Observation versus classification in supervised category learning.

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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.

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

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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?

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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.

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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.

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

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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.

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

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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).

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

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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.

Chromatic Perceptual Learning but No Category Effects without Linguistic Input.

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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.

Individual differences in attention during category learning

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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

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

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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.

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

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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

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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

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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.

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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.

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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.

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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.

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

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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?

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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…

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

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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.

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

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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.

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

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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.

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

Study preferences for exemplar variability in self-regulated category learning.

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Wahlheim, Christopher N; DeSoto, K Andrew

2017-02-01

Increasing exemplar variability during category learning can enhance classification of novel exemplars from studied categories. Four experiments examined whether participants preferred variability when making study choices with the goal of later classifying novel exemplars. In Experiments 1-3, participants were familiarised with exemplars of birds from multiple categories prior to making category-level assessments of learning and subsequent choices about whether to receive more variability or repetitions of exemplars during study. After study, participants classified novel exemplars from studied categories. The majority of participants showed a consistent preference for variability in their study, but choices were not related to category-level assessments of learning. Experiment 4 provided evidence that study preferences were based primarily on theoretical beliefs in that most participants indicated a preference for variability on questionnaires that did not include prior experience with exemplars. Potential directions for theoretical development and applications to education are discussed.

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

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

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

2013-01-01

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

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

Science.gov (United States)

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

2013-10-01

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

Heterogeneity in Perceptual Category Learning by High Functioning Children with Autism Spectrum Disorder

Directory of Open Access Journals (Sweden)

Eduardo eMercado

2015-06-01

Full Text Available Previous research suggests that high functioning children with Autism Spectrum Disorder (ASD sometimes have problems learning categories, but often appear to perform normally in categorization tasks. The deficits that individuals with ASD show when learning categories have been attributed to executive dysfunction, general deficits in implicit learning, atypical cognitive strategies, or abnormal perceptual biases and abilities. Several of these psychological explanations for category learning deficits have been associated with neural abnormalities such as cortical underconnectivity. The present study evaluated how well existing neurally-based theories account for atypical perceptual category learning shown by high functioning children with ASD across multiple category learning tasks involving novel, abstract shapes. Consistent with earlier results, children’s performances revealed two distinct patterns of learning and generalization associated with ASD: one was indistinguishable from performance in typically developing children; the other revealed dramatic impairments. These two patterns were evident regardless of training regimen or stimulus set. Surprisingly, some children with ASD showed both patterns. Simulations of perceptual category learning could account for the two observed patterns in terms of differences in neural plasticity. However, no current psychological or neural theory adequately explains why a child with ASD might show such large fluctuations in category learning ability across training conditions or stimulus sets.

Heterogeneity in perceptual category learning by high functioning children with autism spectrum disorder.

Science.gov (United States)

Mercado, Eduardo; Church, Barbara A; Coutinho, Mariana V C; Dovgopoly, Alexander; Lopata, Christopher J; Toomey, Jennifer A; Thomeer, Marcus L

2015-01-01

Previous research suggests that high functioning (HF) children with autism spectrum disorder (ASD) sometimes have problems learning categories, but often appear to perform normally in categorization tasks. The deficits that individuals with ASD show when learning categories have been attributed to executive dysfunction, general deficits in implicit learning, atypical cognitive strategies, or abnormal perceptual biases and abilities. Several of these psychological explanations for category learning deficits have been associated with neural abnormalities such as cortical underconnectivity. The present study evaluated how well existing neurally based theories account for atypical perceptual category learning shown by HF children with ASD across multiple category learning tasks involving novel, abstract shapes. Consistent with earlier results, children's performances revealed two distinct patterns of learning and generalization associated with ASD: one was indistinguishable from performance in typically developing children; the other revealed dramatic impairments. These two patterns were evident regardless of training regimen or stimulus set. Surprisingly, some children with ASD showed both patterns. Simulations of perceptual category learning could account for the two observed patterns in terms of differences in neural plasticity. However, no current psychological or neural theory adequately explains why a child with ASD might show such large fluctuations in category learning ability across training conditions or stimulus sets.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

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Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

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

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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

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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

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

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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

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

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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

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

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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.

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

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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.

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

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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.

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

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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.

Finding biomedical categories in Medline®

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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.

INCREASES IN FUNCTIONAL CONNECTIVITY BETWEEN PREFRONTAL CORTEX AND STRIATUM DURING CATEGORY LEARNING

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Antzoulatos, Evan G.; Miller, Earl K.

2014-01-01

SUMMARY Functional connectivity between the prefrontal cortex (PFC) and striatum (STR) is thought critical for cognition, and has been linked to conditions like autism and schizophrenia. We recorded from multiple electrodes in PFC and STR while monkeys acquired new categories. Category learning was accompanied by an increase in beta-band synchronization of LFPs between, but not within, the PFC and STR. After learning, different pairs of PFC-STR electrodes showed stronger synchrony for one or the other category, suggesting category-specific functional circuits. This category-specific synchrony was also seen between PFC spikes and STR LFPs, but not the reverse, reflecting the direct monosynaptic connections from the PFC to STR. However, causal connectivity analyses suggested that the polysynaptic connections from STR to the PFC exerted a stronger overall influence. This supports models positing that the basal ganglia “train” the PFC. Category learning may depend on the formation of functional circuits between the PFC and STR. PMID:24930701

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

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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.

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

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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.

The Role of Age and Executive Function in Auditory Category Learning

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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

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

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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

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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…

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

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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.

Multi-task learning with group information for human action recognition

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Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning.

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Grimm, Lisa R; Maddox, W Todd

2013-11-01

Research has identified multiple category-learning systems with each being "tuned" for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning. © 2013.

Concurrent Dynamics of Category Learning and Metacognitive Judgments

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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.

Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

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Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

2004-01-01

Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

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

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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).

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...

Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers.

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Chen, Chi-Hsin; Gershkoff-Stowe, Lisa; Wu, Chih-Yi; Cheung, Hintat; Yu, Chen

2017-08-01

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co-occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities. Copyright © 2016 Cognitive Science Society, Inc.

Dental and Medical Students' Use and Perceptions of Learning Resources in a Human Physiology Course.

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Tain, Monica; Schwartzstein, Richard; Friedland, Bernard; Park, Sang E

2017-09-01

The aim of this study was to determine the use and perceived utility of various learning resources available during the first-year Integrated Human Physiology course at the dental and medical schools at Harvard University. Dental and medical students of the Class of 2018 were surveyed anonymously online in 2015 regarding their use of 29 learning resources in this combined course. The learning resources had been grouped into four categories to discern frequency of use and perceived usefulness among the categories. The survey was distributed to 169 students, and 73 responded for a response rate of 43.2%. There was no significant difference among the learning resource categories in frequency of use; however, there was a statistically significant difference among categories in students' perceptions of usefulness. No correlation was found between frequency of use and perceived usefulness of each category. Students seemingly were not choosing the most useful resources for them. These results suggest that, in the current educational environment, where new technologies and self-directed learning are highly sought after, there remains a need for instructor-guided 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, ...

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

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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.

Dissociation of Category-Learning Systems via Brain Potentials

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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.

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

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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.

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems

OpenAIRE

Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed...

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

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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.

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

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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.

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

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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…

Models as Relational Categories

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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.

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

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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.

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

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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

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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.

A deep learning method for classifying mammographic breast density categories.

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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

Building phonetic categories: an argument for the role of sleep

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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.

Two Pathways to Stimulus Encoding in Category Learning?

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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

Human cloning: category, dignity, and the role of bioethics.

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Shuster, Evelyne

2003-10-01

Human cloning has been simultaneously a running joke for massive worldwide publicity of fringe groups like the Raelians, and the core issue of an international movement at the United Nations in support of a treaty to ban the use of cloning techniques to produce a child (so called reproductive cloning). Yet, even though debates on human cloning have greatly increased since the birth of Dolly, the clone sheep, in 1997, we continue to wonder whether cloning is after all any different from other methods of medically assisted reproduction, and what exactly makes cloning an 'affront to the dignity of humans.' Categories we adopt matter mightily as they inform but can also misinform and lead to mistaken and unproductive decisions. And thus bioethicists have a responsibility to ensure that the proper categories are used in the cloning debates and denounce those who try to win the ethical debate through well-crafted labels rather than well-reasoned argumentations. But it is as important for bioethicists to take a position on broad issues such as human cloning and species altering interventions. One 'natural question' would be, for example, should there be an international treaty to ban human reproductive cloning?

