movement). The others paired a new verb with a new same-category action (e.g., 'keefing' + pentagon shape), or a completely novel category action (e.g., 'keefing' + leg movement). Although all children discriminated novel verb/action pairs, children in the identical actions condition discriminated trials that included the completely novel verb, while children in the variable actions condition discriminated the out-of-category action. These data suggest that - as in noun learning - visual variability affects verb learning and children's ability to form action categories. © 2014 The British Psychological Society.

Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan; Gao, Xin

2014-01-01

Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.

Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan

2014-02-01

Traditional cross-domain learning methods transfer learning from a source domain to a target domain. In this paper, we propose the multiple-domain learning problem for several equally treated domains. The multiple-domain learning problem assumes that samples from different domains have different distributions, but share the same feature and class label spaces. Each domain could be a target domain, while also be a source domain for other domains. A novel multiple-domain representation method is proposed for the multiple-domain learning problem. This method is based on nonnegative matrix factorization (NMF), and tries to learn a basis matrix and coding vectors for samples, so that the domain distribution mismatch among different domains will be reduced under an extended variation of the maximum mean discrepancy (MMD) criterion. The novel algorithm - multiple-domain NMF (MDNMF) - was evaluated on two challenging multiple-domain learning problems - multiple user spam email detection and multiple-domain glioma diagnosis. The effectiveness of the proposed algorithm is experimentally verified. © 2013 Elsevier Ltd. All rights reserved.

Object-graphs for context-aware visual category discovery.

Science.gov (United States)

Lee, Yong Jae; Grauman, Kristen

2012-02-01

How can knowing about some categories help us to discover new ones in unlabeled images? Unsupervised visual category discovery is useful to mine for recurring objects without human supervision, but existing methods assume no prior information and thus tend to perform poorly for cluttered scenes with multiple objects. We propose to leverage knowledge about previously learned categories to enable more accurate discovery, and address challenges in estimating their familiarity in unsegmented, unlabeled images. We introduce two variants of a novel object-graph descriptor to encode the 2D and 3D spatial layout of object-level co-occurrence patterns relative to an unfamiliar region and show that by using them to model the interaction between an image’s known and unknown objects, we can better detect new visual categories. Rather than mine for all categories from scratch, our method identifies new objects while drawing on useful cues from familiar ones. We evaluate our approach on several benchmark data sets and demonstrate clear improvements in discovery over conventional purely appearance-based baselines.

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

Science.gov (United States)

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

The Concept Mastery in the Perspective of Gender of Junior High School Students on Eclipse Theme in Multiple Intelligences-based of Integrated Earth and Space Science Learning

Science.gov (United States)

Liliawati, W.; Utama, J. A.; Mursydah, L. S.

2017-03-01

The purpose of this study is to identify gender-based concept mastery differences of junior high school students after the implementation of multiple intelligences-based integrated earth and space science learning. Pretest-posttest group design was employed to two different classes at one of junior high school on eclipse theme in Tasikmalaya West Java: one class for boys (14 students) and one class of girls (18 students). The two-class received same treatment. The instrument of concepts mastery used in this study was open-ended eight essay questions. Reliability test result of this instrument was 0.9 (category: high) while for validity test results were high and very high category. We used instruments of multiple intelligences identification and learning activity observation sheet for our analysis. The results showed that normalized N-gain of concept mastery for boys and girls were improved, respectively 0.39 and 0.65. Concept mastery for both classes differs significantly. The dominant multiple intelligences for boys were in kinesthetic while girls dominated in the rest of multiple intelligences. Therefor we concluded that the concept mastery was influenced by gender and student’s multiple intelligences. Based on this finding we suggested to considering the factor of gender and students’ multiple intelligences given in the learning activity.

Procedural learning during declarative control.

Science.gov (United States)

Crossley, Matthew J; Ashby, F Gregory

2015-09-01

There is now abundant evidence that human learning and memory are governed by multiple systems. As a result, research is now turning to the next question of how these putative systems interact. For instance, how is overall control of behavior coordinated, and does learning occur independently within systems regardless of what system is in control? Behavioral, neuroimaging, and neuroscience data are somewhat mixed with respect to these questions. Human neuroimaging and animal lesion studies suggest independent learning and are mostly agnostic with respect to control. Human behavioral studies suggest active inhibition of behavioral output but have little to say regarding learning. The results of two perceptual category-learning experiments are described that strongly suggest that procedural learning does occur while the explicit system is in control of behavior and that this learning might be just as good as if the procedural system was controlling the response. These results are consistent with the idea that declarative memory systems inhibit the ability of the procedural system to access motor output systems but do not prevent procedural learning. (c) 2015 APA, all rights reserved).

The multiple reals of workplace learning

Directory of Open Access Journals (Sweden)

Kerry Harman

2014-04-01

Full Text Available The multiple reals of workplace learning are explored in this paper. Drawing on a Foucauldian conceptualisation of power as distributed, relational and productive, networks that work to produce particular objects and subjects as seemingly natural and real are examined. This approach enables different reals of workplace learning to be traced. Data from a collaborative industry-university research project is used to illustrate the approach, with a focus on the intersecting practices of a group of professional developers and a group of workplace learning researchers. The notion of multiple reals holds promise for research on workplace learning as it moves beyond a view of reality as fixed and singular to a notion of reality as performed in and through a diversity of practices, including the practices of workplace learning researchers.

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations.

Science.gov (United States)

Grossberg, Stephen; Pilly, Praveen K

2014-02-05

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

Veto-Consensus Multiple Kernel Learning

NARCIS (Netherlands)

Zhou, Y.; Hu, N.; Spanos, C.J.

2016-01-01

We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The

Multiple-choice pretesting potentiates learning of related information.

Science.gov (United States)

Little, Jeri L; Bjork, Elizabeth Ligon

2016-10-01

Although the testing effect has received a substantial amount of empirical attention, such research has largely focused on the effects of tests given after study. The present research examines the effect of using tests prior to study (i.e., as pretests), focusing particularly on how pretesting influences the subsequent learning of information that is not itself pretested but that is related to the pretested information. In Experiment 1, we found that multiple-choice pretesting was better for the learning of such related information than was cued-recall pretesting or a pre-fact-study control condition. In Experiment 2, we found that the increased learning of non-pretested related information following multiple-choice testing could not be attributed to increased time allocated to that information during subsequent study. Last, in Experiment 3, we showed that the benefits of multiple-choice pretesting over cued-recall pretesting for the learning of related information persist over 48 hours, thus demonstrating the promise of multiple-choice pretesting to potentiate learning in educational contexts. A possible explanation for the observed benefits of multiple-choice pretesting for enhancing the effectiveness with which related nontested information is learned during subsequent study is discussed.

Category-theoretic models of algebraic computer systems

Science.gov (United States)

Kovalyov, S. P.

2016-01-01

A computer system is said to be algebraic if it contains nodes that implement unconventional computation paradigms based on universal algebra. A category-based approach to modeling such systems that provides a theoretical basis for mapping tasks to these systems' architecture is proposed. The construction of algebraic models of general-purpose computations involving conditional statements and overflow control is formally described by a reflector in an appropriate category of algebras. It is proved that this reflector takes the modulo ring whose operations are implemented in the conventional arithmetic processors to the Łukasiewicz logic matrix. Enrichments of the set of ring operations that form bases in the Łukasiewicz logic matrix are found.

An Investigation between Multiple Intelligences and Learning Styles

Science.gov (United States)

Sener, Sabriye; Çokçaliskan, Ayten

2018-01-01

Exploring learning style and multiple intelligence type of learners can enable the students to identify their strengths and weaknesses and learn from them. It is also very important for teachers to understand their learners' learning styles and multiple intelligences since they can carefully identify their goals and design activities that can…

A neurocomputational theory of how explicit learning bootstraps early procedural learning.

Science.gov (United States)

Paul, Erick J; Ashby, F Gregory

2013-01-01

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

Multiple Kernel Learning with Data Augmentation

Science.gov (United States)

2016-11-22

JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to

A Note on DeCaro, Thomas, and Beilock (2008): Further Data Demonstrate Complexities in the Assessment of Information-Integration Category Learning

Science.gov (United States)

Tharp, Ian J.; Pickering, Alan D.

2009-01-01

DeCaro et al. [DeCaro, M. S., Thomas, R. D., & Beilock, S. L. (2008). "Individual differences in category learning: Sometimes less working memory capacity is better than more." "Cognition, 107"(1), 284-294] explored how individual differences in working memory capacity differentially mediate the learning of distinct category structures.…

Which is the best intrinsic motivation signal for learning multiple skills?

Directory of Open Access Journals (Sweden)

Vieri Giuliano Santucci

2013-11-01

Full Text Available Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic Motivations (i.e. motivations not connected to reward-related stimuli play a cardinal role in animal learning, and can be considered as a fundamental tool for developing more autonomous and more adaptive artificial agents. In this work, we provide an exhaustive analysis of a scarcely investigated problem: which kind of IM reinforcement signal is the most suitable for driving the acquisition of multiple skills in the shortest time? To this purpose we implemented an artificial agent with a hierarchical architecture that allows to learn and cache different skills. We tested the system in a setup with continuous states and actions, in particular, with a cinematic robotic arm that has to learn different reaching tasks. We compare the results of different versions of the system driven by several different intrinsic motivation signals. The results show a that intrinsic reinforcements purely based on the knowledge of the system are not appropriate to guide the acquisition of multiple skills, and b that the stronger the link between the IM signal and the competence of the system, the better the performance.

Metacognitive control of categorial neurobehavioral decision systems

Directory of Open Access Journals (Sweden)

Gordon Robert Foxall

2016-02-01

Full Text Available The competing neuro-behavioral decision systems (CNDS model proposes that the degree to which an individual discounts the future is a function of the relative hyperactivity of an impulsive system based on the limbic and paralimbic brain regions and the relative hypoactivity of an executive system based in prefrontal cortex (PFC. The model depicts the relationship between these categorial systems in terms of the antipodal neurophysiological, behavioral, and decision (cognitive functions that engender classes normal and addictive responding. However, a case may be made for construing several components of the impulsive and executive systems depicted in the model as categories (elements of additional systems that are concerned with the metacognitive control of behavior. Hence, this paper proposes a category-based structure for understanding the effects on behavior of CNDS, which includes not only the impulsive and executive systems of the basic model but, a superordinate level of reflective or rational decision-making. Following recent developments in the modeling of cognitive control which contrasts Type 1 (rapid, autonomous, parallel processing with Type 2 (slower, computationally-demanding, sequential processing, the proposed model incorporates an arena in which the potentially conflicting imperatives of impulsive and executive systems are examined and from which a more appropriate behavioral response than impulsive choice emerges. This configuration suggests a forum in which the interaction of picoeconomic interests, which provide a cognitive dimension for CNDS, can be conceptualized. This proposition is examined in light of the resolution of conflict by means of bundling.

A Bayesian Model of Category-Specific Emotional Brain Responses

Science.gov (United States)

Wager, Tor D.; Kang, Jian; Johnson, Timothy D.; Nichols, Thomas E.; Satpute, Ajay B.; Barrett, Lisa Feldman

2015-01-01

Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories—fear, anger, disgust, sadness, or happiness—is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches. PMID:25853490

A Full-Text-Based Search Engine for Finding Highly Matched Documents Across Multiple Categories

Science.gov (United States)

Nguyen, Hung D.; Steele, Gynelle C.

2016-01-01

This report demonstrates the full-text-based search engine that works on any Web-based mobile application. The engine has the capability to search databases across multiple categories based on a user's queries and identify the most relevant or similar. The search results presented here were found using an Android (Google Co.) mobile device; however, it is also compatible with other mobile phones.

On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of Tuberculosis

NARCIS (Netherlands)

Melendez Rodriguez, J.C.; Ginneken, B. van; Maduskar, P.; Philipsen, R.H.H.M.; Ayles, H.; Sanchez, C.I.

2016-01-01

The major advantage of multiple-instance learning (MIL) applied to a computer-aided detection (CAD) system is that it allows optimizing the latter with case-level labels instead of accurate lesion outlines as traditionally required for a supervised approach. As shown in previous work, a MIL-based

Category Theory as a Formal Mathematical Foundation for Model-Based Systems Engineering

KAUST Repository

Mabrok, Mohamed; Ryan, Michael J.

2017-01-01

In this paper, we introduce Category Theory as a formal foundation for model-based systems engineering. A generalised view of the system based on category theory is presented, where any system can be considered as a category. The objects

Multiple category-lot quality assurance sampling: a new classification system with application to schistosomiasis control.

Science.gov (United States)

Olives, Casey; Valadez, Joseph J; Brooker, Simon J; Pagano, Marcello

2012-01-01

Originally a binary classifier, Lot Quality Assurance Sampling (LQAS) has proven to be a useful tool for classification of the prevalence of Schistosoma mansoni into multiple categories (≤10%, >10 and LQAS (MC-LQAS) have not received full treatment. We explore the analytical properties of MC-LQAS, and validate its use for the classification of S. mansoni prevalence in multiple settings in East Africa. We outline MC-LQAS design principles and formulae for operating characteristic curves. In addition, we derive the average sample number for MC-LQAS when utilizing semi-curtailed sampling and introduce curtailed sampling in this setting. We also assess the performance of MC-LQAS designs with maximum sample sizes of n=15 and n=25 via a weighted kappa-statistic using S. mansoni data collected in 388 schools from four studies in East Africa. Overall performance of MC-LQAS classification was high (kappa-statistic of 0.87). In three of the studies, the kappa-statistic for a design with n=15 was greater than 0.75. In the fourth study, where these designs performed poorly (kappa-statistic less than 0.50), the majority of observations fell in regions where potential error is known to be high. Employment of semi-curtailed and curtailed sampling further reduced the sample size by as many as 0.5 and 3.5 observations per school, respectively, without increasing classification error. This work provides the needed analytics to understand the properties of MC-LQAS for assessing the prevalance of S. mansoni and shows that in most settings a sample size of 15 children provides a reliable classification of schools.

Category Theory as a Formal Mathematical Foundation for Model-Based Systems Engineering

KAUST Repository

Mabrok, Mohamed

2017-01-09

In this paper, we introduce Category Theory as a formal foundation for model-based systems engineering. A generalised view of the system based on category theory is presented, where any system can be considered as a category. The objects of the category represent all the elements and components of the system and the arrows represent the relations between these components (objects). The relationship between these objects are the arrows or the morphisms in the category. The Olog is introduced as a formal language to describe a given real-world situation description and requirement writing. A simple example is provided.

A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

Directory of Open Access Journals (Sweden)

Lun Zhai

2014-01-01

Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

Multiple reversal olfactory learning in honeybees

Directory of Open Access Journals (Sweden)

Theo Mota

2010-07-01

Full Text Available In multiple reversal learning, animals trained to discriminate a reinforced from a non-reinforced stimulus are subjected to various, successive reversals of stimulus contingencies (e.g. A+ vs. B-, A- vs. B+, A+ vs. B-. This protocol is useful to determine whether or not animals learn to learn and solve successive discriminations faster (or with fewer errors with increasing reversal experience. Here we used the olfactory conditioning of proboscis extension reflex to study how honeybees Apis mellifera perform in a multiple reversal task. Our experiment contemplated four consecutive differential conditioning phases involving the same odors (A+ vs. B- to A- vs. B+ to A+ vs. B- to A- vs. B+. We show that bees in which the weight of reinforced or non-reinforced stimuli was similar mastered the multiple olfactory reversals. Bees which failed the task exhibited asymmetric responses to reinforced and non-reinforced stimuli, thus being unable to rapidly reverse stimulus contingencies. Efficient reversers did not improve their successive discriminations but rather tended to generalize their choice to both odors at the end of conditioning. As a consequence, both discrimination and reversal efficiency decreasedalong experimental phases. This result invalidates a learning-to-learn effect and indicates that bees do not only respond to the actual stimulus contingencies but rather combine these with an average of past experiences with the same stimuli.  

Moving Beyond Motive-based categories of Targeted Violence

Energy Technology Data Exchange (ETDEWEB)

Weine, Stevan [Univ. of Illinois, Chicago, IL (United States); Cohen, John [Rutgers Univ., New Brunswick, NJ (United States); Brannegan, David [Argonne National Lab. (ANL), Argonne, IL (United States)

2015-10-01

Today’s categories for responding to targeted violence are motive-based and tend to drive policies, practices, training, media coverage, and research. These categories are based on the assumption that there are significant differences between ideological and non-ideological actors and between domestic and international actors. We question the reliance on these categories and offer an alternative way to frame the response to multiple forms of targeted violence. We propose adopting a community-based multidisciplinary approach to assess risk and provide interventions that are focused on the pre-criminal space. We describe four capabilities that should be implemented locally by establishing and maintaining multidisciplinary response teams that combine community and law-enforcement components: (1) community members are educated, making them better able to identify and report patterns associated with elevated risk for violence; (2) community-based professionals are trained to assess the risks for violent behavior posed by individuals; (3) community-based professionals learn to implement strategies that directly intervene in causal factors for those individuals who are at elevated risk; and (4) community-based professionals learn to monitor and assess an individual’s risk for violent behaviors on an ongoing basis. Community-based multidisciplinary response teams have the potential to identify and help persons in the pre-criminal space and to reduce barriers that have traditionally impeded community/law-enforcement collaboration.

A Neurocomputational Theory of how Explicit Learning Bootstraps Early Procedural Learning

Directory of Open Access Journals (Sweden)

Erick Joseph Paul

2013-12-01

Full Text Available It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative system depending largely on the prefrontal cortex, and a procedural (non-declarative system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system’s control of motor responses through basal ganglia-mediated loops.

Finding biomedical categories in Medline®

Directory of Open Access Journals (Sweden)

Yeganova Lana

2012-10-01

Full Text Available Abstract Background There are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly defined ontologies. Automatically identifying meaningful categories of entities in a large text corpus is useful for information extraction, construction of machine learning features, and development of semantic representations. In this paper we describe and compare two methods for automatically learning meaningful biomedical categories in Medline. The first approach is a simple statistical method that uses part-of-speech and frequency information to extract a list of frequent nouns from Medline. The second method implements an alignment-based technique to learn frequent generic patterns that indicate a hyponymy/hypernymy relationship between a pair of noun phrases. We then apply these patterns to Medline to collect frequent hypernyms as potential biomedical categories. Results We study and compare these two alternative sets of terms to identify semantic categories in Medline. We find that both approaches produce reasonable terms as potential categories. We also find that there is a significant agreement between the two sets of terms. The overlap between the two methods improves our confidence regarding categories predicted by these independent methods. Conclusions This study is an initial attempt to extract categories that are discussed in Medline. Rather than imposing external ontologies on Medline, our methods allow categories to emerge from the text.

Cross-platform learning: on the nature of children's learning from multiple media platforms.

Science.gov (United States)

Fisch, Shalom M

2013-01-01

It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several recent studies to explore cross-platform learning (i.e., learning from combined use of multiple media platforms) and how such learning compares to learning from one medium. The paper discusses unique benefits of cross-platform learning, a theoretical mechanism to explain how these benefits might arise, and questions for future research in this emerging field. Copyright © 2013 Wiley Periodicals, Inc., A Wiley Company.

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

Science.gov (United States)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Linear System Control Using Stochastic Learning Automata

Science.gov (United States)

Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.

1998-01-01

This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.

Developing Computer Assisted Media of Pneumatic System Learning Oriented to Industrial Demands

Directory of Open Access Journals (Sweden)

Wahyu Dwi Kurniawan

2017-04-01

Full Text Available This study aimed to develop learning media of pneumatic systems based on computer-assisted learning as an effort to improve the competence of students at the Department of Mechanical Engineering, Faculty of Engineering UNESA. The development method referred to the 4D model design of Thiagarajan comprising the steps of: define, design, develop, and desseminate. The results showed that the expert validation average score included in both categories was 3.54, indicating the learning application acceptable. A limited test showed effective results, namely: (a Analysis of the data included in the category of learning was good (3.64, indicated by students’ enthusiasm in the learning process; (b Teaching learning activities were categorized as good, the students actively involved in learning, and the most dominant activity was doing tasks while discussing; (c Learning objectives were both achieved individually and classically; (d The students showed a positive response expressed by the students’ interest, excitement, and motivation to follow the learning process.

The Effect of Zipfian Frequency Variations on Category Formation in Adult Artificial Language Learning

Science.gov (United States)

Schuler, Kathryn D.; Reeder, Patricia A.; Newport, Elissa L.; Aslin, Richard N.

2017-01-01

Successful language acquisition hinges on organizing individual words into grammatical categories and learning the relationships between them, but the method by which children accomplish this task has been debated in the literature. One proposal is that learners use the shared distributional contexts in which words appear as a cue to their…

On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning.

Science.gov (United States)

Grossberg, Stephen; Markowitz, Jeffrey; Cao, Yongqiang

2011-12-01

Visual object recognition is an essential accomplishment of advanced brains. Object recognition needs to be tolerant, or invariant, with respect to changes in object position, size, and view. In monkeys and humans, a key area for recognition is the anterior inferotemporal cortex (ITa). Recent neurophysiological data show that ITa cells with high object selectivity often have low position tolerance. We propose a neural model whose cells learn to simulate this tradeoff, as well as ITa responses to image morphs, while explaining how invariant recognition properties may arise in stages due to processes across multiple cortical areas. These processes include the cortical magnification factor, multiple receptive field sizes, and top-down attentive matching and learning properties that may be tuned by task requirements to attend to either concrete or abstract visual features with different levels of vigilance. The model predicts that data from the tradeoff and image morph tasks emerge from different levels of vigilance in the animals performing them. This result illustrates how different vigilance requirements of a task may change the course of category learning, notably the critical features that are attended and incorporated into learned category prototypes. The model outlines a path for developing an animal model of how defective vigilance control can lead to symptoms of various mental disorders, such as autism and amnesia. Copyright © 2011 Elsevier Ltd. All rights reserved.

The Development of Inquiry Learning Materials to Complete Content Life System Organization in Junior High School Students

Science.gov (United States)

Mayasari, F.; Raharjo; Supardi, Z. A. I.

2018-01-01

This research aims to develop the material eligibility to complete the inquiry learning of student in the material organization system of junior high school students. Learning materials developed include syllabi, lesson plans, students’ textbook, worksheets, and learning achievement test. This research is the developmental research which employ Dick and Carey model to develop learning material. The experiment was done in Junior High School 4 Lamongan regency using One Group Pretest-Posttest Design. The data collection used validation, observation, achievement test, questionnaire administration, and documentation. Data analysis techniques used quantitative and qualitative descriptive.The results showed that the developed learning material was valid and can be used. Learning activity accomplished with good category, where student activities were observed. The aspects of attitudes were observed during the learning process are honest, responsible, and confident. Student learning achievement gained an average of 81, 85 in complete category, with N-Gain 0, 75 for a high category. The activities and student response to learning was very well categorized. Based on the results, this researcher concluded that the device classified as feasible of inquiry-based learning (valid, practical, and effective) system used on the material organization of junior high school students.

Simple skew category algebras associated with minimal partially defined dynamical systems

DEFF Research Database (Denmark)

Nystedt, Patrik; Öinert, Per Johan

2013-01-01

In this article, we continue our study of category dynamical systems, that is functors s from a category G to Topop, and their corresponding skew category algebras. Suppose that the spaces s(e), for e∈ob(G), are compact Hausdorff. We show that if (i) the skew category algebra is simple, then (ii) G...

Six to Ten Digits Multiplication Fun Learning Using Puppet Prototype

Science.gov (United States)

Islamiah Rosli, D.'oria; Ali, Azita; Peng, Lim Soo; Sujardi, Imam; Usodo, Budi; Adie Perdana, Fengky

2017-01-01

Logic and technical subjects require students to understand basic knowledge in mathematic. For instance, addition, minus, division and multiplication operations need to be mastered by students due to mathematic complexity as the learning mathematic grows higher. Weak foundation in mathematic also contribute to high failure rate in mathematic subjects in schools. In fact, students in primary schools are struggling to learn mathematic because they need to memorize formulas, multiplication or division operations. To date, this study will develop a puppet prototyping for learning mathematic for six to ten digits multiplication. Ten participants involved in the process of developing the prototype in this study. Students involved in the study were those from the intermediate class students whilst teachers were selected based on their vast knowledge and experiences and have more than five years of experience in teaching mathematic. Close participatory analysis will be used in the prototyping process as to fulfil the requirements of the students and teachers whom will use the puppet in learning six to ten digit multiplication in mathematic. Findings showed that, the students had a great time and fun learning experience in learning multiplication and they able to understand the concept of multiplication using puppet. Colour and materials of the puppet also help to attract student attention during learning. Additionally, students able to visualized and able to calculate accurate multiplication value and the puppet help them to recall in multiplying and adding the digits accordingly.

The Effectiveness of Problem-Based Learning Approach Based on Multiple Intelligences in Terms of Student’s Achievement, Mathematical Connection Ability, and Self-Esteem

Science.gov (United States)

Kartikasari, A.; Widjajanti, D. B.

2017-02-01

The aim of this study is to explore the effectiveness of learning approach using problem-based learning based on multiple intelligences in developing student’s achievement, mathematical connection ability, and self-esteem. This study is experimental research with research sample was 30 of Grade X students of MIA III MAN Yogyakarta III. Learning materials that were implemented consisting of trigonometry and geometry. For the purpose of this study, researchers designed an achievement test made up of 44 multiple choice questions with respectively 24 questions on the concept of trigonometry and 20 questions for geometry. The researcher also designed a connection mathematical test and self-esteem questionnaire that consisted of 7 essay questions on mathematical connection test and 30 items of self-esteem questionnaire. The learning approach said that to be effective if the proportion of students who achieved KKM on achievement test, the proportion of students who achieved a minimum score of high category on the results of both mathematical connection test and self-esteem questionnaire were greater than or equal to 70%. Based on the hypothesis testing at the significance level of 5%, it can be concluded that the learning approach using problem-based learning based on multiple intelligences was effective in terms of student’s achievement, mathematical connection ability, and self-esteem.

Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

Science.gov (United States)

Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

2017-08-01

Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7Mental Modelling Ability (M-MMA) for 3Mental Modelling Ability (L-MMA) for 0 ≤ x ≤ 3 score. The result shows that problem solving based learning model with multiple representations approach can be an alternative to be applied in improving students' MMA.

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

Science.gov (United States)

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

Studying different tasks of implicit learning across multiple test sessions conducted on the web

Directory of Open Access Journals (Sweden)

Werner eSævland

2016-06-01

Full Text Available Implicit learning is usually studied through individual performance on a single task, with the most common tasks being Serial Reaction Time task (SRT; Nissen and Bullemer, 1987, Dynamic System Control task (DSC; (Berry and Broadbent, 1984 and artificial Grammar Learning task (AGL; (Reber, 1967. Few attempts have been made to compare performance across different implicit learning tasks within the same experiment. The current experiment was designed study the relationship between performance on the DSC Sugar factory task (Berry and Broadbent, 1984 and the Alternating Serial Reaction Time task (ASRT; (Howard and Howard, 1997. We also addressed another limitation to traditional implicit learning experiments, namely that implicit learning is usually studied in laboratory settings over a restricted time span lasting for less than an hour (Berry and Broadbent, 1984; Nissen and Bullemer, 1987; Reber, 1967. In everyday situations, implicit learning is assumed to involve a gradual accumulation of knowledge across several learning episodes over a larger time span (Norman and Price, 2012. One way to increase the ecological validity of implicit learning experiments could be to present the learning material repeatedly across shorter experimental sessions (Howard and Howard, 1997; Cleeremans and McClelland, 1991. This can most easily be done by using a web-based setup that participants can access from home. We therefore created an online web-based system for measuring implicit learning that could be administered in either single or multiple sessions. Participants (n = 66 were assigned to either a single-session or a multi-session condition. Learning and the degree of conscious awareness of the learned regularities was compared across condition (single vs. multiple sessions and tasks (DSC vs. ASRT. Results showed that learning on the two tasks was not related. However, participants in the multiple sessions condition did show greater improvements in reaction

Functional imaging of the semantic system: retrieval of sensory-experienced and verbally learned knowledge.

Science.gov (United States)

Noppeney, Uta; Price, Cathy J

2003-01-01

This paper considers how functional neuro-imaging can be used to investigate the organization of the semantic system and the limitations associated with this technique. The majority of the functional imaging studies of the semantic system have looked for divisions by varying stimulus category. These studies have led to divergent results and no clear anatomical hypotheses have emerged to account for the dissociations seen in behavioral studies. Only a few functional imaging studies have used task as a variable to differentiate the neural correlates of semantic features more directly. We extend these findings by presenting a new study that contrasts tasks that differentially weight sensory (color and taste) and verbally learned (origin) semantic features. Irrespective of the type of semantic feature retrieved, a common semantic system was activated as demonstrated in many previous studies. In addition, the retrieval of verbally learned, but not sensory-experienced, features enhanced activation in medial and lateral posterior parietal areas. We attribute these "verbally learned" effects to differences in retrieval strategy and conclude that evidence for segregation of semantic features at an anatomical level remains weak. We believe that functional imaging has the potential to increase our understanding of the neuronal infrastructure that sustains semantic processing but progress may require multiple experiments until a consistent explanatory framework emerges.

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

Science.gov (United States)

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers

Science.gov (United States)

Gracious, F. L. Antony; Shyla, F. L. Jasmine Anne

2012-01-01

The present study Multiple Intelligence and Digital Learning Awareness of prospective B.Ed teachers was probed to find the relationship between Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers. Data for the study were collected using self made Multiple Intelligence Inventory and Digital Learning Awareness Scale.…

Dissimilarity-based multiple instance learning

DEFF Research Database (Denmark)

Sørensen, Lauge; Loog, Marco; Tax, David M. J.

2010-01-01

In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised cla...... between distributions of within- and between set point distances, thereby taking relations within and between sets into account. Experiments on five publicly available data sets show competitive performance in terms of classification accuracy compared to previously published results....

Memory and learning disturbances in multiple sclerosis

International Nuclear Information System (INIS)

Izquierdo, Guillermo; Mir, Jordi; Gonzalez, Manuel; Martinez-Parra, Carlos; Campoy, Francisco Jr

1991-01-01

Thirty-five patients with definite multiple sclerosis (MS) were studied. They underwent neuropsychological testing and magnetic resonance imaging (MRI). The MRI findings at different brain areas levels were compared with the neuropsychological findings. A quantitative system was used to measure MRI-MS lesions. In this series, a positive correlation was established between memory and learning disturbances measured by Battery 144, and the lesions measured by MRI (total, hemispheric and , particularly, periventricular lesions). MRI can detect MS lesions, and this study shows that a correlation between MRI and neuropsychological findings is possible if quantitative methods are used to distinguish different MS involvement areas in relation to neuropsychological tasks. These findings suggest that hemispheric lesions in MS produce cognitive disturbances and MRI could be a useful tool in predicting memory and learning impairment. (author). 20 refs.; 1 fig.; 2 tabs

Auditory and phonetic category formation

NARCIS (Netherlands)

Goudbeek, Martijn; Cutler, A.; Smits, R.; Swingley, D.; Cohen, Henri; Lefebvre, Claire

2017-01-01

Among infants' first steps in language acquisition is learning the relevant contrasts of the language-specific phonemic repertoire. This learning is viewed as the formation of categories in a multidimensional psychophysical space. Research in the visual modality has shown that for adults, some kinds

The effect of multiple intelligence-based learning towards students’ concept mastery and interest in learning matter

Science.gov (United States)

Pratiwi, W. N.; Rochintaniawati, D.; Agustin, R. R.

2018-05-01

This research was focused on investigating the effect of multiple intelligence -based learning as a learning approach towards students’ concept mastery and interest in learning matter. The one-group pre-test - post-test design was used in this research towards a sample which was according to the suitable situation of the research sample, n = 13 students of the 7th grade in a private school in Bandar Seri Begawan. The students’ concept mastery was measured using achievement test and given at the pre-test and post-test, meanwhile the students’ interest level was measured using a Likert Scale for interest. Based on the analysis of the data, the result shows that the normalized gain was .61, which was considered as a medium improvement. in other words, students’ concept mastery in matter increased after being taught using multiple intelligence-based learning. The Likert scale of interest shows that most students have a high interest in learning matter after being taught by multiple intelligence-based learning. Therefore, it is concluded that multiple intelligence – based learning helped in improving students’ concept mastery and gain students’ interest in learning matter.

Motivation categories in college students’ learning engagement behaviors and outcomes in Taiwan: An application of cluster analysis

OpenAIRE

Tzu-Ling Hsieh

2016-01-01

This study explores how different motivation categories influence college students’ learning engagement behaviors and outcomes under the context of eastern culture. 178 junior college students were surveyed at a four-year research university in Taiwan. The study addressed two research questions: 1. Are there subgroups of students with significantly different motivation profiles? 2. If so, do these subgroups of students differ significantly in terms of their engagement behaviors and learning o...

Stress Effects on Multiple Memory System Interactions

Science.gov (United States)

Ness, Deborah; Calabrese, Pasquale

2016-01-01

Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour. PMID:27034845

Stress Effects on Multiple Memory System Interactions.

Science.gov (United States)

Ness, Deborah; Calabrese, Pasquale

2016-01-01

Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour.

Stress Effects on Multiple Memory System Interactions

Directory of Open Access Journals (Sweden)

Deborah Ness

2016-01-01

Full Text Available Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory systems, we review recent animal and human studies exploring the effects of stress on multiple memory systems. Apart from discussing the interaction between distinct memory systems in stressful situations, we will also outline the fundamental role of the amygdala in mediating such stress effects. Additionally, based on the methods applied in the herein discussed studies, we will discuss how memory translates into behaviour.

A Multiple Cross-Cultural Comparison of Approaches to Learning

Science.gov (United States)

Bowden, Mark P.; Abhayawansa, Subhash; Manzin, Gregoria

2015-01-01

This study compares learning approaches of local English-speaking students and students from Asian countries studying at an Australian metropolitan university. The sample consists of students across 13 different countries. Unlike previous studies, students from Asian countries are subdivided into two categories: students from Confucian Heritage…

A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

OpenAIRE

Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

2013-01-01

Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

Building Artificial Vision Systems with Machine Learning

Energy Technology Data Exchange (ETDEWEB)

LeCun, Yann [New York University

2011-02-23

Three questions pose the next challenge for Artificial Intelligence (AI), robotics, and neuroscience. How do we learn perception (e.g. vision)? How do we learn representations of the perceptual world? How do we learn visual categories from just a few examples?

Multiple systems for motor skill learning

OpenAIRE

Clark, Dav; Ivry, Richard B.

2010-01-01

Motor learning is a ubiquitous feature of human competence. This review focuses on two particular classes of model tasks for studying skill acquisition. The serial reaction time (SRT) task is used to probe how people learn sequences of actions, while adaptation in the context of visuomotor or force field perturbations serves to illustrate how preexisting movements are recalibrated in novel environments. These tasks highlight important issues regarding the representational changes that occur d...

Due Process in Dual Process: Model-Recovery Simulations of Decision-Bound Strategy Analysis in Category Learning

Science.gov (United States)

Edmunds, Charlotte E. R.; Milton, Fraser; Wills, Andy J.

2018-01-01

Behavioral evidence for the COVIS dual-process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, 2016). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to…

Color descriptors for object category recognition

NARCIS (Netherlands)

van de Sande, K.E.A.; Gevers, T.; Snoek, C.G.M.

2008-01-01

Category recognition is important to access visual information on the level of objects. A common approach is to compute image descriptors first and then to apply machine learning to achieve category recognition from annotated examples. As a consequence, the choice of image descriptors is of great

Simulating Category Learning and Set Shifting Deficits in Patients Weight-Restored from Anorexia Nervosa

Science.gov (United States)

2014-01-01

Neuropsychology, in press     Simulating Category Learning and Set Shifting Deficits in Patients Weight-Restored from Anorexia Nervosa J...University   Objective: To examine set shifting in a group of women previously diagnosed with anorexia nervosa (AN) who are now weight-restored (AN-WR...participant fails to switch to the new rule but rather persists with the previously correct rule. Adult patients with Anorexia Nervosa (AN) are often impaired

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

Science.gov (United States)

Huang, Qinghua; Zhang, Fan; Li, Xuelong

2018-01-01

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Directory of Open Access Journals (Sweden)

Qinghua Huang

2018-01-01

Full Text Available The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Factors that influence the relative use of multiple memory systems.

Science.gov (United States)

Packard, Mark G; Goodman, Jarid

2013-11-01

Neurobehavioral evidence supports the existence of at least two anatomically distinct "memory systems" in the mammalian brain that mediate dissociable types of learning and memory; a "cognitive" memory system dependent upon the hippocampus and a "stimulus-response/habit" memory system dependent upon the dorsolateral striatum. Several findings indicate that despite their anatomical and functional distinctiveness, hippocampal- and dorsolateral striatal-dependent memory systems may potentially interact and that, depending on the learning situation, this interaction may be cooperative or competitive. One approach to examining the neural mechanisms underlying these interactions is to consider how various factors influence the relative use of multiple memory systems. The present review examines several such factors, including information compatibility, temporal sequence of training, the visual sensory environment, reinforcement parameters, emotional arousal, and memory modulatory systems. Altering these parameters can lead to selective enhancements of either hippocampal-dependent or dorsolateral striatal-dependent memory, and bias animals toward the use of either cognitive or habit memory in dual-solution tasks that may be solved adequately with either memory system. In many learning situations, the influence of such experimental factors on the relative use of memory systems likely reflects a competitive interaction between the systems. Research examining how various factors influence the relative use of multiple memory systems may be a useful method for investigating how these systems interact with one another. Copyright © 2013 Wiley Periodicals, Inc.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

Science.gov (United States)

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

Competence-based multiple learning paths: on the road of implementation

Directory of Open Access Journals (Sweden)

María Dolores de Prada

2014-12-01

Full Text Available This article presents the results of an action-research implementation project based on a system that weaves together five different routes to facilitate the development of competences through the use of multiple learning paths for primary and secondary teachers. The first and initial results that the article deals with relate to the experience of math teachers for ages 11 to 14. Other levels and other fields are in the process of being developed. The article deals briefly with the justification, the background and the fundamental principles that underpin the research methodology and introduces a number of elements such as the method followed by the research, the resources and the materials used as well as the results obtained at the end of the second year of this experience. It also justifies the model chosen and the criteria and strategies selected for its reliability and verification. In addition, it provides significant elements of reflection about a number of burning issues: The development of a new profile of the “teacher” in a studentcentred system and the implementation system to be followed, the importance of multiple but integrated learning paths and the relevance as well as the reflection on real cases of competence evaluation.

Category structure determines the relative attractiveness of global versus local averages.