Effective post-literacy learning: A question of a national human resource strategy

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Ahmed, Manzoor

1989-12-01

Initial literacy courses must be followed by opportunities for consolidating the mechanics of literacy skills and practical application of three skills in life. Experience has shown that these `post-literacy' objectives can be achieved, not by a second stage of the literacy course, but by a range of opportunities for learning and application of learning through a network of continuing education opportunities geared to the diverse needs and circumstances of different categories of neo-literates. A taxonomy of learner categories and learning needs is seen as a basis for planning and supporting the network of post-literacy learning. Examples from China, India and Thailand demonstrate the importance of recognizing the continuity of literacy and post-literacy efforts, the need for commitment of resources for this continuum of learning, the role of an organizational structure to deal with this continuum in a coordinated way, and the value of a comprehensive range of learning opportunities for neo-literates. A necessary condition for success in building a network of continuing learning opportunities and contributing to the creation of a `learning society' is to make human resource development the core of national development. It is argued that the scope and dimensions of post-literacy continuing education are integrally linked with the goal of mass basic education and ultimately with the vision of a `learning society'. Such a vision can be a reality only with a serious human resource development focus in national development that will permit the necessary mobilization of resources, the coordination of sectors of government and society and the generation of popular enthusiasm. A radical or an incremental approach can be taken to move towards the primacy of a human resource strategy in national development. In either case, a functioning coordination and support mechanism has to be developed for the key elements of mass basic education including post-literacy learning.

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

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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 Effect of Feedback Delay on Perceptual Category Learning and Item Memory: Further Limits of Multiple Systems.

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Stephens, Rachel G; Kalish, Michael L

2018-02-01

Delayed feedback during categorization training has been hypothesized to differentially affect 2 systems that underlie learning for rule-based (RB) or information-integration (II) structures. We tested an alternative possibility: that II learning requires more precise item representations than RB learning, and so is harmed more by a delay interval filled with a confusable mask. Experiments 1 and 2 examined the effect of feedback delay on memory for RB and II exemplars, both without and with concurrent categorization training. Without the training, II items were indeed more difficult to recognize than RB items, but there was no detectable effect of delay on item memory. In contrast, with concurrent categorization training, there were effects of both category structure and delayed feedback on item memory, which were related to corresponding changes in category learning. However, we did not observe the critical selective impact of delay on II classification performance that has been shown previously. Our own results were also confirmed in a follow-up study (Experiment 3) involving only categorization training. The selective influence of feedback delay on II learning appears to be contingent on the relative size of subgroups of high-performing participants, and in fact does not support that RB and II category learning are qualitatively different. We conclude that a key part of successfully solving perceptual categorization problems is developing more precise item representations, which can be impaired by delayed feedback during training. More important, the evidence for multiple systems of category learning is even weaker than previously proposed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Can machine learning explain human learning?

NARCIS (Netherlands)

Vahdat, M.; Oneto, L.; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

2016-01-01

Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is

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

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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.

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

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Gabay, Yafit; Goldfarb, Liat

2017-07-01

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

Competitive debate classroom as a cooperative learning technique for the human resources subject

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Guillermo A. SANCHEZ PRIETO

2018-01-01

Full Text Available The paper shows an academic debate model as a cooperative learning technique for teaching human resources at University. The general objective of this paper is to conclude if academic debate can be included in the category of cooperative learning. The Specific objective it is presenting a model to implement this technique. Thus the first part of the paper shows the concept of cooperative learning and its main characteristics. The second part presents the debate model believed to be labelled as cooperative learning. Last part concludes with the characteristics of the model that match different aspects or not of the cooperative learning.

Categorial compositionality: a category theory explanation for the systematicity of human cognition.

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Steven Phillips

Full Text Available Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes. In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe--replaced by the relationships between the maps that transform them.

Categorial compositionality: a category theory explanation for the systematicity of human cognition.

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Phillips, Steven; Wilson, William H

2010-07-22

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe--replaced by the relationships between the maps that transform them.

Human Machine Learning Symbiosis

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Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

2017-01-01

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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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

Human semi-supervised learning.

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Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

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

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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.…

Learning and Retention through Predictive Inference and Classification

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Sakamoto, Yasuaki; Love, Bradley C.

2010-01-01

Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and…

Object-graphs for context-aware visual category discovery.

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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.

Definitions of the categories that determine the role of human in social and economic processes

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Smachylo Valentina

2016-01-01

Full Text Available The priorities of the national economy development indicate the growing role of a person in the economic process of creating added value and capitalization of enterprises that require new approaches to the management process in this area. This requires the definition of basic categories that define the role and place of man in the socio-economic processes and characterise a person in the process of work. The article defines the basic aspects that must be considered in the study of the categories «staff», «personnel», «human resources», «cadre», «human potential», «cadre potential», «economically active population», «employment potential», «human capital»: evolution of concepts, level of socio-economic development, the presence or potentiality of human resources, the level of research, management paradigm. The essence, differentiation and interrelation of the given categories in the specified areas are justified. The necessity of socially responsible approach to management is underlined.

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

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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…

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

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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.

Human simulations of vocabulary learning.

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Gillette, J; Gleitman, H; Gleitman, L; Lederer, A

1999-12-07

The work reported here experimentally investigates a striking generalization about vocabulary acquisition: Noun learning is superior to verb learning in the earliest moments of child language development. The dominant explanation of this phenomenon in the literature invokes differing conceptual requirements for items in these lexical categories: Verbs are cognitively more complex than nouns and so their acquisition must await certain mental developments in the infant. In the present work, we investigate an alternative hypothesis; namely, that it is the information requirements of verb learning, not the conceptual requirements, that crucially determine the acquisition order. Efficient verb learning requires access to structural features of the exposure language and thus cannot take place until a scaffolding of noun knowledge enables the acquisition of clause-level syntax. More generally, we experimentally investigate the hypothesis that vocabulary acquisition takes place via an incremental constraint-satisfaction procedure that bootstraps itself into successively more sophisticated linguistic representations which, in turn, enable new kinds of vocabulary learning. If the experimental subjects were young children, it would be difficult to distinguish between this information-centered hypothesis and the conceptual change hypothesis. Therefore the experimental "learners" are adults. The items to be "acquired" in the experiments were the 24 most frequent nouns and 24 most frequent verbs from a sample of maternal speech to 18-24-month-old infants. The various experiments ask about the kinds of information that will support identification of these words as they occur in mother-to-child discourse. Both the proportion correctly identified and the type of word that is identifiable changes significantly as a function of information type. We discuss these results as consistent with the incremental construction of a highly lexicalized grammar by cognitively and pragmatically

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

Science.gov (United States)

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

2013-11-01

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

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.

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.

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

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...

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

How categories come to matter

DEFF Research Database (Denmark)

Leahu, Lucian; Cohn, Marisa; March, Wendy

2013-01-01

In a study of users' interactions with Siri, the iPhone personal assistant application, we noticed the emergence of overlaps and blurrings between explanatory categories such as "human" and "machine". We found that users work to purify these categories, thus resolving the tensions related to the ...... initial data analysis, due to our own forms of latent purification, and outline the particular analytic techniques that helped lead to this discovery. We thus provide an illustrative case of how categories come to matter in HCI research and design.......In a study of users' interactions with Siri, the iPhone personal assistant application, we noticed the emergence of overlaps and blurrings between explanatory categories such as "human" and "machine". We found that users work to purify these categories, thus resolving the tensions related...

[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.

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…

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

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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.

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

Vicarious learning from human models in monkeys.