Science.gov (United States)

Vogel, Tobias; Carr, Evan W; Davis, Tyler; Winkielman, Piotr

2018-02-01

Stimuli that capture the central tendency of presented exemplars are often preferred-a phenomenon also known as the classic beauty-in-averageness effect . However, recent studies have shown that this effect can reverse under certain conditions. We propose that a key variable for such ugliness-in-averageness effects is the category structure of the presented exemplars. When exemplars cluster into multiple subcategories, the global average should no longer reflect the underlying stimulus distributions, and will thereby become unattractive. In contrast, the subcategory averages (i.e., local averages) should better reflect the stimulus distributions, and become more attractive. In 3 studies, we presented participants with dot patterns belonging to 2 different subcategories. Importantly, across studies, we also manipulated the distinctiveness of the subcategories. We found that participants preferred the local averages over the global average when they first learned to classify the patterns into 2 different subcategories in a contrastive categorization paradigm (Experiment 1). Moreover, participants still preferred local averages when first classifying patterns into a single category (Experiment 2) or when not classifying patterns at all during incidental learning (Experiment 3), as long as the subcategories were sufficiently distinct. Finally, as a proof-of-concept, we mapped our empirical results onto predictions generated by a well-known computational model of category learning (the Generalized Context Model [GCM]). Overall, our findings emphasize the key role of categorization for understanding the nature of preferences, including any effects that emerge from stimulus averaging. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Design demonstrations for category B tank systems at Oak Ridge National Laboratory, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1994-11-01

This document presents design demonstrations conducted of liquid low-level waste (LLLW) storage tank systems located at the Oak Ridge National Laboratory (ORNL). Demonstration of the design of these tank systems has been stipulated by the Federal Facility Agreement (FFA) between the US Environmental Protection Agency (EPA)-Region IV; the Tennessee Department of Environment and Conservation (TDEC); and the DOE. The FFA establishes four categories of tanks. These are: Category A -- New or replacement tank systems with secondary containment; Category B -- Existing tank systems with secondary containment; Category C -- Existing tank systems without secondary containment; Category D -- Existing tank systems without secondary containment that are removed from service. This document provides a design demonstration of the secondary containment and ancillary equipment of 11 tank systems listed in the FFA as Category B. The design demonstration for each tank is presented.

Design demonstrations for category B tank systems at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1994-11-01

This document presents design demonstrations conducted of liquid low-level waste (LLLW) storage tank systems located at the Oak Ridge National Laboratory (ORNL). Demonstration of the design of these tank systems has been stipulated by the Federal Facility Agreement (FFA) between the US Environmental Protection Agency (EPA)-Region IV; the Tennessee Department of Environment and Conservation (TDEC); and the DOE. The FFA establishes four categories of tanks. These are: Category A -- New or replacement tank systems with secondary containment; Category B -- Existing tank systems with secondary containment; Category C -- Existing tank systems without secondary containment; Category D -- Existing tank systems without secondary containment that are removed from service. This document provides a design demonstration of the secondary containment and ancillary equipment of 11 tank systems listed in the FFA as Category B. The design demonstration for each tank is presented

Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems

NARCIS (Netherlands)

Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels

Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem.

The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

Science.gov (United States)

Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

2014-01-01

In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

The semantic category-based grouping in the Multiple Identity Tracking task.

Science.gov (United States)

Wei, Liuqing; Zhang, Xuemin; Li, Zhen; Liu, Jingyao

2018-01-01

In the Multiple Identity Tracking (MIT) task, categorical distinctions between targets and distractors have been found to facilitate tracking (Wei, Zhang, Lyu, & Li in Frontiers in Psychology, 7, 589, 2016). The purpose of this study was to further investigate the reasons for the facilitation effect, through six experiments. The results of Experiments 1-3 excluded the potential explanations of visual distinctiveness, attentional distribution strategy, and a working memory mechanism, respectively. When objects' visual information was preserved and categorical information was removed, the facilitation effect disappeared, suggesting that the visual distinctiveness between targets and distractors was not the main reason for the facilitation effect. Moreover, the facilitation effect was not the result of strategically shifting the attentional distribution, because the targets received more attention than the distractors in all conditions. Additionally, the facilitation effect did not come about because the identities of targets were encoded and stored in visual working memory to assist in the recovery from tracking errors; when working memory was disturbed by the object identities changing during tracking, the facilitation effect still existed. Experiments 4 and 5 showed that observers grouped targets together and segregated them from distractors on the basis of their categorical information. By doing this, observers could largely avoid distractor interference with tracking and improve tracking performance. Finally, Experiment 6 indicated that category-based grouping is not an automatic, but a goal-directed and effortful, strategy. In summary, the present findings show that a semantic category-based target-grouping mechanism exists in the MIT task, which is likely to be the major reason for the tracking facilitation effect.

Learnable Classes of Categorial Grammars.

Science.gov (United States)

Kanazawa, Makoto

Learnability theory is an attempt to illuminate the concept of learnability using a mathematical model of learning. Two models of learning of categorial grammars are examined here: the standard model, in which sentences presented to the learner are flat strings of words, and one in which sentences are presented in the form of functor-argument…

Handling multiple metadata streams regarding digital learning material

NARCIS (Netherlands)

Roes, J.B.M.; Vuuren, J. van; Verbeij, N.; Nijstad, H.

2010-01-01

This paper presents the outcome of a study performed in the Netherlands on handling multiple metadata streams regarding digital learning material. The paper describes the present metadata architecture in the Netherlands, the present suppliers and users of metadata and digital learning materials. It

Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

OpenAIRE

Thaut, Michael H.; Peterson, David A.; McIntosh, Gerald C.; Hoemberg, Volker

2014-01-01

Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during ...

Using reusable learning objects (rlos) in injection skills teaching: Evaluations from multiple user types.

Science.gov (United States)

Williams, Julia; O'Connor, Mórna; Windle, Richard; Wharrad, Heather J

2015-12-01

Clinical skills are a critical component of pre-registration nurse education in the United Kingdom, yet there is widespread concern about the clinical skills displayed by newly-qualified nurses. Novel means of supporting clinical skills education are required to address this. A package of Reusable Learning Objects (RLOs) was developed to supplement pre-registration teaching on the clinical skill of administering injection medication. RLOs are electronic resources addressing a single learning objective whose interactivity facilitates learning. This article evaluates a package of five injection RLOs across three studies: (1) questionnaires administered to pre-registration nursing students at University of Nottingham (UoN) (n=46) evaluating the RLO package as a whole; (2) individual RLOs evaluated in online questionnaires by educators and students from UoN; from other national and international institutions; and healthcare professionals (n=265); (3) qualitative evaluation of the RLO package by UoN injection skills tutors (n=6). Data from all studies were assessed for (1) access to, (2) usefulness, (3) impact and (4) integration of the RLOs. Study one found that pre-registration nursing students rate the RLO package highly across all categories, particularly underscoring the value of their self-test elements. Study two found high ratings in online assessments of individual RLOs by multiple users. The global reach is particularly encouraging here. Tutors reported insufficient levels of student-RLO access, which might be explained by the timing of their student exposure. Tutors integrate RLOs into teaching and agree on their use as teaching supplements, not substitutes for face-to-face education. This evaluation encompasses the first years postpackage release. Encouraging data on evaluative categories in this early review suggest that future evaluations are warranted to track progress as the package is adopted and evaluated more widely. Copyright © 2015 Elsevier Ltd

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Science.gov (United States)

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Ling-Yu Duan

2010-01-01

Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Per-Sample Multiple Kernel Approach for Visual Concept Learning

Directory of Open Access Journals (Sweden)

Tian Yonghong

2010-01-01

Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

Multiplicity in public health supply systems: a learning agenda.

Science.gov (United States)

Bornbusch, Alan; Bates, James

2013-08-01

Supply chain integration-merging products for health programs into a single supply chain-tends to be the dominant model in health sector reform. However, multiplicity in a supply system may be justified as a risk management strategy that can better ensure product availability, advance specific health program objectives, and increase efficiency.

Implementation of Multiple Intelligences Supported Project-Based Learning in EFL/ESL Classrooms

Science.gov (United States)

Bas, Gokhan

2008-01-01

This article deals with the implementation of Multiple Intelligences supported Project-Based learning in EFL/ESL Classrooms. In this study, after Multiple Intelligences supported Project-based learning was presented shortly, the implementation of this learning method into English classrooms. Implementation process of MI supported Project-based…

Specialized motor-driven dusp1 expression in the song systems of multiple lineages of vocal learning birds.

Directory of Open Access Journals (Sweden)

Haruhito Horita

Full Text Available Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1 was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits.

Stress Effects on Multiple Memory System Interactions

OpenAIRE

Ness, Deborah; Calabrese, Pasquale

2016-01-01

Extensive behavioural, pharmacological, and neurological research reports stress effects on mammalian memory processes. While stress effects on memory quantity have been known for decades, the influence of stress on multiple memory systems and their distinct contributions to the learning process have only recently been described. In this paper, after summarizing the fundamental biological aspects of stress/emotional arousal and recapitulating functionally and anatomically distinct memory syst...

The Role of a Commander in Military Lessons Learned Systems

Directory of Open Access Journals (Sweden)

Zenon Waliński

2015-06-01

Full Text Available The aim of the paper is to investigate the role of a commander in military Lessons Learned systems. In order to achieve the aim, the paper presents (1 the architecture of the Lessons Learned capabilities in the U.S. Army, NATO and the Polish Armed Forces, (2 the commander’s role in the Lessons Learned process (3 the commander’s role in fostering Lessons Learned organisation culture. The paper is based on multiple case study analysis including Lessons Learned systems in NATO, the U.S. Army and the Polish Armed Forces.

Statistical learning is constrained to less abstract patterns in complex sensory input (but not the least).

Science.gov (United States)

Emberson, Lauren L; Rubinstein, Dani Y

2016-08-01

objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. Copyright © 2016 Elsevier B.V. All rights reserved.

A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

NARCIS (Netherlands)

Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

2009-01-01

Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

Stress and the engagement of multiple memory systems: integration of animal and human studies.

Science.gov (United States)

Schwabe, Lars

2013-11-01

Learning and memory can be controlled by distinct memory systems. How these systems are coordinated to optimize learning and behavior has long been unclear. Accumulating evidence indicates that stress may modulate the engagement of multiple memory systems. In particular, rodent and human studies demonstrate that stress facilitates dorsal striatum-dependent "habit" memory, at the expense of hippocampus-dependent "cognitive" memory. Based on these data, a model is proposed which states that the impact of stress on the relative use of multiple memory systems is due to (i) differential effects of hormones and neurotransmitters that are released during stressful events on hippocampal and dorsal striatal memory systems, thus changing the relative strength of and the interactions between these systems, and (ii) a modulatory influence of the amygdala which biases learning toward dorsal striatum-based memory after stress. This shift to habit memory after stress can be adaptive with respect to current performance but might contribute to psychopathology in vulnerable individuals. Copyright © 2013 Wiley Periodicals, Inc.

Student and Staff Perceptions of a Learning Management System for Blended Learning in Teacher Education

Science.gov (United States)

Holmes, Kathryn A.; Prieto-Rodriguez, Elena

2018-01-01

Higher education institutions routinely use Learning Management Systems (LMS) for multiple purposes; to organise coursework and assessment, to facilitate staff and student interactions, and to act as repositories of learning objects. The analysis reported here involves staff (n = 46) and student (n = 470) responses to surveys as well as data…

A Methodology for Multiple Rule System Integration and Resolution Within a Singular Knowledge Base

Science.gov (United States)

Kautzmann, Frank N., III

1988-01-01

Expert Systems which support knowledge representation by qualitative modeling techniques experience problems, when called upon to support integrated views embodying description and explanation, especially when other factors such as multiple causality, competing rule model resolution, and multiple uses of knowledge representation are included. A series of prototypes are being developed to demonstrate the feasibility of automating the process of systems engineering, design and configuration, and diagnosis and fault management. A study involves not only a generic knowledge representation; it must also support multiple views at varying levels of description and interaction between physical elements, systems, and subsystems. Moreover, it will involve models of description and explanation for each level. This multiple model feature requires the development of control methods between rule systems and heuristics on a meta-level for each expert system involved in an integrated and larger class of expert system. The broadest possible category of interacting expert systems is described along with a general methodology for the knowledge representation and control of mutually exclusive rule systems.

Design of pulse waveform for waveform division multiple access UWB wireless communication system.

Science.gov (United States)

Yin, Zhendong; Wang, Zhirui; Liu, Xiaohui; Wu, Zhilu

2014-01-01

A new multiple access scheme, Waveform Division Multiple Access (WDMA) based on the orthogonal wavelet function, is presented. After studying the correlation properties of different categories of single wavelet functions, the one with the best correlation property will be chosen as the foundation for combined waveform. In the communication system, each user is assigned to different combined orthogonal waveform. Demonstrated by simulation, combined waveform is more suitable than single wavelet function to be a communication medium in WDMA system. Due to the excellent orthogonality, the bit error rate (BER) of multiuser with combined waveforms is so close to that of single user in a synchronous system. That is to say, the multiple access interference (MAI) is almost eliminated. Furthermore, even in an asynchronous system without multiuser detection after matched filters, the result is still pretty ideal and satisfactory by using the third combination mode that will be mentioned in the study.

Understanding nomadic collaborative learning groups

DEFF Research Database (Denmark)

Ryberg, Thomas; Davidsen, Jacob; Hodgson, Vivien

2018-01-01

-term collaborations within the frame of Problem and Project Based Learning. By analysing the patterns of nomadic collaborative learning we identify and discuss how the two groups of students incorporate mobile and digital technologies as well as physical and/or non-digital technologies into their group work......The paper builds on the work of Rossitto et al. on collaborative nomadic work to develop three categories of practice of nomadic collaborative learning groups. Our study is based on interviews, workshops and observations of two undergraduate student's group practices engaged in self-organised, long....... Specifically, we identify the following categories of nomadic collaborative learning practices: “orchestration of work phases, spaces and activities,” “the orchestration of multiple technologies” and “orchestration of togetherness.” We found that for both groups of students there was a fluidity, situatedness...

Multiple goals, motivation and academic learning.

Science.gov (United States)

Valle, Antonio; Cabanach, Ramón G; Núnez, José C; González-Pienda, Julio; Rodríguez, Susana; Piñeiro, Isabel

2003-03-01

The type of academic goals pursued by students is one of the most important variables in motivational research in educational contexts. Although motivational theory and research have emphasised the somewhat exclusive nature of two types of goal orientation (learning goals versus performance goals), some studies (Meece, 1994; Seifert, 1995, 1996) have shown that the two kinds of goals are relatively complementary and that it is possible for students to have multiple goals simultaneously, which guarantees some flexibility to adapt more efficaciously to various contexts and learning situations. The principal aim of this study is to determine the academic goals pursued by university students and to analyse the differences in several very significant variables related to motivation and academic learning. Participants were 609 university students (74% women and 26% men) who filled in several questionnaires about the variables under study. We used cluster analysis ('quick cluster analysis' method) to establish the different groups or clusters of individuals as a function of the three types of goals (learning goals, performance goals, and social reinforcement goals). By means of MANOVA, we determined whether the groups or clusters identified were significantly different in the variables that are relevant to motivation and academic learning. Lastly, we performed ANOVA on the variables that revealed significant effects in the previous analysis. Using cluster analysis, three groups of students with different motivational orientations were identified: a group with predominance of performance goals (Group PG: n = 230), a group with predominance of multiple goals (Group MG: n = 238), and a group with predominance of learning goals (Group LG: n = 141). Groups MG and LG attributed their success more to ability, they had higher perceived ability, they took task characteristics into account when planning which strategies to use in the learning process, they showed higher persistence

Eliciting design patterns for e-learning systems

Science.gov (United States)

Retalis, Symeon; Georgiakakis, Petros; Dimitriadis, Yannis

2006-06-01

Design pattern creation, especially in the e-learning domain, is a highly complex process that has not been sufficiently studied and formalized. In this paper, we propose a systematic pattern development cycle, whose most important aspects focus on reverse engineering of existing systems in order to elicit features that are cross-validated through the use of appropriate, authentic scenarios. However, an iterative pattern process is proposed that takes advantage of multiple data sources, thus emphasizing a holistic view of the teaching learning processes. The proposed schema of pattern mining has been extensively validated for Asynchronous Network Supported Collaborative Learning (ANSCL) systems, as well as for other types of tools in a variety of scenarios, with promising results.

Learning Multiplication Using Indonesian Traditional game in Third Grade

Directory of Open Access Journals (Sweden)

Rully Charitas Indra Prahmana

2012-07-01

Full Text Available Several previous researches showed that students had difficulty inunderstanding the basic concept of multiplication. Students are morelikely to be introduced by using formula without involving the conceptitself. This underlies the researcher to design a learning trajectory oflearning multiplication using Permainan Tradisional Tepuk Bergambar(PT2B as a context based on the student experience. The purpose ofthis research is to look at the role of PT2B in helping students'understanding in learning multiplication, which evolved from theinformal to formal level in third grade with Pendidikan MatematikaRealistik Indonesia (PMRI approach. The method used is designresearch starting from preliminary design, teaching experiments, andretrospective analysis. This research describes how PT2B make a realcontribution to the third grade students of SDN 179 Palembang tounderstand the concept of multiplication. The results showed PT2Bcontext can stimulate students to understand their knowledge of themultiplication concept. The whole strategy and model that studentsdiscover, describe, and discuss shows how the students construction orcontribution can uses to help their initial understanding of that concept.The stages in the learning trajectory of student have an important rolein understanding the concept of the operation number from informal tothe formal level.Keyword: Design Research, PMRI, Multiplication, Permainan TradisionalTepuk Bergambar DOI: http://dx.doi.org/10.22342/jme.3.2.1931.115-132

Design demonstrations for the remaining 19 Category B tank systems at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1995-01-01

This document presents design demonstrations conducted of liquid low-level waste (LLLW) storage tank systems located at the Oak Ridge National Laboratory (ORNL). Demonstration of the design of these tank systems has been stipulated by the Federal Facility Agreement (FFA) between the US Environmental Protection Agency (EPA)--Region IV; the Tennessee Department of Environment and Conservation (TDEC); and the DOE. The FFA establishes four categories of tank systems: Category A--New or Replacement Tank Systems with Secondary Containment; Category B--Existing Tank Systems with Secondary Containment; Category C--Existing Tank Systems Without Secondary Containment; and Category D--Existing Tank Systems Without Secondary Containment That are Removed from Service. This document provides a design demonstration of the secondary containment and ancillary equipment of 19 tank systems listed in the FFA as Category B. Three tank systems originally designated as Category B have been redesignated as Category C and one tank system originally designated as Category B has been redesignated as Category D. The design demonstration for each tank is presented in Section 2. The design demonstrations were developed using information obtained from the design drawings (as-built when available), construction specifications, and interviews with facility operators. The assessments assume that each tank system was constructed in accordance with the design drawings and construction specifications for that system unless specified otherwise. Each design demonstration addresses system conformance to the requirements of the FFA

Cross-Platform Learning: On the Nature of Children's Learning from Multiple Media Platforms

Science.gov (United States)

Fisch, Shalom M.

2013-01-01

It is increasingly common for an educational media project to span several media platforms (e.g., TV, Web, hands-on materials), assuming that the benefits of learning from multiple media extend beyond those gained from one medium alone. Yet research typically has investigated learning from a single medium in isolation. This paper reviews several…

The use of Multiple Representations to Enhance Student Mental Model Development of a Complex Earth System in an Introductory Geoscience Course

Science.gov (United States)

Sell, K. S.; Heather, M. R.; Herbert, B. E.

2004-12-01

Exposing earth system science (ESS) concepts into introductory geoscience courses may present new and unique cognitive learning issues for students including understanding the role of positive and negative feedbacks in system responses to perturbations, spatial heterogeneity, and temporal dynamics, especially when systems exhibit complex behavior. Implicit learning goals of typical introductory undergraduate geoscience courses are more focused on building skill-sets and didactic knowledge in learners than developing a deeper understanding of the dynamics and processes of complex earth systems through authentic inquiry. Didactic teaching coupled with summative assessment of factual knowledge tends to limit student¡¦s understanding of the nature of science, their belief in the relevancy of science to their lives, and encourages memorization and regurgitation; this is especially true among the non-science majors who compose the majority of students in introductory courses within the large university setting. Students organize scientific knowledge and reason about earth systems by manipulating internally constructed mental models. This pilot study focuses on characterizing the impact of inquiry-based learning with multiple representations to foster critical thinking and mental model development about authentic environmental issues of coastal systems in an introductory geoscience course. The research was conducted in nine introductory physical geology laboratory sections (N ˜ 150) at Texas A&M University as part of research connected with the Information Technology in Science (ITS) Center. Participants were randomly placed into experimental and control groups. Experimental groups were exposed to multiple representations including both web-based learning materials (i.e. technology-supported visualizations and analysis of multiple datasets) and physical models, whereas control groups were provided with the traditional ¡workbook style¡" laboratory assignments

Training Lp norm multiple kernel learning in the primal.

Science.gov (United States)

Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei

2013-10-01

Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2013 Elsevier Ltd. All rights reserved.

A presentation system for just-in-time learning in radiology.

Science.gov (United States)

Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

2007-03-01

There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

Perceptual-motor skill learning in Gilles de la Tourette syndrome. Evidence for multiple procedural learning and memory systems.

Science.gov (United States)

Marsh, Rachel; Alexander, Gerianne M; Packard, Mark G; Zhu, Hongtu; Peterson, Bradley S

2005-01-01

Procedural learning and memory systems likely comprise several skills that are differentially affected by various illnesses of the central nervous system, suggesting their relative functional independence and reliance on differing neural circuits. Gilles de la Tourette syndrome (GTS) is a movement disorder that involves disturbances in the structure and function of the striatum and related circuitry. Recent studies suggest that patients with GTS are impaired in performance of a probabilistic classification task that putatively involves the acquisition of stimulus-response (S-R)-based habits. Assessing the learning of perceptual-motor skills and probabilistic classification in the same samples of GTS and healthy control subjects may help to determine whether these various forms of procedural (habit) learning rely on the same or differing neuroanatomical substrates and whether those substrates are differentially affected in persons with GTS. Therefore, we assessed perceptual-motor skill learning using the pursuit-rotor and mirror tracing tasks in 50 patients with GTS and 55 control subjects who had previously been compared at learning a task of probabilistic classifications. The GTS subjects did not differ from the control subjects in performance of either the pursuit rotor or mirror-tracing tasks, although they were significantly impaired in the acquisition of a probabilistic classification task. In addition, learning on the perceptual-motor tasks was not correlated with habit learning on the classification task in either the GTS or healthy control subjects. These findings suggest that the differing forms of procedural learning are dissociable both functionally and neuroanatomically. The specific deficits in the probabilistic classification form of habit learning in persons with GTS are likely to be a consequence of disturbances in specific corticostriatal circuits, but not the same circuits that subserve the perceptual-motor form of habit learning.

Generalized multiple kernel learning with data-dependent priors.

Science.gov (United States)

Mao, Qi; Tsang, Ivor W; Gao, Shenghua; Wang, Li

2015-06-01

Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination with a noninformative prior over multiple views is equivalent to the formulation of MKL. Instead of using the noninformative prior, we introduce a novel data-dependent prior based on an ensemble of kernel predictors, which enhances the prediction performance of MKL by leveraging the merits of the classifier ensemble. With the proposed probabilistic framework of MKL, we propose a hierarchical Bayesian model to learn the proposed data-dependent prior and classification model simultaneously. The resultant problem is convex and other information (e.g., instances with either missing views or missing labels) can be seamlessly incorporated into the data-dependent priors. Furthermore, a variety of existing MKL models can be recovered under the proposed MKL framework and can be readily extended to incorporate these priors. Extensive experiments demonstrate the benefits of our proposed framework in supervised and semisupervised settings, as well as in tasks with partial correspondence among multiple views.

Touching Mercury in Community Media: Identifying Multiple Literacy Learning through Digital Arts Production

Science.gov (United States)

Arndt, Angela E.

2011-01-01

Educational paradigm shifts call for 21st century learners to possess the knowledge, skills, abilities, values, and experiences associated with multiple forms of literacy in a participatory learning culture. Contemporary educational systems are slow to adapt. Outside of school, people have to be self-motivated and have access to resources in order…

Smart-system of distance learning of visually impaired people based on approaches of artificial intelligence

Science.gov (United States)

Samigulina, Galina A.; Shayakhmetova, Assem S.

2016-11-01

Research objective is the creation of intellectual innovative technology and information Smart-system of distance learning for visually impaired people. The organization of the available environment for receiving quality education for visually impaired people, their social adaptation in society are important and topical issues of modern education.The proposed Smart-system of distance learning for visually impaired people can significantly improve the efficiency and quality of education of this category of people. The scientific novelty of proposed Smart-system is using intelligent and statistical methods of processing multi-dimensional data, and taking into account psycho-physiological characteristics of perception and awareness learning information by visually impaired people.

Radiomic modeling of BI-RADS density categories

Science.gov (United States)

Wei, Jun; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Hadjiiski, Lubomir

2017-03-01

Screening mammography is the most effective and low-cost method to date for early cancer detection. Mammographic breast density has been shown to be highly correlated with breast cancer risk. We are developing a radiomic model for BI-RADS density categorization on digital mammography (FFDM) with a supervised machine learning approach. With IRB approval, we retrospectively collected 478 FFDMs from 478 women. As a gold standard, breast density was assessed by an MQSA radiologist based on BI-RADS categories. The raw FFDMs were used for computerized density assessment. The raw FFDM first underwent log-transform to approximate the x-ray sensitometric response, followed by multiscale processing to enhance the fibroglandular densities and parenchymal patterns. Three ROIs were automatically identified based on the keypoint distribution, where the keypoints were obtained as the extrema in the image Gaussian scale-space. A total of 73 features, including intensity and texture features that describe the density and the parenchymal pattern, were extracted from each breast. Our BI-RADS density estimator was constructed by using a random forest classifier. We used a 10-fold cross validation resampling approach to estimate the errors. With the random forest classifier, computerized density categories for 412 of the 478 cases agree with radiologist's assessment (weighted kappa = 0.93). The machine learning method with radiomic features as predictors demonstrated a high accuracy in classifying FFDMs into BI-RADS density categories. Further work is underway to improve our system performance as well as to perform an independent testing using a large unseen FFDM set.

ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics

Science.gov (United States)

Graesser, Arthur C.; Hu, Xiangen; Nye, Benjamin D.; VanLehn, Kurt; Kumar, Rohit; Heffernan, Cristina; Heffernan, Neil; Woolf, Beverly; Olney, Andrew M.; Rus, Vasile; Andrasik, Frank; Pavlik, Philip; Cai, Zhiqiang; Wetzel, Jon; Morgan, Brent; Hampton, Andrew J.; Lippert, Anne M.; Wang, Lijia; Cheng, Qinyu; Vinson, Joseph E.; Kelly, Craig N.; McGlown, Cadarrius; Majmudar, Charvi A.; Morshed, Bashir; Baer, Whitney

2018-01-01

Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics,…

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model.

Science.gov (United States)

Aridhi, Sabeur; Sghaier, Haïtham; Zoghlami, Manel; Maddouri, Mondher; Nguifo, Engelbert Mephu

2016-01-01

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

Simultaneous processing of information on multiple errors in visuomotor learning.

Science.gov (United States)

Kasuga, Shoko; Hirashima, Masaya; Nozaki, Daichi

2013-01-01

The proper association between planned and executed movements is crucial for motor learning because the discrepancies between them drive such learning. Our study explored how this association was determined when a single action caused the movements of multiple visual objects. Participants reached toward a target by moving a cursor, which represented the right hand's position. Once every five to six normal trials, we interleaved either of two kinds of visual perturbation trials: rotation of the cursor by a certain amount (±15°, ±30°, and ±45°) around the starting position (single-cursor condition) or rotation of two cursors by different angles (+15° and -45°, 0° and 30°, etc.) that were presented simultaneously (double-cursor condition). We evaluated the aftereffects of each condition in the subsequent trial. The error sensitivity (ratio of the aftereffect to the imposed visual rotation) in the single-cursor trials decayed with the amount of rotation, indicating that the motor learning system relied to a greater extent on smaller errors. In the double-cursor trials, we obtained a coefficient that represented the degree to which each of the visual rotations contributed to the aftereffects based on the assumption that the observed aftereffects were a result of the weighted summation of the influences of the imposed visual rotations. The decaying pattern according to the amount of rotation was maintained in the coefficient of each imposed visual rotation in the double-cursor trials, but the value was reduced to approximately 40% of the corresponding error sensitivity in the single-cursor trials. We also found a further reduction of the coefficients when three distinct cursors were presented (e.g., -15°, 15°, and 30°). These results indicated that the motor learning system utilized multiple sources of visual error information simultaneously to correct subsequent movement and that a certain averaging mechanism might be at work in the utilization process.

Effects of musicality and motivational orientation on auditory category learning: a test of a regulatory-fit hypothesis.

Science.gov (United States)

McAuley, J Devin; Henry, Molly J; Wedd, Alan; Pleskac, Timothy J; Cesario, Joseph

2012-02-01

Two experiments investigated the effects of musicality and motivational orientation on auditory category learning. In both experiments, participants learned to classify tone stimuli that varied in frequency and duration according to an initially unknown disjunctive rule; feedback involved gaining points for correct responses (a gains reward structure) or losing points for incorrect responses (a losses reward structure). For Experiment 1, participants were told at the start that musicians typically outperform nonmusicians on the task, and then they were asked to identify themselves as either a "musician" or a "nonmusician." For Experiment 2, participants were given either a promotion focus prime (a performance-based opportunity to gain entry into a raffle) or a prevention focus prime (a performance-based criterion that needed to be maintained to avoid losing an entry into a raffle) at the start of the experiment. Consistent with a regulatory-fit hypothesis, self-identified musicians and promotion-primed participants given a gains reward structure made more correct tone classifications and were more likely to discover the optimal disjunctive rule than were musicians and promotion-primed participants experiencing losses. Reward structure (gains vs. losses) had inconsistent effects on the performance of nonmusicians, and a weaker regulatory-fit effect was found for the prevention focus prime. Overall, the findings from this study demonstrate a regulatory-fit effect in the domain of auditory category learning and show that motivational orientation may contribute to musician performance advantages in auditory perception.

Design demonstrations for Category B tank systems at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1992-07-01

This document presents design demonstrations conducted of liquid low-level waste (LLLW) storage tank systems located at the Oak Ridge National Laboratory (ORNL). ORNL has conducted research in energy related fields since 1943. The facilities used to conduct the research include nuclear reactors, chemical pilot plants, research laboratories, radioisotope production laboratories, and support facilities. These facilities have produced a variety of radioactive and/or hazardous wastes. These wastes have been stored and transported through an extensive network of piping and tankage. Demonstration of the design of these tank systems has been stipulated by the Federal Facility Agreement (FFA) between the US Environmental Protection Agency (EPA) - Region IV; the Tennessee Department of Environment and Conservation (TDEC); and the DOE. The FFA establishes four categories of tanks. These are: Category A -- New or Replacement Tank Systems with Secondary Containment; Category B -- Existing Tank Systems with Secondary Containment; Category C -- Existing Tank Systems without Secondary Containment; and Category D -- Existing Tank Systems without Secondary Containment that are; Removed from Service. This document provides a design demonstration of the secondary containment and ancillary equipment of 11 tank systems listed in the FFA as Category ''B.'' The design demonstration for each tank is presented in Section 2. The design demonstrations were developed using information obtained from the design drawings (as-built when available), construction specifications, and interviews with facility operators. The assessments assume that each tank system was constructed in accordance with the design drawings and construction specifications for that system unless specified otherwise. Each design demonstration addresses system conformance to the requirements of the FFA (Appendix F, Subsection C)

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 ...

Basic category theory

CERN Document Server

Leinster, Tom

2014-01-01

At the heart of this short introduction to category theory is the idea of a universal property, important throughout mathematics. After an introductory chapter giving the basic definitions, separate chapters explain three ways of expressing universal properties: via adjoint functors, representable functors, and limits. A final chapter ties all three together. The book is suitable for use in courses or for independent study. Assuming relatively little mathematical background, it is ideal for beginning graduate students or advanced undergraduates learning category theory for the first time. For each new categorical concept, a generous supply of examples is provided, taken from different parts of mathematics. At points where the leap in abstraction is particularly great (such as the Yoneda lemma), the reader will find careful and extensive explanations. Copious exercises are included.

A Multipurpose Interactive System for Promoting and Assessing the Learning of Physics

Science.gov (United States)

Picciarelli, Vittorio; Selvaggi, Giovanna; Stella, Rosa

2013-01-01

This paper presents an interactive system designed to facilitate the effective management of both knowledge consolidation and (self-)assessment of the progress made in the learning of physics by upper secondary school students. Via a specific multiple-choice test database, the system proposes several learning paths designed and implemented in an…

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

Science.gov (United States)

Yuan, Chengzhi; Licht, Stephen; He, Haibo

2017-09-26

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

FINANCIAL CONTROL AS A CATEGORY

Directory of Open Access Journals (Sweden)

Andrey Yu. Volkov

2014-01-01

Full Text Available The article reveals the basics of “financial control” as a category. The main attention is concentrated on the “control” itself (asa term, multiplicity of interpretation of“financial control” term and its juristic-practical matching. The duality of financial control category is detected. The identity of terms “financial control” and “state financial control” is justified. The article also offers ways of development of financial control juristical regulation.

A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

Directory of Open Access Journals (Sweden)

Vivian V. Valentin

2014-07-01

Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

Development of Multiple Thinking and Creativity in Organizational Learning

Science.gov (United States)

Cheng, Yin Cheong

2005-01-01

Purpose: Based on a typology of contextualized multiple thinking, this paper aims to elaborate how the levels of thinking (data, information, knowledge, and intelligence), and the types of thinking as a whole, can be used to profile the characteristics of multiple thinking in organizational learning, re-conceptualize the nature of creativity in…

Remanufacture Systems for Category 1 and 2 Marine Diesel Engines

Science.gov (United States)

EPA maintains a list of remanufacture systems, or “kits”, certified for use with Category 1 and 2 marine diesel engines according to the provisions of 40 CFR Part 1042, Subpart I, and is periodically updated.

Category of Metabolic-Replication Systems in Biology and Medicine

OpenAIRE

I. C. Baianu

2012-01-01

Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs)in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life's origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important questio...

A Conceptual Framework for Error Remediation with Multiple External Representations Applied to Learning Objects

Science.gov (United States)

Leite, Maici Duarte; Marczal, Diego; Pimentel, Andrey Ricardo; Direne, Alexandre Ibrahim

2014-01-01

This paper presents the application of some concepts of Intelligent Tutoring Systems (ITS) to elaborate a conceptual framework that uses the remediation of errors with Multiple External Representations (MERs) in Learning Objects (LO). To this is demonstrated a development of LO for teaching the Pythagorean Theorem through this framework. This…

Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

Science.gov (United States)

Ernst, Benjamin; Steinhauser, Marco

2012-06-01

Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

Grammatical Constructions as Relational Categories.

Science.gov (United States)

Goldwater, Micah B

2017-07-01

This paper argues that grammatical constructions, specifically argument structure constructions that determine the "who did what to whom" part of sentence meaning and how this meaning is expressed syntactically, can be considered a kind of relational category. That is, grammatical constructions are represented as the abstraction of the syntactic and semantic relations of the exemplar utterances that are expressed in that construction, and it enables the generation of novel exemplars. To support this argument, I review evidence that there are parallel behavioral patterns between how children learn relational categories generally and how they learn grammatical constructions specifically. Then, I discuss computational simulations of how grammatical constructions are abstracted from exemplar sentences using a domain-general relational cognitive architecture. Last, I review evidence from adult language processing that shows parallel behavioral patterns with expert behavior from other cognitive domains. After reviewing the evidence, I consider how to integrate this account with other theories of language development. Copyright © 2017 Cognitive Science Society, Inc.

Learning Companion Systems, Social Learning Systems, and the Global Social Learning Club.

Science.gov (United States)

Chan, Tak-Wai

1996-01-01

Describes the development of learning companion systems and their contributions to the class of social learning systems that integrate artificial intelligence agents and use machine learning to tutor and interact with students. Outlines initial social learning projects, their programming languages, and weakness. Future improvements will include…

Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry.

Science.gov (United States)

Pham-The, Hai; Nam, Nguyen-Hai; Nga, Doan-Viet; Hai, Dang Thanh; Dieguez-Santana, Karel; Marrero-Poncee, Yovani; Castillo-Garit, Juan A; Casanola-Martin, Gerardo M; Le-Thi-Thu, Huong

2018-02-09

Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In this paper, we presented MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrated our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data

OpenAIRE

Mild, Andreas; Reutterer, Thomas

2002-01-01

Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This pape...

Age-related difference in the effective neural connectivity associated with probabilistic category learning

International Nuclear Information System (INIS)

Yoon, Eun Jin; Cho, Sang Soo; Kim, Hee Jung; Bang, Seong Ae; Park, Hyun Soo; Kim, Yu Kyeong; Kim, Sang Eun

2007-01-01

Although it is well known that explicit memory is affected by the deleterious changes in brain with aging, but effect of aging in implicit memory such as probabilistic category learning (PCL) is not clear. To identify the effect of aging on the neural interaction for successful PCL, we investigated the neural substrates of PCL and the age-related changes of the neural network between these brain regions. 23 young (age, 252 y; 11 males) and 14 elderly (673 y; 7 males) healthy subjects underwent FDG PET during a resting state and 150-trial weather prediction (WP) task. Correlations between the WP hit rates and regional glucose metabolism were assessed using SPM2 (P diff (37) = 142.47, P<0.005), Systematic comparisons of each path revealed that frontal crosscallosal and the frontal to parahippocampal connection were most responsible for the model differences (P<0.05). For the successful PCL, the elderly recruits the basal ganglia implicit memory system but MTL recruitment differs from the young. The inadequate MTL correlation pattern in the elderly is may be caused by the changes of the neural pathway related with explicit memory. These neural changes can explain the decreased performance of PCL in elderly subjects

musical mnemonics aid verbal memory and induce learning related brain plasticity in multiple sclerosis

Directory of Open Access Journals (Sweden)

Michael eThaut

2014-06-01

Full Text Available Recent research in music and brain function has suggested that the temporal pattern structure in music andrhythm can enhance cognitive functions. To further elucidate this question specifically for memory weinvestigated if a musical template can enhance verbal learning in patients with multiple sclerosis and ifmusic assisted learning will also influence short-term, system-level brain plasticity. We measuredsystems-level brain activity with oscillatory network synchronization during music assisted learning.Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG in alpha andbeta frequency bands in 54 patients with multiple sclerosis (MS. The study sample was randomlydivided into 2 groups, either hearing a spoken or musical (sung presentation of Rey’s Auditory VerbalLearning Test (RAVLT. We defined the learning-related synchronization (LRS as the percent changein EEG spectral power from the first time the word was presented to the average of the subsequent wordencoding trials. LRS differed significantly between the music and spoken conditions in low alpha andupper beta bands. Patients in the music condition showed overall better word memory and better wordorder memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. Theevidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization inprefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicitin musical stimuli enhances ‘deep encoding’ during verbal learning and sharpens the timing of neuraldynamics in brain networks degraded by demyelination in MS

Unsupervised multiple kernel learning for heterogeneous data integration.

Science.gov (United States)

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Computing color categories

NARCIS (Netherlands)

Yendrikhovskij, S.N.; Rogowitz, B.E.; Pappas, T.N.

2000-01-01

This paper is an attempt to develop a coherent framework for understanding, modeling, and computing color categories. The main assumption is that the structure of color category systems originates from the statistical structure of the perceived color environment. This environment can be modeled as

Discovery learning with SAVI approach in geometry learning

Science.gov (United States)

Sahara, R.; Mardiyana; Saputro, D. R. S.