Science.gov (United States)

Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

2012-01-01

We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

Managing Human Resource Learning for Innovation

DEFF Research Database (Denmark)

Nielsen, Peter

Managing human resource learning for innovation develops a systemic understanding of building innovative capabilities. Building innovative capabilities require active creation, coordination and absorption of useful knowledge and thus a cohesive management approach to learning. Often learning...... in organizations and work is approached without considerations on how to integrate it in the management of human resources. The book investigates the empirical conditions for managing human resources learning for innovation. With focus on innovative performance the importance of modes of innovation, clues...

Vicarious learning from human models in monkeys.

Directory of Open Access Journals (Sweden)

Rossella Falcone

Full Text Available We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

Robot learning from human teachers

CERN Document Server

Chernova, Sonia

2014-01-01

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn f

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.

Vicarious Learning from Human Models in Monkeys

OpenAIRE

Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

2012-01-01

We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was app...

Actively learning human gaze shifting paths for semantics-aware photo cropping.

Science.gov (United States)

Zhang, Luming; Gao, Yue; Ji, Rongrong; Xia, Yingjie; Dai, Qionghai; Li, Xuelong

2014-05-01

Photo cropping is a widely used tool in printing industry, photography, and cinematography. Conventional cropping models suffer from the following three challenges. First, the deemphasized role of semantic contents that are many times more important than low-level features in photo aesthetics. Second, the absence of a sequential ordering in the existing models. In contrast, humans look at semantically important regions sequentially when viewing a photo. Third, the difficulty of leveraging inputs from multiple users. Experience from multiple users is particularly critical in cropping as photo assessment is quite a subjective task. To address these challenges, this paper proposes semantics-aware photo cropping, which crops a photo by simulating the process of humans sequentially perceiving semantically important regions of a photo. We first project the local features (graphlets in this paper) onto the semantic space, which is constructed based on the category information of the training photos. An efficient learning algorithm is then derived to sequentially select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path, which simulates humans actively perceiving semantics in a photo. Furthermore, we learn a prior distribution of such active graphlet paths from training photos that are marked as aesthetically pleasing by multiple users. The learned priors enforce the corresponding active graphlet path of a test photo to be maximally similar to those from the training photos. Experimental results show that: 1) the active graphlet path accurately predicts human gaze shifting, and thus is more indicative for photo aesthetics than conventional saliency maps and 2) the cropped photos produced by our approach outperform its competitors in both qualitative and quantitative comparisons.

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.

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…

Accounting for individual differences in human associative learning.

Science.gov (United States)

Byrom, Nicola C

2013-09-04

Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility in learning caused by external factors, there has been limited work considering how to model the influence of dispositional factors. This review looks at the range of individual differences in human associative learning that have been explored and the attempts to account for, and model, this flexibility. To fully understand human associative learning, further research needs to attend to the causes of variation in human learning.

Accounting for Individual Differences in Human Associative Learning.

Directory of Open Access Journals (Sweden)

Nicola C Byrom

2013-09-01

Full Text Available Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility in learning caused by external factors, there has been limited work considering how to model the influence of dispositional factors. This review looks at the range of individual differences in human associative learning that have been explored and the attempts to account for, and model, this flexibility. To fully understand human associative learning, further research needs to attend to the causes of variation in human learning.

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…

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.

The structure and formation of natural categories

Science.gov (United States)

Fisher, Douglas; Langley, Pat

1990-01-01

Categorization and concept formation are critical activities of intelligence. These processes and the conceptual structures that support them raise important issues at the interface of cognitive psychology and artificial intelligence. The work presumes that advances in these and other areas are best facilitated by research methodologies that reward interdisciplinary interaction. In particular, a computational model is described of concept formation and categorization that exploits a rational analysis of basic level effects by Gluck and Corter. Their work provides a clean prescription of human category preferences that is adapted to the task of concept learning. Also, their analysis was extended to account for typicality and fan effects, and speculate on how the concept formation strategies might be extended to other facets of intelligence, such as problem solving.

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.

Human Learning and Memory

Science.gov (United States)

Lieberman, David A.

2012-01-01

This innovative textbook is the first to integrate learning and memory, behaviour, and cognition. It focuses on fascinating human research in both memory and learning (while also bringing in important animal studies) and brings the reader up to date with the latest developments in the subject. Students are encouraged to think critically: key…

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.

Social categories as markers of intrinsic interpersonal obligations.

Science.gov (United States)

Rhodes, Marjorie; Chalik, Lisa

2013-06-01

Social categorization is an early-developing feature of human social cognition, yet the role that social categories play in children's understanding of and predictions about human behavior has been unclear. In the studies reported here, we tested whether a foundational functional role of social categories is to mark people as intrinsically obligated to one another (e.g., obligated to protect rather than harm). In three studies, children (aged 3-9, N = 124) viewed only within-category harm as violating intrinsic obligations; in contrast, they viewed between-category harm as violating extrinsic obligations defined by explicit rules. These data indicate that children view social categories as marking patterns of intrinsic interpersonal obligations, suggesting that a key function of social categories is to support inferences about how people will relate to members of their own and other groups.

Human capital and human resource management to achieve ambidextrous learning: A structural perspective

Directory of Open Access Journals (Sweden)

Mirta Diaz-Fernandez

2017-01-01

Full Text Available Organisational learning has become increasingly important for strategic renewal. Ambidextrous organisations are especially successful in the current environment, where firms are required to be efficient and adapt to change. Using a structural approach, this study discusses arguments about the nature of ambidexterity and identifies the kinds of human capital that better support specific learning types and HRM practices suited to these components of human capital. Results highlight learning differences between marketing and production units, as well as different HRM practices and human capital orientations. This study points out that human capital mediates between HRM practices and learning.

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…

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.

The evolutionary basis of human social learning.

Science.gov (United States)

Morgan, T J H; Rendell, L E; Ehn, M; Hoppitt, W; Laland, K N

2012-02-22

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.

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.

Accounting for individual differences in human associative learning

OpenAIRE

Byrom, Nicola C.

2013-01-01

Associative learning has provided fundamental insights to understanding psychopathology. However, psychopathology occurs along a continuum and as such, identification of disruptions in processes of associative learning associated with aspects of psychopathology illustrates a general flexibility in human associative learning. A handful of studies have looked specifically at individual differences in human associative learning, but while much work has concentrated on accounting for flexibility ...

Perceptual learning and human expertise.

Science.gov (United States)

Kellman, Philip J; Garrigan, Patrick

2009-06-01

We consider perceptual learning: experience-induced changes in the way perceivers extract information. Often neglected in scientific accounts of learning and in instruction, perceptual learning is a fundamental contributor to human expertise and is crucial in domains where humans show remarkable levels of attainment, such as language, chess, music, and mathematics. In Section 2, we give a brief history and discuss the relation of perceptual learning to other forms of learning. We consider in Section 3 several specific phenomena, illustrating the scope and characteristics of perceptual learning, including both discovery and fluency effects. We describe abstract perceptual learning, in which structural relationships are discovered and recognized in novel instances that do not share constituent elements or basic features. In Section 4, we consider primary concepts that have been used to explain and model perceptual learning, including receptive field change, selection, and relational recoding. In Section 5, we consider the scope of perceptual learning, contrasting recent research, focused on simple sensory discriminations, with earlier work that emphasized extraction of invariance from varied instances in more complex tasks. Contrary to some recent views, we argue that perceptual learning should not be confined to changes in early sensory analyzers. Phenomena at various levels, we suggest, can be unified by models that emphasize discovery and selection of relevant information. In a final section, we consider the potential role of perceptual learning in educational settings. Most instruction emphasizes facts and procedures that can be verbalized, whereas expertise depends heavily on implicit pattern recognition and selective extraction skills acquired through perceptual learning. We consider reasons why perceptual learning has not been systematically addressed in traditional instruction, and we describe recent successful efforts to create a technology of perceptual

Perceptual learning and human expertise

Science.gov (United States)

Kellman, Philip J.; Garrigan, Patrick

2009-06-01

We consider perceptual learning: experience-induced changes in the way perceivers extract information. Often neglected in scientific accounts of learning and in instruction, perceptual learning is a fundamental contributor to human expertise and is crucial in domains where humans show remarkable levels of attainment, such as language, chess, music, and mathematics. In Section 2, we give a brief history and discuss the relation of perceptual learning to other forms of learning. We consider in Section 3 several specific phenomena, illustrating the scope and characteristics of perceptual learning, including both discovery and fluency effects. We describe abstract perceptual learning, in which structural relationships are discovered and recognized in novel instances that do not share constituent elements or basic features. In Section 4, we consider primary concepts that have been used to explain and model perceptual learning, including receptive field change, selection, and relational recoding. In Section 5, we consider the scope of perceptual learning, contrasting recent research, focused on simple sensory discriminations, with earlier work that emphasized extraction of invariance from varied instances in more complex tasks. Contrary to some recent views, we argue that perceptual learning should not be confined to changes in early sensory analyzers. Phenomena at various levels, we suggest, can be unified by models that emphasize discovery and selection of relevant information. In a final section, we consider the potential role of perceptual learning in educational settings. Most instruction emphasizes facts and procedures that can be verbalized, whereas expertise depends heavily on implicit pattern recognition and selective extraction skills acquired through perceptual learning. We consider reasons why perceptual learning has not been systematically addressed in traditional instruction, and we describe recent successful efforts to create a technology of perceptual

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).

Working memory supports inference learning just like classification learning.

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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.

Basic level category structure emerges gradually across human ventral visual cortex.

Science.gov (United States)

Iordan, Marius Cătălin; Greene, Michelle R; Beck, Diane M; Fei-Fei, Li

2015-07-01

Objects can be simultaneously categorized at multiple levels of specificity ranging from very broad ("natural object") to very distinct ("Mr. Woof"), with a mid-level of generality (basic level: "dog") often providing the most cognitively useful distinction between categories. It is unknown, however, how this hierarchical representation is achieved in the brain. Using multivoxel pattern analyses, we examined how well each taxonomic level (superordinate, basic, and subordinate) of real-world object categories is represented across occipitotemporal cortex. We found that, although in early visual cortex objects are best represented at the subordinate level (an effect mostly driven by low-level feature overlap between objects in the same category), this advantage diminishes compared to the basic level as we move up the visual hierarchy, disappearing in object-selective regions of occipitotemporal cortex. This pattern stems from a combined increase in within-category similarity (category cohesion) and between-category dissimilarity (category distinctiveness) of neural activity patterns at the basic level, relative to both subordinate and superordinate levels, suggesting that successive visual areas may be optimizing basic level representations.

Using human brain activity to guide machine learning.

Science.gov (United States)

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

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…

Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not.

Science.gov (United States)

MacDorman, Karl F; Chattopadhyay, Debaleena

2016-01-01

Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica's realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes-eyelashes-mouth and skin-nose-eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations-those eliciting the greatest category uncertainty-were neither the eeriest nor the coldest. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Haptic Human-Human Interaction Through a Compliant Connection Does Not Improve Motor Learning in a Force Field

NARCIS (Netherlands)

Beckers, Niek; Keemink, Arvid; van Asseldonk, Edwin; van der Kooij, Herman; Prattichizzo, Domenico; Shinoda, Hiroyuki; Tan, Hong Z.; Ruffaldi, Emanuele; Frisoli, Antonio

2018-01-01

Humans have a natural ability to haptically interact with other humans, for instance during physically assisting a child to learn how to ride a bicycle. A recent study has shown that haptic human-human interaction can improve individual motor performance and motor learning rate while learning to

Towards Statistical Unsupervised Online Learning for Music Listening with Hearing Devices

DEFF Research Database (Denmark)

Purwins, Hendrik; Marchini, Marco; Marxer, Richard

of sounds into phonetic/instrument categories and learning of instrument event sequences is performed jointly using a Hierarchical Dirichlet Process Hidden Markov Model. Whereas machines often learn by processing a large data base and subsequently updating parameters of the algorithm, humans learn...... and their respective transition counts. We propose to use online learning for the co-evolution of both CI user and machine in (re-)learning musical language. [1] Marco Marchini and Hendrik Purwins. Unsupervised analysis and generation of audio percussion sequences. In International Symposium on Computer Music Modeling...... categories) as well as the temporal context horizon (e.g. storing up to 2-note sequences or up to 10-note sequences) is adaptable. The framework in [1] is based on two cognitively plausible principles: unsupervised learning and statistical learning. Opposed to supervised learning in primary school children...

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

21 CFR 330.5 - Drug categories.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false Drug categories. 330.5 Section 330.5 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS FOR HUMAN...) Stimulants. (r) Antitussives. (s) Allergy treatment products. (t) Cold remedies. (u) Antirheumatic products...

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...

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.

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…

Human Spaceflight Conjunction Assessment: Lessons Learned

Science.gov (United States)

Smith, Jason T.

2011-01-01

This viewgraph presentation reviews the process of a human space flight conjunction assessment and lessons learned from the more than twelve years of International Space Station (ISS) operations. Also, the application of these lessons learned to a recent ISS conjunction assessment with object 84180 on July 16, 2009 is also presented.

ROBOT LEARNING OF OBJECT MANIPULATION TASK ACTIONS FROM HUMAN DEMONSTRATIONS

Directory of Open Access Journals (Sweden)

Maria Kyrarini

2017-08-01

Full Text Available Robot learning from demonstration is a method which enables robots to learn in a similar way as humans. In this paper, a framework that enables robots to learn from multiple human demonstrations via kinesthetic teaching is presented. The subject of learning is a high-level sequence of actions, as well as the low-level trajectories necessary to be followed by the robot to perform the object manipulation task. The multiple human demonstrations are recorded and only the most similar demonstrations are selected for robot learning. The high-level learning module identifies the sequence of actions of the demonstrated task. Using Dynamic Time Warping (DTW and Gaussian Mixture Model (GMM, the model of demonstrated trajectories is learned. The learned trajectory is generated by Gaussian mixture regression (GMR from the learned Gaussian mixture model.  In online working phase, the sequence of actions is identified and experimental results show that the robot performs the learned task successfully.

Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning.

Science.gov (United States)

Oudeyer, Pierre-Yves

2017-01-01

Autonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

Right to Development and Right to the City : A Proposal of Human Rights Categories Universal as assumptions Citizenship

Directory of Open Access Journals (Sweden)

Alessandra Danielle Carneiro dos Santos Hilário

2016-05-01

Full Text Available This article discusses the Right to the City, in a conceptual dimension and wide, and his dialectical relationship with the Universal Declaration of Human Rights of 1948 and its universalism and cultural relativism categories. The Right to the City (RtC is capitula- ted as one of the categories of the Human Right to Development from the compartments on Human Rights to descend from the Universal Declaration of Human Rights. Linked to this assumption, the discussion of universalism and cultural relativism theories bring to the fore important questions and considerations as to RtC condition, since in its current design and trampled by an evil legacy of neoliberalism, this right has demonstrated the need for authoritative action of the State, given the nature of fundamental human right of the third dimension. Through RtC, boasts up of economic, social and cultural rights, requiring a positive action of the state as compliance guarantee this human right. In this bias, relevant are discussions about the concept of law, morality, liberalism, effectiveness and universality of human rights theories and cultural relativism in dialectic with the RtC and its complexity. It starts from the assumption that the Universal Declaration of Human Rights and other statements which have descended universality (despite criticism, however, this har- vest, it is imperative closer examination of the concept, forecast, guarantee and effective- ness fundamental human rights, which may lead to a mixed application of universalistic and relativistic theories when analyzed from the perspective of these institutes. The Hu- man Right to Development (RtD presupposes notions of environmental sustainability and economic democracy, with qualified participation of social subjects (wide citizenship, seen continuous and articulated perspective as guiding the development process.

Dissociable Learning Processes Underlie Human Pain Conditioning.

Science.gov (United States)

Zhang, Suyi; Mano, Hiroaki; Ganesh, Gowrishankar; Robbins, Trevor; Seymour, Ben

2016-01-11

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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…

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.

Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

Science.gov (United States)

Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

2009-10-28

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

Structure Mapping for Social Learning.