2018-05-01

Geometry is one branch of mathematics that an important role in learning mathematics in the schools. This research aims to find out about Discovery Learning with SAVI approach to achievement of learning geometry. This research was conducted at Junior High School in Surakarta city. Research data were obtained through test and questionnaire. Furthermore, the data was analyzed by using two-way Anova. The results showed that Discovery Learning with SAVI approach gives a positive influence on mathematics learning achievement. Discovery Learning with SAVI approach provides better mathematics learning outcomes than direct learning. In addition, students with high self-efficacy categories have better mathematics learning achievement than those with moderate and low self-efficacy categories, while student with moderate self-efficacy categories are better mathematics learning achievers than students with low self-efficacy categories. There is an interaction between Discovery Learning with SAVI approach and self-efficacy toward student's mathematics learning achievement. Therefore, Discovery Learning with SAVI approach can improve mathematics learning achievement.

Perceptual Learning of Intonation Contour Categories in Adults and 9- to 11-Year-Old Children: Adults Are More Narrow-Minded

Science.gov (United States)

Kapatsinski, Vsevolod; Olejarczuk, Paul; Redford, Melissa A.

2017-01-01

We report on rapid perceptual learning of intonation contour categories in adults and 9- to 11-year-old children. Intonation contours are temporally extended patterns, whose perception requires temporal integration and therefore poses significant working memory challenges. Both children and adults form relatively abstract representations of…

The Effectiveness of the Gesture-Based Learning System (GBLS and Its Impact on Learning Experience

Directory of Open Access Journals (Sweden)

Moamer Ali Shakroum

2016-06-01

Full Text Available Several studies and experiments have been conducted in recent years to examine the value and the advantage of using the Gesture-Based Learning System (GBLS.The investigation of the influence of the GBLS mode on the learning outcomes is still scarce. Most previous studies did not address more than one category of learning outcomes (cognitive, affective outcomes, etc. at the same time when used to understand the impact of GBLS. Moreover, none of these studies considered the difference in students’ characteristics such as learning styles and spatial abilities. Therefore, a comprehensive empirical research on the impact of the GBLS mode on learning outcomes is needed. The purpose of this paper is to fill in the gap and to investigate the effectiveness of the GBLS mode on learning using Technology Mediated Learning (TML models. This study revealed that the GBLS mode has greater positive impact on students’ learning outcomes (cognitive and affective outcomes when compared with other two learning modes that are classified as Computer Simulation Software Learning (CSSL mode and conventional learning mode. In addition, this study also found that the GBLS mode is capable of serving all students with different learning styles and spatial ability levels. The results of this study revealed that the GBLS mode outperformed the existing learning methods by providing a unique learning experience that considers the differences between students. The results have also shown that the Kinect user interface can create an interactive and an enjoyable learning experience.

Skew category algebras associated with partially defined dynamical systems

DEFF Research Database (Denmark)

Lundström, Patrik; Öinert, Per Johan

2012-01-01

We introduce partially defined dynamical systems defined on a topological space. To each such system we associate a functor s from a category G to Topop and show that it defines what we call a skew category algebra A ⋊σ G. We study the connection between topological freeness of s and, on the one...... hand, ideal properties of A ⋊σ G and, on the other hand, maximal commutativity of A in A ⋊σ G. In particular, we show that if G is a groupoid and for each e ∈ ob(G) the group of all morphisms e → e is countable and the topological space s(e) is Tychonoff and Baire. Then the following assertions...... are equivalent: (i) s is topologically free; (ii) A has the ideal intersection property, i.e. if I is a nonzero ideal of A ⋊σ G, then I ∩ A ≠ {0}; (iii) the ring A is a maximal abelian complex subalgebra of A ⋊σ G. Thereby, we generalize a result by Svensson, Silvestrov and de Jeu from the additive group...

Deep-learning-based classification of FDG-PET data for Alzheimer's disease categories

Science.gov (United States)

Singh, Shibani; Srivastava, Anant; Mi, Liang; Caselli, Richard J.; Chen, Kewei; Goradia, Dhruman; Reiman, Eric M.; Wang, Yalin

2017-11-01

Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic Alzheimer's disease (AD) patients. PET scans provide functional information that is unique and unavailable using other types of imaging. However, the computational efficacy of FDG-PET data alone, for the classification of various Alzheimers Diagnostic categories, has not been well studied. This motivates us to correctly discriminate various AD Diagnostic categories using FDG-PET data. Deep learning has improved state-of-the-art classification accuracies in the areas of speech, signal, image, video, text mining and recognition. We propose novel methods that involve probabilistic principal component analysis on max-pooled data and mean-pooled data for dimensionality reduction, and multilayer feed forward neural network which performs binary classification. Our experimental dataset consists of baseline data of subjects including 186 cognitively unimpaired (CU) subjects, 336 mild cognitive impairment (MCI) subjects with 158 Late MCI and 178 Early MCI, and 146 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We measured F1-measure, precision, recall, negative and positive predictive values with a 10-fold cross validation scheme. Our results indicate that our designed classifiers achieve competitive results while max pooling achieves better classification performance compared to mean-pooled features. Our deep model based research may advance FDG-PET analysis by demonstrating their potential as an effective imaging biomarker of AD.

Right away: A late, right-lateralized category effect complements an early, left-lateralized category effect in visual search.

Science.gov (United States)

Constable, Merryn D; Becker, Stefanie I

2017-10-01

According to the Sapir-Whorf hypothesis, learned semantic categories can influence early perceptual processes. A central finding in support of this view is the lateralized category effect-namely, the finding that categorically different colors (e.g., blue and green hues) can be discriminated faster than colors within the same color category (e.g., different hues of green), especially when they are presented in the right visual field. Because the right visual field projects to the left hemisphere, this finding has been popularly couched in terms of the left-lateralization of language. However, other studies have reported bilateral category effects, which has led some researchers to question the linguistic origins of the effect. Here we examined the time course of lateralized and bilateral category effects in the classical visual search paradigm by means of eyetracking and RT distribution analyses. Our results show a bilateral category effect in the manual responses, which is combined of an early, left-lateralized category effect and a later, right-lateralized category effect. The newly discovered late, right-lateralized category effect occurred only when observers had difficulty locating the target, indicating a specialization of the right hemisphere to find categorically different targets after an initial error. The finding that early and late stages of visual search show different lateralized category effects can explain a wide range of previously discrepant findings.

Optimizing Multiple-Choice Tests as Learning Events

Science.gov (United States)

Little, Jeri Lynn

2011-01-01

Although generally used for assessment, tests can also serve as tools for learning--but different test formats may not be equally beneficial. Specifically, research has shown multiple-choice tests to be less effective than cued-recall tests in improving the later retention of the tested information (e.g., see meta-analysis by Hamaker, 1986),…

Learning to Control Advanced Life Support Systems

Science.gov (United States)

Subramanian, Devika

2004-01-01

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges. In particular, advanced life support systems are nonlinear coupled dynamical systems and it is difficult for humans to take all interactions into account to design an effective control strategy. In this project. we developed several reinforcement learning controllers that actively explore the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated these controllers using a discrete event simulation of an advanced life support system. This simulation, called BioSim, designed by Nasa scientists David Kortenkamp and Scott Bell has multiple, interacting life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. They are implemented in a consumer/producer relationship in which certain modules produce resources that are consumed by other modules. Stores hold resources between modules. Control of this simulation is via adjusting flows of resources between modules and into/out of stores. We developed adaptive algorithms that control the flow of resources in BioSim. Our learning algorithms discovered several ingenious strategies for maximizing mission length by controlling the air and water recycling systems as well as crop planting schedules. By exploiting non-linearities in the overall system dynamics, the learned controllers easily out- performed controllers written by human experts. In sum, we accomplished three goals. We (1) developed foundations for learning models of coupled dynamical systems by active exploration of the state space, (2) developed and tested algorithms that learn to efficiently control air and water recycling processes as well as crop scheduling in Biosim, and (3) developed an understanding of the role machine learning in designing control systems for

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm.

Science.gov (United States)

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

Learning foreign sounds in an alien world: videogame training improves non-native speech categorization.

Science.gov (United States)

Lim, Sung-joo; Holt, Lori L

2011-01-01

Although speech categories are defined by multiple acoustic dimensions, some are perceptually weighted more than others and there are residual effects of native-language weightings in non-native speech perception. Recent research on nonlinguistic sound category learning suggests that the distribution characteristics of experienced sounds influence perceptual cue weights: Increasing variability across a dimension leads listeners to rely upon it less in subsequent category learning (Holt & Lotto, 2006). The present experiment investigated the implications of this among native Japanese learning English /r/-/l/ categories. Training was accomplished using a videogame paradigm that emphasizes associations among sound categories, visual information, and players' responses to videogame characters rather than overt categorization or explicit feedback. Subjects who played the game for 2.5h across 5 days exhibited improvements in /r/-/l/ perception on par with 2-4 weeks of explicit categorization training in previous research and exhibited a shift toward more native-like perceptual cue weights. Copyright © 2011 Cognitive Science Society, Inc.

Using mini-games for learning multiplication and division: A longitudinal effect study

NARCIS (Netherlands)

Bakker, M.

2014-01-01

This thesis reports the findings of a three-year longitudinal research project set up to investigate the effectiveness of employing online mini-games for the learning of multiplication and division, including multiplicative fact knowledge (declarative knowledge), multiplicative operation skills

General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning.

Science.gov (United States)

Chung, Wooyong; Kim, Jisu; Lee, Heejin; Kim, Euntai

2015-11-01

Support vector regression has been considered as one of the most important regression or function approximation methodologies in a variety of fields. In this paper, two new general dimensional multiple output support vector regressions (MSVRs) named SOCPL1 and SOCPL2 are proposed. The proposed methods are formulated in the dual space and their relationship with the previous works is clearly investigated. Further, the proposed MSVRs are extended into the multiple kernel learning and their training is implemented by the off-the-shelf convex optimization tools. The proposed MSVRs are applied to benchmark problems and their performances are compared with those of the previous methods in the experimental section.

A Survey Of Top 10 Open Source Learning Management Systems

Directory of Open Access Journals (Sweden)

Mohamed R. Elabnody

2015-08-01

Full Text Available Open Source LMSs are fully flexible and customizable so they can be designed in line with your schoolorganizations brand image. Open Source LMSs can also be converted to social learning platforms. You can create an online community through your LMS. This paper describes the most important features in learning management systems LMS that are critical to compare and contrast depend on your system requirements. Also represents a multiple LMS providers that are designed to use in university environment.

Working memory supports inference learning just like classification learning.

Science.gov (United States)

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

Learning about the scale of the solar system using digital planetarium visualizations

Science.gov (United States)

Yu, Ka Chun; Sahami, Kamran; Dove, James

2017-07-01

We studied the use of a digital planetarium for teaching relative distances and sizes in introductory undergraduate astronomy classes. Inspired in part by the classic short film The Powers of Ten and large physical scale models of the Solar System that can be explored on foot, we created lectures using virtual versions of these two pedagogical approaches for classes that saw either an immersive treatment in the planetarium or a non-immersive version in the regular classroom (with N = 973 students participating in total). Students who visited the planetarium had not only the greatest learning gains, but their performance increased with time, whereas students who saw the same visuals projected onto a flat display in their classroom showed less retention over time. The gains seen in the students who visited the planetarium reveal that this medium is a powerful tool for visualizing scale over multiple orders of magnitude. However the modest gains for the students in the regular classroom also show the utility of these visualization approaches for the broader category of classroom physics simulations.

[Multiple mere exposure effect: category evaluation measured in the Go/No-go association task (GNAT)].

Science.gov (United States)

Kawakami, Naoaki; Yoshida, Fujio

2011-12-01

The effect on likability of multiple subliminal exposures to the same person was investigated. Past studies on the mere exposure effect indicated a correlation between the frequency of repeated exposure to the same stimulus and likability. We proposed that exposure to various stimuli of the same person would have a stronger effect on likability. Participants were subliminally exposed to photographs of a person's face taken from seven angles (multi-angle-exposure) three times each (Experiment 1), or photographs of a person with seven facial expressions (multi-expression-exposure) three times each (Experiment 2). Then, the likability toward the exposed person was measured using the Go/No-go Association Task. The results indicated that the effect of the multiple exposures from various angles was equivalent to exposure to only one full-face photograph shown 21 times (Experiment 1). Moreover, likability was significantly higher in the case of exposure to various facial expressions than for exposure to only a single facial expression (Experiment 2). The results suggest that exposure to various stimuli in a category is more effective than repeated exposure to a single stimulus for increasing likability.

Reduced multiple empirical kernel learning machine.

Science.gov (United States)

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

Imaging evidence for disturbances in multiple learning and memory systems in persons with autism spectrum disorders.

Science.gov (United States)

Goh, Suzanne; Peterson, Bradley S

2012-03-01

The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. We conducted a search of PubMed from 1996 to 2010 using the terms 'autism,''learning,''memory,' and 'neuroimaging.' We limited our review to studies correlating learning and memory function with neuroimaging features of the brain. The early literature supports the following preliminary hypotheses: (1) abnormalities of hippocampal subregions may contribute to autistic deficits in episodic and relational memory; (2) disturbances to an amygdala-based network (which may include the fusiform gyrus, superior temporal cortex, and mirror neuron system) may contribute to autistic deficits in socio-emotional learning and memory; and (3) abnormalities of the striatum may contribute to developmental dyspraxia in individuals with ASD. Characterizing the disturbances to learning and memory systems in ASD can inform our understanding of the neural bases of autistic behaviors and the phenotypic heterogeneity of ASD. © The Authors. Developmental Medicine & Child Neurology © 2012 Mac Keith Press.

Design demonstrations for Category B tank systems piping at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1994-12-01

Demonstration of the design of the piping systems described in this report is stipulated by the Federal Facility Agreement (FFA) between the U.S. Environmental Protection Agency (EPA)-Region IV, the Tennessee Department of Environment and Conservation (TDEC), and the U.S. Department of Energy (DOE). This report provides a design demonstration of the secondary containment and ancillary equipment of 30 piping systems designated in the FFA as Category B (i.e., existing tank systems with secondary containment). Based on the findings of the Design Demonstrations for the Remaining 19 Category B Tank Systems, (DOE/OR/03-1150 ampersand D2), three tank systems originally designated as Category B have been redesignated as Category C (i.e., existing tank systems without secondary containment). The design demonstrations were developed using information obtained from design drawings (as-built when available), construction specifications, and interviews with facility operators. Each design demonstration addresses system conformance to the requirements of the FFA (Appendix F, Section C). Deficiencies or restrictions regarding the ability to demonstrate that each of the containment systems conforms to FFA requirements are noted in the discussion of each piping system and presented in Table 2.0-1

Consumer Product Category Database

Science.gov (United States)

The Chemical and Product Categories database (CPCat) catalogs the use of over 40,000 chemicals and their presence in different consumer products. The chemical use information is compiled from multiple sources while product information is gathered from publicly available Material Safety Data Sheets (MSDS). EPA researchers are evaluating the possibility of expanding the database with additional product and use information.

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

Science.gov (United States)

Tabor, Whitney; Cho, Pyeong W.; Dankowicz, Harry

2013-01-01

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…

Interorganizational learning systems

DEFF Research Database (Denmark)

Hjalager, Anne-Mette

1999-01-01

The occurrence of organizational and interorganizational learning processes is not only the result of management endeavors. Industry structures and market related issues have substantial spill-over effects. The article reviews literature, and it establishes a learning model in which elements from...... organizational environments are included into a systematic conceptual framework. The model allows four types of learning to be identified: P-learning (professional/craft systems learning), T-learning (technology embedded learning), D-learning (dualistic learning systems, where part of the labor force is exclude...... from learning), and S-learning (learning in social networks or clans). The situation related to service industries illustrates the typology....

Design demonstrations for the remaining 19 Category B tank systems at Oak Ridge National Laboratory, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1993-06-01

This document presents design demonstrations conducted of liquid low-level waste (LLLW) storage tank systems located at the Oak Ridge National Laboratory (ORNL). ORNL has conducted research in energy related fields since 1943. The facilities used to conduct the research include nuclear reactors, chemical pilot plants, research laboratories, radioisotope production laboratories, and support facilities. These facilities have produced a variety of radioactive and/or hazardous wastes that have been transported and stored through an extensive network of piping and tankage. Demonstration of the design of these tank systems has been stipulated by the Federal Facility Agreement (FFA) between the EPA (United States Environmental Protection Agency)-Region IV; the Tennessee Department of Environment and Conservation (TDEC); and the DOE. The FFA establishes four categories of tank systems: Category A-New or Replacement Tank Systems with Secondary Containment; Category B-Existing Tank Systems with Secondary Containment; Category C-Existing Tank Systems Without Secondary Containment, and Category D-Existing Tank Systems Without Secondary Containment That are Removed from Service. This document provides a design demonstration of the secondary containment and ancillary equipment of 19 tank systems listed in the FFA as Category B. The design demonstration for each tank is presented in Section 2. The assessments assume that each tank system was constructed in accordance with the design drawings and construction specifications for that system unless specified otherwise. Each design demonstration addresses system conformance to the requirements of the FFA (Appendix F, Section C)

Adult Learning, Economy and Society

DEFF Research Database (Denmark)

Olesen, Henning Salling

2010-01-01

The article relates the different types of adult education, continuing education and training to an overall societal context of socio-economic modernization by focussing on the multiple functions of adult learning. Each of well known empirical categories is seen in its historical relation to mode...... embracing form which set a new framework for human participation in the new global society....

Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information.

Science.gov (United States)

Schapiro, Anna C; McDevitt, Elizabeth A; Chen, Lang; Norman, Kenneth A; Mednick, Sara C; Rogers, Timothy T

2017-11-01

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both REM and NREM sleep. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

Feature Inference Learning and Eyetracking

Science.gov (United States)

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Practical Usage of Multiple-Choice Questions as Part of Learning and Self-Evaluation

Directory of Open Access Journals (Sweden)

Paula Kangasniemi

2016-12-01

Full Text Available The poster describes how the multiple-choice questions could be a part of learning, not only assessing. We often think of the role of questions only in order to test the student's skills. We have tested how questions could be a part of learning in our web-based course of information retrieval in Lapland University. In web-based learning there is a need for high-quality mediators. Mediators are learning promoters which trigger, support, and amplify learning. Mediators can be human mediators or tool mediators. The tool mediators are for example; tests, tutorials, guides and diaries. The multiple-choice questions can also be learning promoters which select, interpret and amplify objects for learning. What do you have to take into account when you are preparing multiple-choice questions as mediators? First you have to prioritize teaching objectives: what must be known and what should be known. According to our experience with contact learning, you can assess what the things are that students have problems with and need more guidance on. The most important addition to the questions is feedback during practice. The questions’ answers (wrong or right are not important. The feedback on the answers are important to guide students on how to search. The questions promote students’ self-regulation and self-evaluation. Feedback can be verbal, a screenshot or a video. We have added a verbal feedback for every question and also some screenshots and eight videos in our web-based course.

Multiple-instance learning as a classifier combining problem

DEFF Research Database (Denmark)

Li, Yan; Tax, David M. J.; Duin, Robert P. W.

2013-01-01

In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the ass...

Discrimination of artificial categories structured by family resemblances: a comparative study in people (Homo sapiens) and pigeons (Columba livia).