Science.gov (United States)

Christie, Stella

2017-07-01

Analogical reasoning is a foundational tool for human learning, allowing learners to recognize relational structures in new events and domains. Here I sketch some grounds for understanding and applying analogical reasoning in social learning. The social world is fundamentally characterized by relations between people, with common relational structures-such as kinships and social hierarchies-forming social units that dictate social behaviors. Just as young learners use analogical reasoning for learning relational structures in other domains-spatial relations, verbs, relational categories-analogical reasoning ought to be a useful cognitive tool for acquiring social relations and structures. Copyright © 2017 Cognitive Science Society, Inc.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

Science.gov (United States)

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

On Logical Characterisation of Human Concept Learning based on Terminological Systems

DEFF Research Database (Denmark)

Badie, Farshad

2018-01-01

The central focus of this article is the epistemological assumption that knowledge could be generated based on human beings' experiences and over their conceptions of the world. Logical characterisation of human inductive learning over their produced conceptions within terminological systems and ...... and analysis of actual human inductive reasoning (and learning). This research connects with the topics 'logic & learning', 'cognitive modelling' and 'terminological knowledge representation'.......The central focus of this article is the epistemological assumption that knowledge could be generated based on human beings' experiences and over their conceptions of the world. Logical characterisation of human inductive learning over their produced conceptions within terminological systems...

Amplifying human ability through autonomics and machine learning in IMPACT

Science.gov (United States)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

Prediction of skin sensitization potency using machine learning approaches.

Science.gov (United States)

Zang, Qingda; Paris, Michael; Lehmann, David M; Bell, Shannon; Kleinstreuer, Nicole; Allen, David; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Strickland, Judy

2017-07-01

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Associative learning and the control of human dietary behavior.

Science.gov (United States)

Brunstrom, Jeffrey M

2007-07-01

Most of our food likes and disliked are learned. Relevant forms of associative learning have been identified in animals. However, observations of the same associative processes are relatively scarce in humans. The first section of this paper outlines reasons why this might be the case. Emphasis is placed on recent research exploring individual differences and the importance or otherwise of hunger and contingency awareness. The second section briefly considers the effect of learning on meal size, and the author revisits the question of how learned associations might come to influence energy intake in humans.

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

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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 ...

Human-Guided Learning for Probabilistic Logic Models

Directory of Open Access Journals (Sweden)

Phillip Odom

2018-06-01

Full Text Available Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role of the human has been restricted to being a “mere labeler” in recent times. We hypothesize and demonstrate that probabilistic logic can provide an effective and natural way for the expert to specify domain advice. Specifically, we consider different types of advice-giving in relational domains where noise could arise due to systematic errors or class-imbalance inherent in the domains. The advice is provided as logical statements or privileged features that are thenexplicitly considered by an iterative learning algorithm at every update. Our empirical evidence shows that human advice can effectively accelerate learning in noisy, structured domains where so far humans have been merely used as labelers or as designers of the (initial or final structure of the model.

Error Discounting in Probabilistic Category Learning

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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…

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.

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

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Omurtag, Ahmet; Aghajani, Haleh; Onur Keles, Hasan

2017-12-01

Objective. Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system’s ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results. EEG+fNIRS’s decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance. Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Learning with hierarchical-deep models.

Science.gov (United States)

Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

Learning Semantics of Gestural Instructions for Human-Robot Collaboration

Science.gov (United States)

Shukla, Dadhichi; Erkent, Özgür; Piater, Justus

2018-01-01

Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions. PMID:29615888

Learning Semantics of Gestural Instructions for Human-Robot Collaboration.

Science.gov (United States)

Shukla, Dadhichi; Erkent, Özgür; Piater, Justus

2018-01-01

Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions.

Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic

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Sabine van der Ham

2015-10-01

Full Text Available When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults’ generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants’ reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories.

Peak Shift in Pigeon and Human Categorisation

OpenAIRE

Aitken, Michael R. F.

1996-01-01

In a series of experiments, both pigeon and human subjects were trained to categorise two groups of confusable stimuli, with each category being made up of distortions of a ‘Prototype’. Once the subjects had successfully learned to categorise the training stimuli, they were tested on their responding to a variety of previously unseen stimuli: these were distortions of the Prototypes towards (‘Closer’ exemplars), or away from (‘Further’ exemplars), the other category, and the Prototypes thems...

Approximation to the distribution of fitness effects across functional categories in human segregating polymorphisms.

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Fernando Racimo

2014-11-01

Full Text Available Quantifying the proportion of polymorphic mutations that are deleterious or neutral is of fundamental importance to our understanding of evolution, disease genetics and the maintenance of variation genome-wide. Here, we develop an approximation to the distribution of fitness effects (DFE of segregating single-nucleotide mutations in humans. Unlike previous methods, we do not assume that synonymous mutations are neutral or not strongly selected, and we do not rely on fitting the DFE of all new nonsynonymous mutations to a single probability distribution, which is poorly motivated on a biological level. We rely on a previously developed method that utilizes a variety of published annotations (including conservation scores, protein deleteriousness estimates and regulatory data to score all mutations in the human genome based on how likely they are to be affected by negative selection, controlling for mutation rate. We map this and other conservation scores to a scale of fitness coefficients via maximum likelihood using diffusion theory and a Poisson random field model on SNP data. Our method serves to approximate the deleterious DFE of mutations that are segregating, regardless of their genomic consequence. We can then compare the proportion of mutations that are negatively selected or neutral across various categories, including different types of regulatory sites. We observe that the distribution of intergenic polymorphisms is highly peaked at neutrality, while the distribution of nonsynonymous polymorphisms has a second peak at [Formula: see text]. Other types of polymorphisms have shapes that fall roughly in between these two. We find that transcriptional start sites, strong CTCF-enriched elements and enhancers are the regulatory categories with the largest proportion of deleterious polymorphisms.

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....

Consistent individual differences in human social learning strategies.

Science.gov (United States)

Molleman, Lucas; van den Berg, Pieter; Weissing, Franz J

2014-04-04

Social learning has allowed humans to build up extensive cultural repertoires, enabling them to adapt to a wide variety of environmental and social conditions. However, it is unclear which social learning strategies people use, especially in social contexts where their payoffs depend on the behaviour of others. Here we show experimentally that individuals differ in their social learning strategies and that they tend to employ the same learning strategy irrespective of the interaction context. Payoff-based learners focus on their peers' success, while decision-based learners disregard payoffs and exclusively focus on their peers' past behaviour. These individual differences may be of considerable importance for cultural evolution. By means of a simple model, we demonstrate that groups harbouring individuals with different learning strategies may be faster in adopting technological innovations and can be more efficient through successful role differentiation. Our study highlights the importance of individual variation for human interactions and sheds new light on the dynamics of cultural evolution.

Human Factors in Accidents Involving Remotely Piloted Aircraft

Science.gov (United States)

Merlin, Peter William

2013-01-01

This presentation examines human factors that contribute to RPA mishaps and provides analysis of lessons learned. RPA accident data from U.S. military and government agencies were reviewed and analyzed to identify human factors issues. Common contributors to RPA mishaps fell into several major categories: cognitive factors (pilot workload), physiological factors (fatigue and stress), environmental factors (situational awareness), staffing factors (training and crew coordination), and design factors (human machine interface).

Learning objects as coadjuvants in the human physiology teaching-learning process

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Marcus Vinícius Lara

2014-08-01

Full Text Available The use of Information and Communication Technologies (ICTs in the academic environment of biomedical area has gained much importance, both for their ability to complement the understanding of the subject obtained in the classroom, is the ease of access, or makes more pleasure the learning process, since these tools are present in everyday of the students and use a simple language. Considering that, this study aims to report the experience of building learning objects in human physiology as a tool for learning facilitation, and discuss the impact of this teaching methodology

Individual differences in the learning potential of human beings

Science.gov (United States)

Stern, Elsbeth

2017-01-01

To the best of our knowledge, the genetic foundations that guide human brain development have not changed fundamentally during the past 50,000 years. However, because of their cognitive potential, humans have changed the world tremendously in the past centuries. They have invented technical devices, institutions that regulate cooperation and competition, and symbol systems, such as script and mathematics, that serve as reasoning tools. The exceptional learning ability of humans allows newborns to adapt to the world they are born into; however, there are tremendous individual differences in learning ability among humans that become obvious in school at the latest. Cognitive psychology has developed models of memory and information processing that attempt to explain how humans learn (general perspective), while the variation among individuals (differential perspective) has been the focus of psychometric intelligence research. Although both lines of research have been proceeding independently, they increasingly converge, as both investigate the concepts of working memory and knowledge construction. This review begins with presenting state-of-the-art research on human information processing and its potential in academic learning. Then, a brief overview of the history of psychometric intelligence research is combined with presenting recent work on the role of intelligence in modern societies and on the nature-nurture debate. Finally, promising approaches to integrating the general and differential perspective will be discussed in the conclusion of this review.

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.

"Wide-Awake Learning": Integrative Learning and Humanities Education

Science.gov (United States)

Booth, Alan

2011-01-01

This article reviews the development of integrative learning and argues that it has an important role to play in broader conceptions of the undergraduate curriculum recently advanced in the UK. It suggests that such a focus might also provide arts and humanities educators with a hopeful prospect in difficult times: a means by which the distinctive…

Out of sight, out of mind: Categorization learning and normal aging.

Science.gov (United States)

Schenk, Sabrina; Minda, John P; Lech, Robert K; Suchan, Boris

2016-10-01

The present combined EEG and eye tracking study examined the process of categorization learning at different age ranges and aimed to investigate to which degree categorization learning is mediated by visual attention and perceptual strategies. Seventeen young subjects and ten elderly subjects had to perform a visual categorization task with two abstract categories. Each category consisted of prototypical stimuli and an exception. The categorization of prototypical stimuli was learned very early during the experiment, while the learning of exceptions was delayed. The categorization of exceptions was accompanied by higher P150, P250 and P300 amplitudes. In contrast to younger subjects, elderly subjects had problems in the categorization of exceptions, but showed an intact categorization performance for prototypical stimuli. Moreover, elderly subjects showed higher fixation rates for important stimulus features and higher P150 amplitudes, which were positively correlated with the categorization performances. These results indicate that elderly subjects compensate for cognitive decline through enhanced perceptual and attentional processing of individual stimulus features. Additionally, a computational approach has been applied and showed a transition away from purely abstraction-based learning to an exemplar-based learning in the middle block for both groups. However, the calculated models provide a better fit for younger subjects than for elderly subjects. The current study demonstrates that human categorization learning is based on early abstraction-based processing followed by an exemplar-memorization stage. This strategy combination facilitates the learning of real world categories with a nuanced category structure. In addition, the present study suggests that categorization learning is affected by normal aging and modulated by perceptual processing and visual attention. Copyright © 2016 Elsevier Ltd. All rights reserved.

The lasting effects of process-specific versus stimulus-specific learning during infancy.

Science.gov (United States)

Hadley, Hillary; Pickron, Charisse B; Scott, Lisa S

2015-09-01

The capacity to tell the difference between two faces within an infrequently experienced face group (e.g. other species, other race) declines from 6 to 9 months of age unless infants learn to match these faces with individual-level names. Similarly, the use of individual-level labels can also facilitate differentiation of a group of non-face objects (strollers). This early learning leads to increased neural specialization for previously unfamiliar face or object groups. The current investigation aimed to determine whether early conceptual learning between 6 and 9 months leads to sustained behavioral advantages and neural changes in these same children at 4-6 years of age. Results suggest that relative to a control group of children with no previous training and to children with infant category-level naming experience, children with early individual-level training exhibited faster response times to human faces. Further, individual-level training with a face group - but not an object group - led to more adult-like neural responses for human faces. These results suggest that early individual-level learning results in long-lasting process-specific effects, which benefit categories that continue to be perceived and recognized at the individual level (e.g. human faces). © 2014 John Wiley & Sons Ltd.

Editorial: Technology for higher education, adult learning and human performance

OpenAIRE

Minhong Wang; Chi-Cheng Chang; Feng Wu

2013-01-01

This special issue is dedicated to technology-enabled approaches for improving higher education, adult learning, and human performance. Improvement of learning and human development for sustainable development has been recognized as a key strategy for individuals, institutions, and organizations to strengthen their competitive advantages. It becomes crucial to help adult learners and knowledge workers to improve their self-directed and life-long learning capabilities. Meanwhile, advances in t...

Human-like brain hemispheric dominance in birdsong learning.

Science.gov (United States)

Moorman, Sanne; Gobes, Sharon M H; Kuijpers, Maaike; Kerkhofs, Amber; Zandbergen, Matthijs A; Bolhuis, Johan J

2012-07-31

Unlike nonhuman primates, songbirds learn to vocalize very much like human infants acquire spoken language. In humans, Broca's area in the frontal lobe and Wernicke's area in the temporal lobe are crucially involved in speech production and perception, respectively. Songbirds have analogous brain regions that show a similar neural dissociation between vocal production and auditory perception and memory. In both humans and songbirds, there is evidence for lateralization of neural responsiveness in these brain regions. Human infants already show left-sided dominance in their brain activation when exposed to speech. Moreover, a memory-specific left-sided dominance in Wernicke's area for speech perception has been demonstrated in 2.5-mo-old babies. It is possible that auditory-vocal learning is associated with hemispheric dominance and that this association arose in songbirds and humans through convergent evolution. Therefore, we investigated whether there is similar song memory-related lateralization in the songbird brain. We exposed male zebra finches to tutor or unfamiliar song. We found left-sided dominance of neuronal activation in a Broca-like brain region (HVC, a letter-based name) of juvenile and adult zebra finch males, independent of the song stimulus presented. In addition, juvenile males showed left-sided dominance for tutor song but not for unfamiliar song in a Wernicke-like brain region (the caudomedial nidopallium). Thus, left-sided dominance in the caudomedial nidopallium was specific for the song-learning phase and was memory-related. These findings demonstrate a remarkable neural parallel between birdsong and human spoken language, and they have important consequences for our understanding of the evolution of auditory-vocal learning and its neural mechanisms.

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.

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).

Challenges of nursing teaching-learning to care for human dying - professors' perceptions

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Emanuelle Caires Dias Araújo Nunes

2017-10-01

Full Text Available Abstract The objective of this study was to investigate professors' perceptions about their experiences in the teaching-learning process of nursing care in relation to dying. This is a descriptive-exploratory, qualitative research, delimited by data saturation, was carried out with 11 nursing professors from three higher education institutions. Data collection involved the drawing-text theme technique and a semi-structured interview. Analysis used the technique of collective subject discourse. The results identified three categories: How I would like to take care in the context of finitude - my challenge; Challenging fragilities in the teaching of nurses in the context of care concerned with death and dying; Strategies to compensate or promote more substantial nurse training related to care in finitude. We conclude that the graduation of the nurses studied did not satisfactorily develop the necessary skills and abilities to deal with human death and dying. This study infers the need of permanent education to support transformations in this area.

Teaching and Learning French--A Tale of Desire in the Humanities

Science.gov (United States)

Cunningham, Catriona

2017-01-01

This article considers the way we talk about learning and teaching the humanities in higher education in the UK. By using the tools of the arts and humanities within the scholarship of learning and teaching, and examining a personal perspective, the author explores the transformational impact of French language learning and teaching. Close textual…

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.

Convergent transcriptional specializations in the brains of humans and song-learning birds

DEFF Research Database (Denmark)

Pfenning, Andreas R.; Hara, Erina; Whitney, Osceola

2014-01-01

Song-learning birds and humans share independently evolved similarities in brain pathways for vocal learning that are essential for song and speech and are not found in most other species. Comparisons of brain transcriptomes of song-learning birds and humans relative to vocal nonlearners identified...... convergent gene expression specializations in specific song and speech brain regions of avian vocal learners and humans. The strongest shared profiles relate bird motor and striatal song-learning nuclei, respectively, with human laryngeal motor cortex and parts of the striatum that control speech production...... and learning. Most of the associated genes function in motor control and brain connectivity. Thus, convergent behavior and neural connectivity for a complex trait are associated with convergent specialized expression of multiple genes....