Science.gov (United States)

Makino, Hiroshi; Jitsumori, Masako

2007-02-01

Adult humans (Homo sapiens) and pigeons (Columba livia) were trained to discriminate artificial categories that the authors created by mimicking 2 properties of natural categories. One was a family resemblance relationship: The highly variable exemplars, including those that did not have features in common, were structured by a similarity network with the features correlating to one another in each category. The other was a polymorphous rule: No single feature was essential for distinguishing the categories, and all the features overlapped between the categories. Pigeons learned the categories with ease and then showed a prototype effect in accord with the degrees of family resemblance for novel stimuli. Some evidence was also observed for interactive effects of learning of individual exemplars and feature frequencies. Humans had difficulty in learning the categories. The participants who learned the categories generally responded to novel stimuli in an all-or-none fashion on the basis of their acquired classification decision rules. The processes that underlie the classification performances of the 2 species are discussed.

Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

Directory of Open Access Journals (Sweden)

Guoyang Yan

2014-01-01

Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

Licensing system for primary category radioactive installations

International Nuclear Information System (INIS)

Ramirez Riquelme, Angelica Beatriz

1997-01-01

The development of a licensing system for primary category radioactive installations is described, which aims to satisfy the needs of the Chilean Nuclear Energy Commission's Department of Nuclear and Radiological Safety, particularly the sections for Licensing Outside Radioactive Installations and Safety Control. This system involves the identification, control and inspection of the installations, their personnel and connected activities, for the purpose of protecting the population's health and the environment. Following the basic cycle methodology, a systems analysis and engineering stage was prepared, establishing the functions of the system's elements and defining the requirements, based on interviews with the users. This stage was followed by the design stage, focusing on the data structure, the software architecture and the procedural detail. The codification stage followed, which translated the design into legible machine-readable format. In the testing stage, the entries that were defined were proven to produce the expected data. Finally and operational and maintenance stage was developed, when the system was installed and put to use. All the above generated a useful system for the Licensing section of the Department of Nuclear and Radiological Safety, since it provides faster and easier access to information. A project is described that introduces new development tools in the Computer department following standards established by the C.CH.E.N. (author)

Broad Categories for the Diagnosis of Eating Disorders (BCD-ED): An Alternative System for Classification

Science.gov (United States)

Walsh, B. Timothy; Sysko, Robyn

2009-01-01

Eating Disorder Not Otherwise Specified (EDNOS), a residual category in DSM-IV, is the most commonly used eating disorder diagnosis in clinical settings. However, the features of individuals with EDNOS are heterogeneous and difficult to characterize. A diagnostic scheme, termed Broad Categories for the Diagnosis of Eating Disorders (BCD-ED), is proposed to diminish use of the EDNOS category markedly while preserving the existing eating disorder categories. The BCD-ED scheme consists of three broad categories, in a hierarchical relationship, consisting of: Anorexia Nervosa and Behaviorally Similar disorders, Bulimia Nervosa and Behaviorally Similar Disorders, Binge Eating Disorder and Behaviorally Similar Disorders, and a residual category of EDNOS. The advantages and disadvantages of adopting this scheme for DSM-V are considered, and issues relevant to BCD-ED are discussed. Specifically, we review the proportion of individuals with DSM-IV EDNOS that would be re-classified under the BCD-ED system, support for the hierarchy of the three categories, and the potential risk of “overdiagnosis.” PMID:19650083

Comulang: towards a collaborative e-learning system that supports student group modeling.

Science.gov (United States)

Troussas, Christos; Virvou, Maria; Alepis, Efthimios

2013-01-01

This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called "Comulang" while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system's basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.

Napping-Ultra Flash Profile as a Tool for Category Identification and Subsequent Model System Formulation of Caramel Corn Products.

Science.gov (United States)

Mayhew, Emily; Schmidt, Shelly; Lee, Soo-Yeun

2016-07-01

In a novel approach to formulation, the flash descriptive profiling technique Napping-Ultra Flash Profile (Napping-UFP) was used to characterize a wide range of commercial caramel corn products. The objectives were to identify product categories, develop model systems based on product categories, and correlate analytical parameters with sensory terms generated through the Napping-UFP exercise. In one 2 h session, 12 panelists participated in 4 Napping-UFP exercises, describing and grouping, on a 43×56 cm paper sheet, 12 commercial caramel corn samples by degree of similarity, globally and in terms of aroma-by-mouth, texture, and taste. The coordinates of each sample's placement on the paper sheet and descriptive terms generated by the panelists were used to conduct Multiple Factor Analysis (MFA) and hierarchical clustering of the samples. Strong trends in the clustering of samples across the 4 Napping-UFP exercises resulted in the determination of 3 overarching types of commercial caramel corn: "small-scale dark" (typified by burnt, rich caramel corn), "large-scale light" (typified by light and buttery caramel corn), and "large-scale dark" (typified by sweet and molasses-like caramel corn). Representative samples that best exemplified the properties of each category were used as guides in the formulation of 3 model systems that represent the spread of commercial caramel corn products. Analytical testing of the commercial products, including aw measurement, moisture content determination, and thermal characterization via differential scanning calorimetry, were conducted and results related to sensory descriptors using Spearman's correlation. © 2016 Institute of Food Technologists®

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Science.gov (United States)

Luxhoj, J. T.; Ancel, E.; Green, L. L.; Shih, A. T.; Jones, S. M.; Reveley, M. S.

2014-01-01

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

Science.gov (United States)

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-07-05

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Connecting Multiple Intelligences through Open and Distance Learning: Going towards a Collective Intelligence?

Science.gov (United States)

Medeiros Vieira, Leandro Mauricio; Ferasso, Marcos; Schröeder, Christine da Silva

2014-01-01

This theoretical essay is a learning approach reflexion on Howard Gardner's Theory of Multiple Intelligences and the possibilities provided by the education model known as open and distance learning. Open and distance learning can revolutionize traditional pedagogical practice, meeting the needs of those who have different forms of cognitive…

Declarative and nondeclarative memory: multiple brain systems supporting learning and memory.

Science.gov (United States)

Squire, L R

1992-01-01

Abstract The topic of multiple forms of memory is considered from a biological point of view. Fact-and-event (declarative, explicit) memory is contrasted with a collection of non conscious (non-declarative, implicit) memory abilities including skills and habits, priming, and simple conditioning. Recent evidence is reviewed indicating that declarative and non declarative forms of memory have different operating characteristics and depend on separate brain systems. A brain-systems framework for understanding memory phenomena is developed in light of lesion studies involving rats, monkeys, and humans, as well as recent studies with normal humans using the divided visual field technique, event-related potentials, and positron emission tomography (PET).

Energy information data base: energy categories

International Nuclear Information System (INIS)

1980-03-01

Citations entered into DOE's computerized bibliographic information system are assigned six-digit subject category numbers to group information broadly for storage, retrieval, and manipulation. These numbers are used in the preparation of printed documents, such as bibliographies and abstract journals, to arrange the citations and as searching aids in the on-line system, DOE/RECON. This document has been prepared for use by those individuals responsible for the assignment of category numbers to documents being entered into the Technical Information Center (TIC) system, those individuals and organizations processing magnetic tape copies of the files, those individuals doing on-line searching for information in TIC-created files, and others who, having no access to RECON, need printed copy. The six-digit numbers assigned to documents are listed, along with the category names and text to define the scope of interest. Asterisks highlight those categories added or changed since the previous printing, and a subject index further details the subject content of each category

Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

Science.gov (United States)

Cheng, Ching-An; Huang, Han-Pang

2016-12-01

We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.

Four categories of design challenges to building game-based business

DEFF Research Database (Denmark)

Henriksen, Thomas Duus; Harpelund, Christian

2014-01-01

Building a business on the basis of designing and selling learning games is seldom a straightforward task. Often, such a project involves a diversity of competencies for handling a wide variety of challenges. On the basis of a longitudinal study of the game ChangeSetter, this chapter proposes...... a four-category approach to understanding such challenges. The four categories include 1) the learning game design, 2) didactic design on how the game is to be used, 3) organisational design for establishing both supply and demand, and finally 4) business design, which concerns the establishment...... of a business model that ensures continual rather than incidental income. While the four categories can be used for understanding the various challenges and what competencies they prompt for, the key argument of the chapter is to start with the business design as it is likely to cause extensive iterations...

Understanding Cognitive Language Learning Strategies

Directory of Open Access Journals (Sweden)

Sergio Di Carlo

2017-01-01

Full Text Available Over time, definitions and taxonomies of language learning strategies have been critically examined. This article defines and classifies cognitive language learning strategies on a more grounded basis. Language learning is a macro-process for which the general hypotheses of information processing are valid. Cognitive strategies are represented by the pillars underlying the encoding, storage and retrieval of information. In order to understand the processes taking place on these three dimensions, a functional model was elaborated from multiple theoretical contributions and previous models: the Smart Processing Model. This model operates with linguistic inputs as well as with any other kind of information. It helps to illustrate the stages, relations, modules and processes that occur during the flow of information. This theoretical advance is a core element to classify cognitive strategies. Contributions from cognitive neuroscience have also been considered to establish the proposed classification which consists of five categories. Each of these categories has a different predominant function: classification, preparation, association, elaboration and transfer-practice. This better founded taxonomy opens the doors to potential studies that would allow a better understanding of the interdisciplinary complexity of language learning. Pedagogical and methodological implications are also discussed.

Typicality effects in artificial categories: is there a hemisphere difference?

Science.gov (United States)

Richards, L G; Chiarello, C

1990-07-01

In category classification tasks, typicality effects are usually found: accuracy and reaction time depend upon distance from a prototype. In this study, subjects learned either verbal or nonverbal dot pattern categories, followed by a lateralized classification task. Comparable typicality effects were found in both reaction time and accuracy across visual fields for both verbal and nonverbal categories. Both hemispheres appeared to use a similarity-to-prototype matching strategy in classification. This indicates that merely having a verbal label does not differentiate classification in the two hemispheres.

Parallel multiple instance learning for extremely large histopathology image analysis.

Science.gov (United States)

Xu, Yan; Li, Yeshu; Shen, Zhengyang; Wu, Ziwei; Gao, Teng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

2017-08-03

Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

The Development of Categorization: Effects of Classification and Inference Training on Category Representation

Science.gov (United States)

Deng, Wei; Sloutsky, Vladimir M.

2015-01-01

Does category representation change in the course of development? And if so, how and why? The current study attempted to answer these questions by examining category learning and category representation. In Experiment 1, 4-year-olds, 6-year-olds, and adults were trained with either a classification task or an inference task and their…

Modulation of multiple memory systems: from neurotransmitters to metabolic substrates.

Science.gov (United States)

Gold, Paul E; Newman, Lori A; Scavuzzo, Claire J; Korol, Donna L

2013-11-01

This article reviews evidence showing that neurochemical modulators can regulate the relative participation of the hippocampus and striatum in learning and memory tasks. For example, relative release of acetylcholine increases in the hippocampus and striatum reflects the relative engagement of these brain systems during learning of place and response tasks. Acetylcholine release is regulated in part by available brain glucose levels, which themselves are dynamically modified during learning. Recent findings suggest that glucose acts through astrocytes to deliver lactate to neurons. Brain glycogen is contained in astrocytes and provides a capacity to deliver energy substrates to neurons when needed, a need that can be generated by training on tasks that target hippocampal and striatal processing mechanisms. These results integrate an increase in blood glucose after epinephrine release from the adrenal medulla with provision of brain energy substrates, including lactate released from astrocytes. Together, the availability of peripheral and central energy substrates regulate the processing of learning and memory within and across multiple neural systems. Dysfunctions of the physiological steps that modulate memory--from hormones to neurotransmitters to metabolic substrates--may contribute importantly to some of the cognitive impairments seen during normal aging and during neurodegenerative diseases. Copyright © 2013 Wiley Periodicals, Inc.

Music mnemonics aid Verbal Memory and Induce Learning - Related Brain Plasticity in Multiple Sclerosis.

Science.gov (United States)

Thaut, Michael H; Peterson, David A; McIntosh, Gerald C; Hoemberg, Volker

2014-01-01

Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey's auditory verbal learning test. We defined the "learning-related synchronization" (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances "deep encoding" during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS.

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

Science.gov (United States)

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Social software: E-learning beyond learning management systems

DEFF Research Database (Denmark)

Dalsgaard, Christian

2006-01-01

The article argues that it is necessary to move e-learning beyond learning management systems and engage students in an active use of the web as a resource for their self-governed, problem-based and collaborative activities. The purpose of the article is to discuss the potential of social software...... to move e-learning beyond learning management systems. An approach to use of social software in support of a social constructivist approach to e-learning is presented, and it is argued that learning management systems do not support a social constructivist approach which emphasizes self-governed learning...... activities of students. The article suggests a limitation of the use of learning management systems to cover only administrative issues. Further, it is argued that students' self-governed learning processes are supported by providing students with personal tools and engaging them in different kinds of social...

Collaborative E-Learning with Multiple Imaginary Co-Learner: Design, Issues and Implementation

OpenAIRE

Melvin Ballera; Mosbah Mohamed Elssaedi; Ahmed Khalil Zohdy

2013-01-01

Collaborative problem solving in e-learning can take in the form of discussion among learner, creating a highly social learning environment and characterized by participation and interactivity. This paper, designed a collaborative learning environment where agent act as co-learner, can play different roles during interaction. Since different roles have been assigned to the agent, learner will assume that multiple co-learner exists to help and guide him all throughout the ...

New designing of E-Learning systems with using network learning

OpenAIRE

Malayeri, Amin Daneshmand; Abdollahi, Jalal

2010-01-01

One of the most applied learning in virtual spaces is using E-Learning systems. Some E-Learning methodologies has been introduced, but the main subject is the most positive feedback from E-Learning systems. In this paper, we introduce a new methodology of E-Learning systems entitle "Network Learning" with review of another aspects of E-Learning systems. Also, we present benefits and advantages of using these systems in educating and fast learning programs. Network Learning can be programmable...

The classification, cost categories and the system of accounting for uranium resources in the Russian Federation and CIS countries

International Nuclear Information System (INIS)

Naumov, S.S.; Shumilin, M.V.

1998-01-01

In 1992 the uranium resource classification systems of Kazakhstan, Russian Federation, Ukraine and Uzbekistan are the same as the system used in the former Soviet Union. The Soviet Union adopted this system as a state law in 1981. Under this system resources are reported as ''in situ'' with no allowance for mining or milling losses and resource depletion. The resources are subdivided according to the degree of exploration and economic value. The classification system divides resources into 7 categories. This includes 3 categories of explored resources (A, B, and C); one of preliminary assessment (C2); and 3 as prognosticated or speculative (P1, P2 and P3). Further analyses and classification is used to determine the readiness for production. This system is used to define the inventory of ''State Balance''. Examples are given for classifying vein-type and rollfront-type sandstone hosted deposits. A discussion of how resources are classified by cost category is given. It is stated, however, that any coincidence between the cost categories used in the former Soviet Union and the cost categories of the IAEA system are ''purely accidental''. (author)

A system for learning statistical motion patterns.

Science.gov (United States)

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

Science.gov (United States)

Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

2006-01-01

Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

Multiple Chaotic Central Pattern Generators with Learning for Legged Locomotion and Malfunction Compensation

DEFF Research Database (Denmark)

Ren, Guanjiao; Chen, Weihai; Dasgupta, Sakyasingha

2015-01-01

on a simulated annealing algorithm. In a normal situation, the CPGs synchronize and their dynamics are identical. With leg malfunction or disability, the CPGs lose synchronization leading to independent dynamics. In this case, the learning mechanism is applied to automatically adjust the remaining legs...... in a physical simulation of a quadruped as well as a hexapod robot and finally in a real six-legged walking machine called AMOSII. The experimental results presented here reveal that using multiple CPGs with learning is an effective approach for adaptive locomotion generation where, for instance, different body...... chaotic CPG controller has difficulties dealing with leg malfunction. Specifically, in the scenarios presented here, its movement permanently deviates from the desired trajectory. To address this problem, we extend the single chaotic CPG to multiple CPGs with learning. The learning mechanism is based...

Testing the Efficiency of Markov Chain Monte Carlo with People Using Facial Affect Categories

Science.gov (United States)

Martin, Jay B.; Griffiths, Thomas L.; Sanborn, Adam N.

2012-01-01

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as…

Imaging Evidence for Disturbances in Multiple Learning and Memory Systems in Persons with Autism Spectrum Disorders

Science.gov (United States)

Goh, Suzanne; Peterson, Bradley S.

2012-01-01

Aim: The aim of this article is to review neuroimaging studies of autism spectrum disorders (ASD) that examine declarative, socio-emotional, and procedural learning and memory systems. Method: We conducted a search of PubMed from 1996 to 2010 using the terms "autism,""learning,""memory," and "neuroimaging." We limited our review to studies…

Subject categories and scope descriptions

International Nuclear Information System (INIS)

2002-01-01

This document is one in a series of publications known as the ETDE/INIS Joint Reference Series. It defines the subject categories and provides the scope descriptions to be used for categorization of the nuclear literature for the preparation of INIS and ETDE input by national and regional centres. Together with the other volumes of the INIS Reference Series it defines the rules, standards and practices and provides the authorities to be used in the International Nuclear Information System and ETDE. A complete list of the volumes published in the INIS Reference Series may be found on the inside front cover of this publication. This INIS/ETDE Reference Series document is intended to serve two purposes: to define the subject scope of the International Nuclear Information System (INIS) and the Energy Technology Data Exchange (ETDE) and to define the subject classification scheme of INIS and ETDE. It is thus the guide to the inputting centres in determining which items of literature should be reported, and in determining where the full bibliographic entry and abstract of each item should be included in INIS or ETDE database. Each category is identified by a category code consisting of three alphanumeric characters. A scope description is given for each subject category. The scope of INIS is the sum of the scopes of all the categories. With most categories cross references are provided to other categories where appropriate. Cross references should be of assistance in finding the appropriate category; in fact, by indicating topics that are excluded from the category in question, the cross references help to clarify and define the scope of the category to which they are appended. A Subject Index is included as an aid to subject classifiers, but it is only an aid and not a means for subject classification. It facilitates the use of this document, but is no substitute for the description of the scope of the subject categories

Effects of statistical learning on the acquisition of grammatical categories through Qur'anic memorization: A natural experiment.

Science.gov (United States)

Zuhurudeen, Fathima Manaar; Huang, Yi Ting

2016-03-01

Empirical evidence for statistical learning comes from artificial language tasks, but it is unclear how these effects scale up outside of the lab. The current study turns to a real-world test case of statistical learning where native English speakers encounter the syntactic regularities of Arabic through memorization of the Qur'an. This unique input provides extended exposure to the complexity of a natural language, with minimal semantic cues. Memorizers were asked to distinguish unfamiliar nouns and verbs based on their co-occurrence with familiar pronouns in an Arabic language sample. Their performance was compared to that of classroom learners who had explicit knowledge of pronoun meanings and grammatical functions. Grammatical judgments were more accurate in memorizers compared to non-memorizers. No effects of classroom experience were found. These results demonstrate that real-world exposure to the statistical properties of a natural language facilitates the acquisition of grammatical categories. Copyright © 2015 Elsevier B.V. All rights reserved.

Building phonetic categories: an argument for the role of sleep

Directory of Open Access Journals (Sweden)

F. Sayako Earle

2014-10-01

Full Text Available The current review provides specific predictions for the role of sleep-mediated memory consolidation in the formation of new speech sound representations. Specifically, this discussion will highlight selected literature on the different ideas concerning category representation in speech, followed by a broad overview of memory consolidation and how it relates to human behavior, as relevant to speech/perceptual learning. In combining behavioral and physiological accounts from animal models with insights from the human consolidation literature on auditory skill/word learning, we are in the early stages of understanding how the transfer of experiential information between brain structures during sleep manifests in changes to online perception. Arriving at the conclusion that this process is crucial in perceptual learning and the formation of novel categories, further speculation yields the adjacent claim that the habitual disruption in this process leads to impoverished quality in the representation of speech sounds.

Pareto-path multitask multiple kernel learning.