THE CATEGORY „HOME” IN THE ANTROPOLOGICAL SPACE OF CULTURE

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COMENDANT TATIANA

2015-09-01

Full Text Available The present work considers the category „Home” in the anthropological space of culture. The authors analyze the typology of human nature within the cultural-historical space. The article also underlines the semantic recurrence of the archetypes of the category „Home” in the archaic forms of social conscience such as: mythology, folklore etc. Special attention is given to the treatment of this category in holy religious texts. Emphasis is laid on the characteristic features of the process of modifying the category „Home” in contemporary reality.

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 ...

Do domestic dogs learn words based on humans' referential behaviour?

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Sebastian Tempelmann

Full Text Available Some domestic dogs learn to comprehend human words, although the nature and basis of this learning is unknown. In the studies presented here we investigated whether dogs learn words through an understanding of referential actions by humans rather than simple association. In three studies, each modelled on a study conducted with human infants, we confronted four word-experienced dogs with situations involving no spatial-temporal contiguity between the word and the referent; the only available cues were referential actions displaced in time from exposure to their referents. We found that no dogs were able to reliably link an object with a label based on social-pragmatic cues alone in all the tests. However, one dog did show skills in some tests, possibly indicating an ability to learn based on social-pragmatic cues.

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…

Libertarianism & Category-Mistake

Directory of Open Access Journals (Sweden)

Carlos G. Patarroyo G.

2009-12-01

Full Text Available This paper offers a defense against two accusations according to which libertarianism incurs in a category-mistake. The philosophy of Gilbert Ryle will be used to explain the reasons which ground these accusations. Further, it will be shown why, although certain sorts of libertarianism based on agent-causation or Cartesian dualism incur in these mistakes, there is at least one version of libertarianism to which this criticism does not necessarily apply: the version that seeks to find in physical indeterminism the grounding of human free will.

Sleep spindle-related reactivation of category-specific cortical regions after learning face-scene associations

DEFF Research Database (Denmark)

Bergmann, Til O; Mölle, Matthias; Diedrichs, Jens

2012-01-01

Newly acquired declarative memory traces are believed to be reactivated during NonREM sleep to promote their hippocampo-neocortical transfer for long-term storage. Yet it remains a major challenge to unravel the underlying neuronal mechanisms. Using simultaneous electroencephalography (EEG......-coupled reactivation of brain regions representing the specific task stimuli was traced during subsequent NonREM sleep with EEG-informed fMRI. Relative to the control task, learning face-scene associations triggered a stronger combined activation of neocortical and hippocampal regions during subsequent sleep. Notably......) and functional magnetic resonance imaging (fMRI) recordings in humans, we show that sleep spindles play a key role in the reactivation of memory-related neocortical representations. On separate days, participants either learned face-scene associations or performed a visuomotor control task. Spindle...

Learning and motivation in the human striatum.

Science.gov (United States)

Shohamy, Daphna

2011-06-01

The past decade has seen a dramatic change in our understanding of the role of the striatum in behavior. Early perspectives emphasized a role for the striatum in habitual learning of stimulus-response associations and sequences of actions. Recent advances from human neuroimaging research suggest a broader role for the striatum in motivated learning. New findings demonstrate that the striatum represents multiple learning signals and highlight the contribution of the striatum across many cognitive domains and contexts. Recent findings also emphasize interactions between the striatum and other specialized brain systems for learning. Together, these findings suggest that the striatum contributes to a distributed network that learns to select actions based on their predicted value in order to optimize behavior. Copyright © 2011 Elsevier Ltd. All rights reserved.

Scaffolding in geometry based on self regulated learning

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Bayuningsih, A. S.; Usodo, B.; Subanti, S.

2017-12-01

This research aim to know the influence of problem based learning model by scaffolding technique on junior high school student’s learning achievement. This research took location on the junior high school in Banyumas. The research data obtained through mathematic learning achievement test and self-regulated learning (SRL) questioner. Then, the data analysis used two ways ANOVA. The results showed that scaffolding has positive effect to the mathematic learning achievement. The mathematic learning achievement use PBL-Scaffolding model is better than use PBL. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

Verb Learning in 14- and 18-Month-Old English-Learning Infants

Science.gov (United States)

He, Angela Xiaoxue; Lidz, Jeffrey

2017-01-01

The present study investigates English-learning infants' early understanding of the link between the grammatical category "verb" and the conceptual category "event," and their ability to recruit morphosyntactic information online to learn novel verb meanings. We report two experiments using an infant-controlled…

THE USE OF CATEGORIES AS INDICATORS OF ORGANIZATIONAL CLIMATE IN BRAZILIAN COMPANIES

Directory of Open Access Journals (Sweden)

Joel Souza Dutra

2012-04-01

Full Text Available In order to analyze employees’ perception of the work environment, companies with a well-established people management structure periodically conduct organizational climate surveys. These surveys are meant to offer an understanding of how employees view the quality of the relationships they experience in the company. One of the characteristics of this type of survey, identified both in the relevant literature and empirically in practice, is the use of categories or indicators to direct development of the research instrument, data analysis, and later intervention as needed according to the results of the survey. This article seeks to propose a categorization of organizational climate dimensions directed at the Brazilian corporate reality, analysing its internal consistency and its construct validity. To that end, we used the results of a wide-ranging data sample collected from 123,445 respondents of 491 organizations in various regions of Brazil. The proposed analysis categories – identity, satisfaction and motivation, learning and development, and leadership – were reviewed based on theories of organizational behavior and then submitted to a focus group composed of human resources professionals employed by prominent Brazilian corporations.

Visual memory needs categories

OpenAIRE

Olsson, Henrik; Poom, Leo

2005-01-01

Capacity limitations in the way humans store and process information in working memory have been extensively studied, and several memory systems have been distinguished. In line with previous capacity estimates for verbal memory and memory for spatial information, recent studies suggest that it is possible to retain up to four objects in visual working memory. The objects used have typically been categorically different colors and shapes. Because knowledge about categories is stored in long-t...

Human Subjects Protection: A Source for Ethical Service-Learning Practice

Science.gov (United States)

Wendler, Rachael

2012-01-01

Human subjects research ethics were developed to ensure responsible conduct when university researchers learn by interacting with community members. As service-learning students also learn by interacting with community members, a similar set of principles may strengthen the ethical practice of service-learning. This article identifies ethical…

Efectos del desarrollo en la memoria de trabajo y el aprendizaje de categorías en niños Developmet effects on working memory and category learning in children

Directory of Open Access Journals (Sweden)

Federico José Sánchez

2009-12-01

Full Text Available Se han reportado diferencias relacionadas con la edad en el desempeño de diversas tareas dependientes del lóbulo frontal, incluyendo tareas de memoria de trabajo (Luciana and Nelson, 1998; Luna et al. 2001; Bunge et al., 2002. Se han encontrado también efectos de edad y género sobre la memoria de trabajo en niños (Vuontela et. al, 2003. Además, el aprendizaje de categorías ha sido asociado con la actividad del lóbulo frontal (Dickins, 2000; Schlund 2007. El presente trabajo investigó los efectos de la edad y el género sobre la memoria de trabajo y el aprendizaje de categorías en niños de 8 a 13 años. Se encontraron efectos de edad y género sobre la memoria de trabajo, y en la tarea de aprendizaje de categorías sólo se observaron efectos de la edad. Los resultados sugieren que el desempeño de memoria de trabajo podría estar asociado con la velocidad de procesamiento en el aprendizaje de categorías.Age-related differences have been reported in the performance of several frontal lobe-dependent tasks, including working memory (Luciana and Nelson, 1998; Luna et al. 2001; Bunge et al., 2002. Effects of age and gender on working memory have been found in children (Vuontela et. al, 2003. On the other hand, category learning has also been associated with frontal lobe activity (Dickins, 2000; Schlund 2007. The present study addressed the effects of age and gender on working memory and category learning, in 8-13 year old children. Age and gender effects were found on the working memory task, and age effects only were observed on category learning. The results suggest that working memory performance might be associated with processing speed in the category learning task.

Cultural dimensions of learning

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Eyford, Glen A.