Science.gov (United States)

Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

2015-01-01

A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.

A Coterminous Collaborative Learning Model: Interconnectivity of Leadership and Learning

Directory of Open Access Journals (Sweden)

Ilana Margolin

2012-05-01

Full Text Available This qualitative ethnographic study examines a collaborative leadership model focused on learning and socially just practices within a change context of a wide educational partnership. The study analyzes a range of perspectives of novice teachers, mentor teachers, teacher educators and district superintendents on leadership and learning. The findings reveal the emergence of a coalition of leaders crossing borders at all levels of the educational system: local school level, district level and teacher education level who were involved in coterminous collaborative learning. Four categories of learning were identified as critical to leading a change in the educational system: learning in professional communities, learning from practice, learning through theory and research and learning from and with leaders. The implications of the study for policy makers as well as for practitioners are to adopt a holistic approach to the educational environment and plan a collaborative learning continuum from initial pre-service programs through professional development learning at all levels.

The interaction of short-term and long-term memory in phonetic category formation

Science.gov (United States)

Harnsberger, James D.

2002-05-01

This study examined the role that short-term memory capacity plays in the relationship between novel stimuli (e.g., non-native speech sounds, native nonsense words) and phonetic categories in long-term memory. Thirty native speakers of American English were administered five tests: categorial AXB discrimination using nasal consonants from Malayalam; categorial identification, also using Malayalam nasals, which measured the influence of phonetic categories in long-term memory; digit span; nonword span, a short-term memory measure mediated by phonetic categories in long-term memory; and paired-associate word learning (word-word and word-nonword pairs). The results showed that almost all measures were significantly correlated with one another. The strongest predictor for the discrimination and word-nonword learning results was nonword (r=+0.62) and digit span (r=+0.51), respectively. When the identification test results were partialed out, only nonword span significantly correlated with discrimination. The results show a strong influence of short-term memory capacity on the encoding of phonetic detail within phonetic categories and suggest that long-term memory representations regulate the capacity of short-term memory to preserve information for subsequent encoding. The results of this study will also be discussed with regards to resolving the tension between episodic and abstract models of phonetic category structure.

HyDR-MI : A hybrid algorithm to reduce dimensionality in multiple instance learning

NARCIS (Netherlands)

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

2013-01-01

Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple

Comparing Product Category Rules from Different Programs: Learned Outcomes Towards Global Alignment

Science.gov (United States)

Purpose Product category rules (PCRs) provide category-specific guidance for estimating and reporting product life cycle environmental impacts, typically in the form of environmental product declarations and product carbon footprints. Lack of global harmonization between PCRs or ...

Multiple-Machine Scheduling with Learning Effects and Cooperative Games

Directory of Open Access Journals (Sweden)

Yiyuan Zhou

2015-01-01

Full Text Available Multiple-machine scheduling problems with position-based learning effects are studied in this paper. There is an initial schedule in this scheduling problem. The optimal schedule minimizes the sum of the weighted completion times; the difference between the initial total weighted completion time and the minimal total weighted completion time is the cost savings. A multiple-machine sequencing game is introduced to allocate the cost savings. The game is balanced if the normal processing times of jobs that are on the same machine are equal and an equal number of jobs are scheduled on each machine initially.

Supervised Learning for Dynamical System Learning.

Science.gov (United States)

Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

2015-01-01

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

Computation in the Learning System of Cephalopods.

Science.gov (United States)

Young, J Z

1991-04-01

The memory mechanisms of cephalopods consist of a series of matrices of intersecting axes, which find associations between the signals of input events and their consequences. The tactile memory is distributed among eight such matrices, and there is also some suboesophageal learning capacity. The visual memory lies in the optic lobe and four matrices, with some re-exciting pathways. In both systems, damage to any part reduces proportionally the effectiveness of the whole memory. These matrices are somewhat like those in mammals, for instance those in the hippocampus. The first matrix in both visual and tactile systems receives signals of vision and taste, and its output serves to increase the tendency to attack or to take with the arms. The second matrix provides for the correlation of groups of signals on its neurons, which pass signals to the third matrix. Here large cells find clusters in the sets of signals. Their output re-excites those of the first lobe, unless pain occurs. In that case, this set of cells provides a record that ensures retreat. There is experimental evidence that these distributed memory systems allow for the identification of categories of visual and tactile inputs, for generalization, and for decision on appropriate behavior in the light of experience. The evidence suggests that learning in cephalopods is not localized to certain layers or "grandmother cells" but is distributed with high redundance in serial networks, with recurrent circuits.

Error Discounting in Probabilistic Category Learning

Science.gov (United States)

Craig, Stewart; Lewandowsky, Stephan; Little, Daniel R.

2011-01-01

The assumption in some current theories of probabilistic categorization is that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report 2 probabilistic-categorization experiments in which we investigated error…

STEM based learning to facilitate middle school students’ conceptual change, creativity and collaboration in organization of living system topic

Science.gov (United States)

Rustaman, N. Y.; Afianti, E.; Maryati, S.

2018-05-01

A study using one group pre-post-test experimental design on Life organization system topic was carried out to investigate student’s tendency in learning abstract concept, their creativity and collaboration in designing and producing cell models through STEM-based learning. A number of seventh grade students in Cianjur district were involved as research subjects (n=34). Data were collected using two tier test for tracing changes in student conception before and after the application of STEM-based learning, and rubrics in creativity design (adopted from Torrance) and product on cell models (individually, in group), and rubric for self-assessment and observed skills on collaboration adapted from Marzano’s for life-long learning. Later the data obtained were analyzed qualitatively by interpreting the tendency of data presented in matrix sorted by gender. Research findings showed that the percentage of student’s scientific concept mastery is moderate in general. Their creativity in making a cell model design varied in category (expressing, emergent, excellent, not yet evident). Student’s collaboration varied from excellent, fair, good, less once, to less category in designing cell model. It was found that STEM based learning can facilitate students conceptual change, creativity and collaboration.

Learning Content Management Systems

Directory of Open Access Journals (Sweden)

Tache JURUBESCU

2008-01-01

Full Text Available The paper explains the evolution of e-Learning and related concepts and tools and its connection with other concepts such as Knowledge Management, Human Resources Management, Enterprise Resource Planning, and Information Technology. The paper also distinguished Learning Content Management Systems from Learning Management Systems and Content Management Systems used for general web-based content. The newest Learning Content Management System, very expensive and yet very little implemented is one of the best tools that helps us to cope with the realities of the 21st Century in what learning concerns. The debates over how beneficial one or another system is for an organization, can be driven by costs involved, efficiency envisaged, and availability of the product on the market.

Music mnemonics aid Verbal Memory and Induce Learning – Related Brain Plasticity in Multiple Sclerosis

Science.gov (United States)

Thaut, Michael H.; Peterson, David A.; McIntosh, Gerald C.; Hoemberg, Volker

2014-01-01

Recent research on music and brain function has suggested that the temporal pattern structure in music and rhythm can enhance cognitive functions. To further elucidate this question specifically for memory, we investigated if a musical template can enhance verbal learning in patients with multiple sclerosis (MS) and if music-assisted learning will also influence short-term, system-level brain plasticity. We measured systems-level brain activity with oscillatory network synchronization during music-assisted learning. Specifically, we measured the spectral power of 128-channel electroencephalogram (EEG) in alpha and beta frequency bands in 54 patients with MS. The study sample was randomly divided into two groups, either hearing a spoken or a musical (sung) presentation of Rey’s auditory verbal learning test. We defined the “learning-related synchronization” (LRS) as the percent change in EEG spectral power from the first time the word was presented to the average of the subsequent word encoding trials. LRS differed significantly between the music and the spoken conditions in low alpha and upper beta bands. Patients in the music condition showed overall better word memory and better word order memory and stronger bilateral frontal alpha LRS than patients in the spoken condition. The evidence suggests that a musical mnemonic recruits stronger oscillatory network synchronization in prefrontal areas in MS patients during word learning. It is suggested that the temporal structure implicit in musical stimuli enhances “deep encoding” during verbal learning and sharpens the timing of neural dynamics in brain networks degraded by demyelination in MS. PMID:24982626

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

Science.gov (United States)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Science.gov (United States)

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

Design of multiple representations e-learning resources based on a contextual approach for the basic physics course

Science.gov (United States)

Bakri, F.; Muliyati, D.

2018-05-01

This research aims to design e-learning resources with multiple representations based on a contextual approach for the Basic Physics Course. The research uses the research and development methods accordance Dick & Carey strategy. The development carried out in the digital laboratory of Physics Education Department, Mathematics and Science Faculty, Universitas Negeri Jakarta. The result of the process of product development with Dick & Carey strategy, have produced e-learning design of the Basic Physics Course is presented in multiple representations in contextual learning syntax. The appropriate of representation used in the design of learning basic physics include: concept map, video, figures, data tables of experiment results, charts of data tables, the verbal explanations, mathematical equations, problem and solutions example, and exercise. Multiple representations are presented in the form of contextual learning by stages: relating, experiencing, applying, transferring, and cooperating.

A Semantic Representation Of Adult Learners' Developing Conceptions Of Self Realisation Through Learning Process

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

based on educational informatics. I shall draw your attention to the fact that in information sciences an ontology is described as an explicit (and formal) specification of a shared conceptualisation on the domain of interest. Ontologies of a thing/phenomenon support different researchers in providing......Learning is the reflective activity that enables the learner to draw upon her/his previous experiences and background knowledge to conceptualise, realise, understand and evaluate the present, so as to shape her/his future actions and to construct and develop new knowledge for her(him)self. Learning....... This research will conceptually focus on multiple categories through the adult learners’ developing conceptions of learning. The focus will be on different categories from the basic conceptions to excellent ones. I will take an appropriate model of students’ developing conceptions of learning into my...

Metabolic, Replication and Genomic Category of Systems in Biology, Bioinformatics and Medicine

OpenAIRE

I. C. Baianu

2012-01-01

Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs)in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life’s origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important quest...

Enhancing Undergraduate Chemistry Learning by Helping Students Make Connections among Multiple Graphical Representations

Science.gov (United States)

Rau, Martina A.

2015-01-01

Multiple representations are ubiquitous in chemistry education. To benefit from multiple representations, students have to make connections between them. However, connection making is a difficult task for students. Prior research shows that supporting connection making enhances students' learning in math and science domains. Most prior research…

On Utmost Multiplicity of Hierarchical Stellar Systems

Directory of Open Access Journals (Sweden)

Gebrehiwot Y. M.

2016-12-01

Full Text Available According to theoretical considerations, multiplicity of hierarchical stellar systems can reach, depending on masses and orbital parameters, several hundred, while observational data confirm the existence of at most septuple (seven-component systems. In this study, we cross-match the stellar systems of very high multiplicity (six and more components in modern catalogues of visual double and multiple stars to find among them the candidates to hierarchical systems. After cross-matching the catalogues of closer binaries (eclipsing, spectroscopic, etc., some of their components were found to be binary/multiple themselves, what increases the system's degree of multiplicity. Optical pairs, known from literature or filtered by the authors, were flagged and excluded from the statistics. We compiled a list of hierarchical systems with potentially very high multiplicity that contains ten objects. Their multiplicity does not exceed 12, and we discuss a number of ways to explain the lack of extremely high multiplicity systems.

When bad stress goes good: increased threat reactivity predicts improved category learning performance.

Science.gov (United States)

Ell, Shawn W; Cosley, Brandon; McCoy, Shannon K

2011-02-01

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

Enriching Adaptation in E-Learning Systems through a Situation-Aware Ontology Network

Science.gov (United States)

Pernas, Ana Marilza; Diaz, Alicia; Motz, Regina; de Oliveira, Jose Palazzo Moreira

2012-01-01

Purpose: The broader adoption of the internet along with web-based systems has defined a new way of exchanging information. That advance added by the multiplication of mobile devices has required systems to be even more flexible and personalized. Maybe because of that, the traditional teaching-controlled learning style has given up space to a new…

Systemic Treatments for Noninfectious Vitreous Inflammation

Directory of Open Access Journals (Sweden)

Angela Jiang

2013-01-01

Full Text Available Vitreous inflammation, or vitritis, may result from many causes, including both infectious and noninfectious, including rheumatologic and autoimmune processes. Vitritis is commonly vision threatening and has serious sequelae. Treatment is frequently challenging, but, today, there are multiple methods of systemic treatment for vitritis. These categories include corticosteroids, antimetabolites, alkylating agents, T-cell inhibitors/calcineurin inhibitors, and biologic agents. These treatment categories were reviewed last year, but, even over the course of just a year, many therapies have made progress, as we have learned more about their indications and efficacy. We discuss here discoveries made over the past year on both existing and new drugs, as well as reviewing mechanisms of action, clinical dosages, specific conditions that are treated, adverse effects, and usual course of treatment for each class of therapy.

Emotional Arousal and Multiple Memory Systems in the Mammalian Brain

Directory of Open Access Journals (Sweden)

Mark G. Packard

2012-03-01

Full Text Available Emotional arousal induced by stress and/or anxiety can exert complex effects on learning and memory processes in mammals. Recent studies have begun to link study of the influence of emotional arousal on memory with earlier research indicating that memory is organized in multiple systems in the brain that differ in terms of the type of memory they mediate. Specifically, these studies have examined whether emotional arousal may have a differential effect on the cognitive and stimulus-response habit memory processes subserved by the hippocampus and dorsal striatum, respectively. Evidence indicates that stress or the peripheral injection of anxiogenic drugs can bias animals and humans towards the use of striatal-dependent habit memory in dual-solution tasks in which both hippocampal and stritatal-based strategies can provide an adequate solution. A bias towards the use of habit memory can also be produced by intra-basolateral amygdala administration of anxiogenic drugs, consistent with the well documented role of efferent projections of this brain region in mediating the modulatory influence of emotional arousal on memory. In some learning situations, the bias towards the use of habit memory produced by emotional arousal appears to result from an impairing effect on hippocampus-dependent cognitive memory. Further research examining the neural mechanisms linking emotion and the relative use of multiple memory systems should prove useful in view of the potential role for maladaptive habitual behaviors in various human psychopathologies.

The Learning Science through Theatre Initiative in the Context of Responsible Research and Innovation

Directory of Open Access Journals (Sweden)

Zacharoula Smyrnaiou

2017-10-01

Full Text Available Fostering Responsible Research and Innovation (RRI is the next big step in the methodological teaching of Science. This is the solution towards an open classroom and innovation system of learning. The school science teaching needs to become more engaging. Science education should be an essential component of a learning continuum not only in classroom, but also for all, from pre- school to active engaged citizenship. "The Learning Science Through Theatre" Initiative creates a network of knowledge and collaboration between different communities by learning about science through other disciplines and learning about other disciplines through science. Forty Three (43 theatrical performances during the school years 2014-2016 were organized by secondary school students (2000 subjects which embed both scientific concepts and cultural/ social elements which are expressed by embodied, verbal interaction and analogies. The methodology constitutes a merging of qualitative, quantitative and grounded theory analysis. The data were classified into categories and they were cross- checked by registrations forms, filled by the teachers. Results show that the acquisition of knowledge is successful with the co- existence of multiple semiotic systems and the theatrical performances are compatible with the principles of RRI.

Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation

Directory of Open Access Journals (Sweden)

Takuya Goto

2010-09-01

Full Text Available Since English expressions vary according to the genres, it is important for students to study questions that are generated from sentences of the target genre. Although various questions are prepared, it is still not enough to satisfy various genres which students want to learn. On the other hand, when producing English questions, sufficient grammatical knowledge and vocabulary are needed, so it is difficult for non-expert to prepare English questions by themselves. In this paper, we propose an automatic generation system of multiple-choice cloze questions from English texts. Empirical knowledge is necessary to produce appropriate questions, so machine learning is introduced to acquire knowledge from existing questions. To generate the questions from texts automatically, the system (1 extracts appropriate sentences for questions from texts based on Preference Learning, (2 estimates a blank part based on Conditional Random Field, and (3 generates distracters based on statistical patterns of existing questions. Experimental results show our method is workable for selecting appropriate sentences and blank part. Moreover, our method is appropriate to generate the available distracters, especially for the sentence that does not contain the proper noun.

Multiple instance learning tracking method with local sparse representation

KAUST Repository

Xie, Chengjun

2013-10-01

When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

Lesion classification using clinical and visual data fusion by multiple kernel learning

Science.gov (United States)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

Event recognition in personal photo collections via multiple instance learning-based classification of multiple images

Science.gov (United States)

Ahmad, Kashif; Conci, Nicola; Boato, Giulia; De Natale, Francesco G. B.

2017-11-01

Over the last few years, a rapid growth has been witnessed in the number of digital photos produced per year. This rapid process poses challenges in the organization and management of multimedia collections, and one viable solution consists of arranging the media on the basis of the underlying events. However, album-level annotation and the presence of irrelevant pictures in photo collections make event-based organization of personal photo albums a more challenging task. To tackle these challenges, in contrast to conventional approaches relying on supervised learning, we propose a pipeline for event recognition in personal photo collections relying on a multiple instance-learning (MIL) strategy. MIL is a modified form of supervised learning and fits well for such applications with weakly labeled data. The experimental evaluation of the proposed approach is carried out on two large-scale datasets including a self-collected and a benchmark dataset. On both, our approach significantly outperforms the existing state-of-the-art.

Patient Safety Learning Systems: A Systematic Review and Qualitative Synthesis.

Science.gov (United States)

2017-01-01

, and multiple mechanisms to provide feedback through routes to reporters and the wider community (local meetings, email alerts, bulletins, paper contributions, etc.). The design of a patient safety learning system can be optimized by an awareness of the barriers to and facilitators of successful adoption and implementation identified by health care professionals. Evaluation of the effectiveness of a patient safety learning system is needed to refine its design.

Positive attitudinal shifts with the Physics by Inquiry curriculum across multiple implementations

Directory of Open Access Journals (Sweden)

Beth A. Lindsey

2012-01-01

Full Text Available Recent publications have documented positive attitudinal shifts on the Colorado Learning Attitudes about Science Survey (CLASS among students enrolled in courses with an explicit epistemological focus. We now report positive attitudinal shifts in classes using the Physics by Inquiry (PbI curriculum, which has only an implicit focus on student epistemologies and nature of science issues. These positive shifts have occurred in several different implementations of the curriculum, across multiple institutions and multiple semesters. In many classes, students experienced significant attitudinal shifts in the problem-solving categories of the CLASS, despite the conceptual focus of most PbI courses.

Associative learning in baboons (Papio papio) and humans (Homo sapiens): species differences in learned attention to visual features.

Science.gov (United States)

Fagot, J; Kruschke, J K; Dépy, D; Vauclair, J

1998-10-01

We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features.

A Web-Based Learning Support System for Inquiry-Based Learning

Science.gov (United States)

Kim, Dong Won; Yao, Jingtao

The emergence of the Internet and Web technology makes it possible to implement the ideals of inquiry-based learning, in which students seek truth, information, or knowledge by questioning. Web-based learning support systems can provide a good framework for inquiry-based learning. This article presents a study on a Web-based learning support system called Online Treasure Hunt. The Web-based learning support system mainly consists of a teaching support subsystem, a learning support subsystem, and a treasure hunt game. The teaching support subsystem allows instructors to design their own inquiry-based learning environments. The learning support subsystem supports students' inquiry activities. The treasure hunt game enables students to investigate new knowledge, develop ideas, and review their findings. Online Treasure Hunt complies with a treasure hunt model. The treasure hunt model formalizes a general treasure hunt game to contain the learning strategies of inquiry-based learning. This Web-based learning support system empowered with the online-learning game and founded on the sound learning strategies furnishes students with the interactive and collaborative student-centered learning environment.

Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

Science.gov (United States)

Chen, Tianle; Zeng, Donglin

2015-01-01

Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

Intelligent Web-Based Learning System with Personalized Learning Path Guidance

Science.gov (United States)

Chen, C. M.

2008-01-01

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths…

Machine learning systems

Energy Technology Data Exchange (ETDEWEB)

Forsyth, R

1984-05-01

With the dramatic rise of expert systems has come a renewed interest in the fuel that drives them-knowledge. For it is specialist knowledge which gives expert systems their power. But extracting knowledge from human experts in symbolic form has proved arduous and labour-intensive. So the idea of machine learning is enjoying a renaissance. Machine learning is any automatic improvement in the performance of a computer system over time, as a result of experience. Thus a learning algorithm seeks to do one or more of the following: cover a wider range of problems, deliver more accurate solutions, obtain answers more cheaply, and simplify codified knowledge. 6 references.

More than a boundary shift: Perceptual adaptation to foreign-accented speech reshapes the internal structure of phonetic categories.

Science.gov (United States)

Xie, Xin; Theodore, Rachel M; Myers, Emily B

2017-01-01

The literature on perceptual learning for speech shows that listeners use lexical information to disambiguate phonetically ambiguous speech sounds and that they maintain this new mapping for later recognition of ambiguous sounds for a given talker. Evidence for this kind of perceptual reorganization has focused on phonetic category boundary shifts. Here, we asked whether listeners adjust both category boundaries and internal category structure in rapid adaptation to foreign accents. We investigated the perceptual learning of Mandarin-accented productions of word-final voiced stops in English. After exposure to a Mandarin speaker's productions, native-English listeners' adaptation to the talker was tested in 3 ways: a cross-modal priming task to assess spoken word recognition (Experiment 1), a category identification task to assess shifts in the phonetic boundary (Experiment 2), and a goodness rating task to assess internal category structure (Experiment 3). Following exposure, both category boundary and internal category structure were adjusted; moreover, these prelexical changes facilitated subsequent word recognition. Together, the results demonstrate that listeners' sensitivity to acoustic-phonetic detail in the accented input promoted a dynamic, comprehensive reorganization of their perceptual response as a consequence of exposure to the accented input. We suggest that an examination of internal category structure is important for a complete account of the mechanisms of perceptual learning. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Game-Based Learning: Increasing the Logical-Mathematical, Naturalistic, and Linguistic Learning Levels of Primary School Students

Directory of Open Access Journals (Sweden)

María Esther Del Moral Pérez

2018-01-01

Full Text Available Game-based learning is an innovative methodology that takes advantage of the educational potential offered by videogames in general and serious games in particular to boost training processes, thus making it easier for users to achieve motivated learning. The present paper focuses on the description of the Game to Learn Project, which has as its aim not only to promote the use of serious games and digital mini-games for the development of Multiple Intelligences, but also to analyse whether this methodology results in increased learning. Teachers assessed the level achieved by primary education students (N=119 in each learning category, before and after participating in the project, by means of a qualitative instrument. Finally, after corresponding analysis through descriptive statistical techniques, bivariate correlations, and ANOVA, the results showed significant differences between children’s learning levels in logical-mathematical, naturalistic and linguistic abilities before and after their participation in this innovative project, thus revealing a widespread increase in every indicator.

Multiple Intelligences, Motivations and Learning Experience Regarding Video-Assisted Subjects in a Rural University

Science.gov (United States)

Hajhashemi, Karim; Caltabiano, Nerina; Anderson, Neil; Tabibzadeh, Seyed Asadollah

2018-01-01

This study investigates multiple intelligences in relation to online video experiences, age, gender, and mode of learning from a rural Australian university. The inter-relationships between learners' different intelligences and their motivations and learning experience with the supplementary online videos utilised in their subjects are…

Student-Generated Content: Enhancing Learning through Sharing Multiple-Choice Questions

Science.gov (United States)

Hardy, Judy; Bates, Simon P.; Casey, Morag M.; Galloway, Kyle W.; Galloway, Ross K.; Kay, Alison E.; Kirsop, Peter; McQueen, Heather A.

2014-01-01

The relationship between students' use of PeerWise, an online tool that facilitates peer learning through student-generated content in the form of multiple-choice questions (MCQs), and achievement, as measured by their performance in the end-of-module examinations, was investigated in 5 large early-years science modules (in physics, chemistry and…

Effects of task and category membership on representation stability

Directory of Open Access Journals (Sweden)

Céline Manetta

2011-01-01

Full Text Available This study examined the within-subject stability of 150 participants who performed both a sorting task and a property-generation task over multiple sessions, focusing on three concrete concept categories (food, animals and bathroom products. We hypothesized that (1 the within-subject stability would be higher in the sorting task than in the property-generation task and (2 the nature of the category would influence both the within-subject stability of the classification groups in the sorting task and the properties generated to define these groups. The results show that the within-subject stability of conceptual representations depends both on the task and on the nature of the category. The stability of the representations was greater in the sorting task than in the property-generation task and in the food category. These results are discussed from a longitudinal perspective.

External validity of individual differences in multiple cue probability learning: The case of pilot training

Directory of Open Access Journals (Sweden)

Nadine Matton

2013-09-01

Full Text Available Individuals differ in their ability to deal with unpredictable environments. Could impaired performances on learning an unpredictable cue-criteria relationship in a laboratory task be associated with impaired learning of complex skills in a natural setting? We focused on a multiple-cue probability learning (MCPL laboratory task and on the natural setting of pilot training. We used data from three selection sessions and from the three corresponding selected pilot student classes of a national airline pilot selection and training system. First, applicants took an MCPL task at the selection stage (N=556; N=701; N=412. Then, pilot trainees selected from the applicant pools (N=44; N=60; N=28 followed the training for 2.5 to 3 yrs. Differences in final MCPL performance were associated with pilot training difficulties. Indeed, poor MCPL performers experienced almost twice as many pilot training difficulties as better MCPL performers (44.0% and 25.0%, respectively.

Recommender Systems for Learning

CERN Document Server

Manouselis, Nikos; Verbert, Katrien; Duval, Erik

2013-01-01

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

Capacity-oriented curriculum system of optoelectronics in the context of large category cultivation

Science.gov (United States)

Luo, Yuan; Hu, Zhangfang; Zhang, Yi

2017-08-01

In order to cultivate the innovative talents with the comprehensive development to meet the talents demand for development of economic society, Chongqing University of Posts and Telecommunications implements cultivation based on broadening basic education and enrolment in large category of general education. Optoelectronic information science and engineering major belongs to the electronic engineering category. The "2 +2" mode is utilized for personnel training, where students are without major in the first and second year and assigned to a major within the major categories in the end of the second year. In the context of the comprehensive cultivation, for the changes in the demand for professionals in the global competitive environment with the currently rapid development, especially the demand for the professional engineering technology personnel suitable to industry and development of local economic society, the concept of CDIO engineering ability cultivation is used for reference. Thus the curriculum system for the three-node structure optoelectronic information science and engineering major is proposed, which attaches great importance to engineering practice and innovation cultivation under the background of the comprehensive cultivation. The conformity between the curriculum system and the personnel training objectives is guaranteed effectively, and the consistency between the teaching philosophy and the teaching behavior is enhanced. Therefore, the idea of major construction is clear with specific characteristics.

The Development of Learning Management System Using Edmodo

Science.gov (United States)

Joko; Septia Wulandari, Gayuh

2018-04-01

The development of Learning Management System (LMS) can be used as an online learning media by managing the teacher in delivering the material and giving a task. This study aims to: 1) to know the validity of learning devices using LMS with Edmodo, 2) know the student’s response to LMS implementation using Edmodo, and 3) to know the difference of the learning outcome that is students who learned by using LMS with Edmodo and Direct Learning Model (DLM). This research method is quasi experimental by using control group pretest-posttest design. The population of the study was the student at SMKN 1 Sidoarjo. Research sample X TITL 1 class as control goup, and X TITL 2 class as experimental group. The researcher used scale rating to analyze the data validity and students’ respon, and t-test was used to examine the difference of learning outcomes with significant 0.05. The result of the research shows: 1) the average learning device validity use Edmodo 88.14%, lesson plan validity is 92.45%, pretest-posttest validity is 89.15%, learning material validity is 84.64%, and affective and psychomotor-portfolio observation sheets validity is 86.33 included very good criteria or very suitable to be used for research; 2) the result of students’ response questionnaire after taught by using LMS with Edmodo 86.03% in very good category and students agreed that Edmodo can be used in learning; and 3) the learning outcome of LMS by using Edmodo with DLM are: a) there are significant difference of the student cognitive learning outcome which is taught by using Edmodo with the student who use DLM. The average of student learning outcome that is taught LMS using Edmodo is 81.69 compared to student with DLM outcome 76.39, b) there is difference of affective learning outcome that is taught LMS using Edmodo compared to student using DLM. The average of student learning outcomeof affective that is taught LMS by using Edmodo is 83.50 compared students who use DLM 80.34, and c) there is

Optimizing multiple-choice tests as tools for learning.

Science.gov (United States)

Little, Jeri L; Bjork, Elizabeth Ligon

2015-01-01

Answering multiple-choice questions with competitive alternatives can enhance performance on a later test, not only on questions about the information previously tested, but also on questions about related information not previously tested-in particular, on questions about information pertaining to the previously incorrect alternatives. In the present research, we assessed a possible explanation for this pattern: When multiple-choice questions contain competitive incorrect alternatives, test-takers are led to retrieve previously studied information pertaining to all of the alternatives in order to discriminate among them and select an answer, with such processing strengthening later access to information associated with both the correct and incorrect alternatives. Supporting this hypothesis, we found enhanced performance on a later cued-recall test for previously nontested questions when their answers had previously appeared as competitive incorrect alternatives in the initial multiple-choice test, but not when they had previously appeared as noncompetitive alternatives. Importantly, however, competitive alternatives were not more likely than noncompetitive alternatives to be intruded as incorrect responses, indicating that a general increased accessibility for previously presented incorrect alternatives could not be the explanation for these results. The present findings, replicated across two experiments (one in which corrective feedback was provided during the initial multiple-choice testing, and one in which it was not), thus strongly suggest that competitive multiple-choice questions can trigger beneficial retrieval processes for both tested and related information, and the results have implications for the effective use of multiple-choice tests as tools for learning.

Using a quantitative risk register to promote learning from a patient safety reporting system.

Science.gov (United States)

Mansfield, James G; Caplan, Robert A; Campos, John S; Dreis, David F; Furman, Cathie

2015-02-01

Patient safety reporting systems are now used in most health care delivery organizations. These systems, such as the one in use at Virginia Mason (Seattle) since 2002, can provide valuable reports of risk and harm from the front lines of patient care. In response to the challenge of how to quantify and prioritize safety opportunities, a risk register system was developed and implemented. Basic risk register concepts were refined to provide a systematic way to understand risks reported by staff. The risk register uses a comprehensive taxonomy of patient risk and algorithmically assigns each patient safety report to 1 of 27 risk categories in three major domains (Evaluation, Treatment, and Critical Interactions). For each category, a composite score was calculated on the basis of event rate, harm, and cost. The composite scores were used to identify the "top five" risk categories, and patient safety reports in these categories were analyzed in greater depth to find recurrent patterns of risk and associated opportunities for improvement. The top five categories of risk were easy to identify and had distinctive "profiles" of rate, harm, and cost. The ability to categorize and rank risks across multiple dimensions yielded insights not previously available. These results were shared with leadership and served as input for planning quality and safety initiatives. This approach provided actionable input for the strategic planning process, while at the same time strengthening the Virginia Mason culture of safety. The quantitative patient safety risk register serves as one solution to the challenge of extracting valuable safety lessons from large numbers of incident reports and could profitably be adopted by other organizations.

Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations

Science.gov (United States)

Rau, Martina A.; Scheines, Richard

2012-01-01

Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…

Picture reality decision, semantic categories and gender. A new set of pictures, with norms and an experimental study.

Science.gov (United States)

Barbarotto, Riccardo; Laiacona, Marcella; Macchi, Valeria; Capitani, Erminio

2002-01-01

We present a new corpus of 80 pictures of unreal objects, useful for a controlled assessment of object reality decision. The new pictures were assembled from parts of the Snodgrass and Vanderwart [J. Exp. Psychol., Hum. Learning Memory 6; 1980: 174] set and were devised for the purpose of contrasting natural categories (animals, fruits and vegetables), artefacts (tools, vehicles and furniture), body parts and musical instruments. We examined 140 normal subjects in a free-choice and a multiple-choice object decision task, assembled with 80 pictures of real objects and above 80 new pictures of unreal objects in order to obtain a difficulty index for each picture. We found that the tasks were more difficult with pictures representing natural entities than with pictures of artefacts. We found a gender by category interaction, with a female superiority with some natural categories (fruits and vegetables, but not animals), and a male advantage with artefacts. On this basis, the difficulty index we calculated for each picture is separately reported for males and females. We discuss the possible origin of the gender effect, which has been found with the same categories in other tasks and has a counterpart in the different familiarity of the stimuli for males and females. In particular, we contrast explanations based on socially determined gender differences with accounts based on evolutionary pressures. We further comment on the relationship between data from normal subjects and the domain-specific account of semantic category dissociations observed in brain-damaged patients.

A Fuzzy Logic Framework for Integrating Multiple Learned Models

Energy Technology Data Exchange (ETDEWEB)

Hartog, Bobi Kai Den [Univ. of Nebraska, Lincoln, NE (United States)

1999-03-01

The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

Directory of Open Access Journals (Sweden)

Jian Ding

2014-01-01

Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

An explicit statistical model of learning lexical segmentation using multiple cues

NARCIS (Netherlands)

Çöltekin, Ça ̆grı; Nerbonne, John; Lenci, Alessandro; Padró, Muntsa; Poibeau, Thierry; Villavicencio, Aline

2014-01-01

This paper presents an unsupervised and incremental model of learning segmentation that combines multiple cues whose use by children and adults were attested by experimental studies. The cues we exploit in this study are predictability statistics, phonotactics, lexical stress and partial lexical

Comparing Product Category Rules from Different Programs: Learned Outcomes Towards Global Alignment (Presentation)

Science.gov (United States)

Purpose Product category rules (PCRs) provide category-specific guidance for estimating and reporting product life cycle environmental impacts, typically in the form of environmental product declarations and product carbon footprints. Lack of global harmonization between PCRs or ...

Toward a science of learning systems: a research agenda for the high-functioning Learning Health System.

Science.gov (United States)

Friedman, Charles; Rubin, Joshua; Brown, Jeffrey; Buntin, Melinda; Corn, Milton; Etheredge, Lynn; Gunter, Carl; Musen, Mark; Platt, Richard; Stead, William; Sullivan, Kevin; Van Houweling, Douglas

2015-01-01

The capability to share data, and harness its potential to generate knowledge rapidly and inform decisions, can have transformative effects that improve health. The infrastructure to achieve this goal at scale--marrying technology, process, and policy--is commonly referred to as the Learning Health System (LHS). Achieving an LHS raises numerous scientific challenges. The National Science Foundation convened an invitational workshop to identify the fundamental scientific and engineering research challenges to achieving a national-scale LHS. The workshop was planned by a 12-member committee and ultimately engaged 45 prominent researchers spanning multiple disciplines over 2 days in Washington, DC on 11-12 April 2013. The workshop participants collectively identified 106 research questions organized around four system-level requirements that a high-functioning LHS must satisfy. The workshop participants also identified a new cross-disciplinary integrative science of cyber-social ecosystems that will be required to address these challenges. The intellectual merit and potential broad impacts of the innovations that will be driven by investments in an LHS are of great potential significance. The specific research questions that emerged from the workshop, alongside the potential for diverse communities to assemble to address them through a 'new science of learning systems', create an important agenda for informatics and related disciplines. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Matching based on biological categories in Orangutans (Pongo abelii and a Gorilla (Gorilla gorilla gorilla

Directory of Open Access Journals (Sweden)

Jennifer Vonk

2013-09-01

Full Text Available Following a series of experiments in which six orangutans and one gorilla discriminated photographs of different animal species in a two-choice touch screen procedure, Vonk & MacDonald (2002 and Vonk & MacDonald (2004 concluded that orangutans, but not the gorilla, seemed to learn intermediate level category discriminations, such as primates versus non-primates, more rapidly than they learned concrete level discriminations, such as orangutans versus humans. In the current experiments, four of the same orangutans and the gorilla were presented with delayed matching-to-sample tasks in which they were rewarded for matching photos of different members of the same primate species; golden lion tamarins, Japanese macaques, and proboscis monkeys, or family; gibbons, lemurs (Experiment 1, and subsequently for matching photos of different species within the following classes: birds, reptiles, insects, mammals, and fish (Experiment 2. Members of both Great Ape species were rapidly able to match the photos at levels above chance. Orangutans matched images from both category levels spontaneously whereas the gorilla showed effects of learning to match intermediate level categories. The results show that biological knowledge is not necessary to form natural categories at both concrete and intermediate levels.

Intellectual enrichment lessens the effect of brain atrophy on learning and memory in multiple sclerosis.

Science.gov (United States)

Sumowski, James F; Wylie, Glenn R; Chiaravalloti, Nancy; DeLuca, John

2010-06-15

Learning and memory impairments are prevalent among persons with multiple sclerosis (MS); however, such deficits are only weakly associated with MS disease severity (brain atrophy). The cognitive reserve hypothesis states that greater lifetime intellectual enrichment lessens the negative impact of brain disease on cognition, thereby helping to explain the incomplete relationship between brain disease and cognitive status in neurologic populations. The literature on cognitive reserve has focused mainly on Alzheimer disease. The current research examines whether greater intellectual enrichment lessens the negative effect of brain atrophy on learning and memory in patients with MS. Forty-four persons with MS completed neuropsychological measures of verbal learning and memory, and a vocabulary-based estimate of lifetime intellectual enrichment. Brain atrophy was estimated with third ventricle width measured from 3-T magnetization-prepared rapid gradient echo MRIs. Hierarchical regression was used to predict learning and memory with brain atrophy, intellectual enrichment, and the interaction between brain atrophy and intellectual enrichment. Brain atrophy predicted worse learning and memory, and intellectual enrichment predicted better learning; however, these effects were moderated by interactions between brain atrophy and intellectual enrichment. Specifically, higher intellectual enrichment lessened the negative impact of brain atrophy on both learning and memory. These findings help to explain the incomplete relationship between multiple sclerosis disease severity and cognition, as the effect of disease on cognition is attenuated among patients with higher intellectual enrichment. As such, intellectual enrichment is supported as a protective factor against disease-related cognitive impairment in persons with multiple sclerosis.

Feature-Based versus Category-Based Induction with Uncertain Categories

Science.gov (United States)

Griffiths, Oren; Hayes, Brett K.; Newell, Ben R.

2012-01-01

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is…

Data categories for marine planning

Science.gov (United States)

Lightsom, Frances L.; Cicchetti, Giancarlo; Wahle, Charles M.

2015-01-01

The U.S. National Ocean Policy calls for a science- and ecosystem-based approach to comprehensive planning and management of human activities and their impacts on America’s oceans. The Ocean Community in Data.gov is an outcome of 2010–2011 work by an interagency working group charged with designing a national information management system to support ocean planning. Within the working group, a smaller team developed a list of the data categories specifically relevant to marine planning. This set of categories is an important consensus statement of the breadth of information types required for ocean planning from a national, multidisciplinary perspective. Although the categories were described in a working document in 2011, they have not yet been fully implemented explicitly in online services or geospatial metadata, in part because authoritative definitions were not created formally. This document describes the purpose of the data categories, provides definitions, and identifies relations among the categories and between the categories and external standards. It is intended to be used by ocean data providers, managers, and users in order to provide a transparent and consistent framework for organizing and describing complex information about marine ecosystems and their connections to humans.