1990-06-01

How, what, when and where we learn is frequently discussed, as are content versus process, or right brain versus left brain learning. What is usually missing is the cultural dimension. This is not an easy concept to define, but various aspects can be identified. The World Decade for Cultural Development emphasizes the need for a counterbalance to a quantitative, economic approach. In the last century poets also warned against brutalizing materialism, and Sorokin and others have described culture more recently in terms of cohesive basic values expressed through aesthetics and institutions. Bloom's taxonomy incorporates the category of affective learning, which internalizes values. If cultural learning goes beyond knowledge acquisition, perhaps the surest way of understanding the cultural dimension of learning is to examine the aesthetic experience. This can use myths, metaphors and symbols, and to teach and learn by using these can help to unlock the human potential for vision and creativity.

Health effects of risk-assessment categories

International Nuclear Information System (INIS)

Kramer, C.F.; Rybicka, K.; Knutson, A.; Morris, S.C.

1983-10-01

Environmental and occupational health effects associated with exposures to various chemicals are a subject of increasing concern. One recently developed methodology for assessing the health impacts of various chemical compounds involves the classification of similar chemicals into risk-assessment categories (RACs). This report reviews documented human health effects for a broad range of pollutants, classified by RACs. It complements other studies that have estimated human health effects by RAC based on analysis and extrapolation of data from animal research

Health effects of risk-assessment categories

Energy Technology Data Exchange (ETDEWEB)

Kramer, C.F.; Rybicka, K.; Knutson, A.; Morris, S.C.

1983-10-01

Environmental and occupational health effects associated with exposures to various chemicals are a subject of increasing concern. One recently developed methodology for assessing the health impacts of various chemical compounds involves the classification of similar chemicals into risk-assessment categories (RACs). This report reviews documented human health effects for a broad range of pollutants, classified by RACs. It complements other studies that have estimated human health effects by RAC based on analysis and extrapolation of data from animal research.

Intrinsically motivated reinforcement learning for human-robot interaction in the real-world.

Science.gov (United States)

Qureshi, Ahmed Hussain; Nakamura, Yutaka; Yoshikawa, Yuichiro; Ishiguro, Hiroshi

2018-03-26

For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement. Copyright © 2018 Elsevier Ltd. All rights reserved.

EVOLUTION OF THEORETICAL APPROACHES TO THE DEFINITION OF THE CATEGORY “PERSONNEL POTENTIAL”

Directory of Open Access Journals (Sweden)

Аlexandra Deshchenko

2016-02-01

Full Text Available The article describes the evolution of theoretical approaches to definition of the category «personnel potential» based on the analysis of approaches to definition of the conceptual apparatus of labor Economics, including such categories as: labor force, labor resources, labor potential, human resources, human capital, human capital different authors. The analysis of the evolution of the terms in accordance with the stages of development of a society.

Human-level control through deep reinforcement learning

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning.

Science.gov (United States)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning.

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Loonis, Roman F; Brincat, Scott L; Antzoulatos, Evan G; Miller, Earl K

2017-10-11

A meta-analysis of non-human primates performing three different tasks (Object-Match, Category-Match, and Category-Saccade associations) revealed signatures of explicit and implicit learning. Performance improved equally following correct and error trials in the Match (explicit) tasks, but it improved more after correct trials in the Saccade (implicit) task, a signature of explicit versus implicit learning. Likewise, error-related negativity, a marker for error processing, was greater in the Match (explicit) tasks. All tasks showed an increase in alpha/beta (10-30 Hz) synchrony after correct choices. However, only the implicit task showed an increase in theta (3-7 Hz) synchrony after correct choices that decreased with learning. In contrast, in the explicit tasks, alpha/beta synchrony increased with learning and decreased thereafter. Our results suggest that explicit versus implicit learning engages different neural mechanisms that rely on different patterns of oscillatory synchrony. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning collaborative teamwork: an argument for incorporating the humanities.

Science.gov (United States)

Hall, Pippa; Brajtman, Susan; Weaver, Lynda; Grassau, Pamela Anne; Varpio, Lara

2014-11-01

A holistic, collaborative interprofessional team approach, which includes patients and families as significant decision-making members, has been proposed to address the increasing burden being placed on the health-care system. This project hypothesized that learning activities related to the humanities during clinical placements could enhance interprofessional teamwork. Through an interprofessional team of faculty, clinical staff, students, and patient representatives, we developed and piloted the self-learning module, "interprofessional education for collaborative person-centred practice through the humanities". The module was designed to provide learners from different professions and educational levels with a clinical placement/residency experience that would enable them, through a lens of the humanities, to better understand interprofessional collaborative person-centred care without structured interprofessional placement activities. Learners reported the self-paced and self-directed module to be a satisfactory learning experience in all four areas of care at our institution, and certain attitudes and knowledge were significantly and positively affected. The module's evaluation resulted in a revised edition providing improved structure and instruction for students with no experience in self-directed learning. The module was recently adapted into an interactive bilingual (French and English) online e-learning module to facilitate its integration into the pre-licensure curriculum at colleges and universities.

Effectiveness of using blended learning strategies for teaching and learning human anatomy.

Science.gov (United States)

Pereira, José A; Pleguezuelos, Eulogio; Merí, Alex; Molina-Ros, Antoni; Molina-Tomás, M Carmen; Masdeu, Carlos

2007-02-01

This study aimed to implement innovative teaching methods--blended learning strategies--that include the use of new information technologies in the teaching of human anatomy and to analyse both the impact of these strategies on academic performance, and the degree of user satisfaction. The study was carried out among students in Year 1 of the biology degree curriculum (human biology profile) at Pompeu Fabra University, Barcelona. Two groups of students were tested on knowledge of the anatomy of the locomotor system and results compared between groups. Blended learning strategies were employed in 1 group (BL group, n = 69); the other (TT group; n = 65) received traditional teaching aided by complementary material that could be accessed on the Internet. Both groups were evaluated using the same types of examination. The average marks presented statistically significant differences (BL 6.3 versus TT 5.0; P < 0.0001). The percentage pass rate for the subject in the first call was higher in the BL group (87.9% versus 71.4%; P = 0.02), reflecting a lower incidence of students who failed to sit the examination (BL 4.3% versus TT 13.8%; P = 0.05). There were no differences regarding overall satisfaction with the teaching received. Blended learning was more effective than traditional teaching for teaching human anatomy.

Human likeness: cognitive and affective factors affecting adoption of robot-assisted learning systems

Science.gov (United States)

Yoo, Hosun; Kwon, Ohbyung; Lee, Namyeon

2016-07-01

With advances in robot technology, interest in robotic e-learning systems has increased. In some laboratories, experiments are being conducted with humanoid robots as artificial tutors because of their likeness to humans, the rich possibilities of using this type of media, and the multimodal interaction capabilities of these robots. The robot-assisted learning system, a special type of e-learning system, aims to increase the learner's concentration, pleasure, and learning performance dramatically. However, very few empirical studies have examined the effect on learning performance of incorporating humanoid robot technology into e-learning systems or people's willingness to accept or adopt robot-assisted learning systems. In particular, human likeness, the essential characteristic of humanoid robots as compared with conventional e-learning systems, has not been discussed in a theoretical context. Hence, the purpose of this study is to propose a theoretical model to explain the process of adoption of robot-assisted learning systems. In the proposed model, human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and para-social relationships; these factors are considered as possible determinants of the degree to which human cognition and affection are related to the adoption of robot-assisted learning systems.

Contested Categories

DEFF Research Database (Denmark)

Drawing on social science perspectives, Contested Categories presents a series of empirical studies that engage with the often shifting and day-to-day realities of life sciences categories. In doing so, it shows how such categories remain contested and dynamic, and that the boundaries they create...

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

Science.gov (United States)

Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

2014-03-01

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

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

Grammatical category dissociation in multilingual aphasia.

Science.gov (United States)

Faroqi-Shah, Yasmeen; Waked, Arifi N

2010-03-01

Word retrieval deficits for specific grammatical categories, such as verbs versus nouns, occur as a consequence of brain damage. Such deficits are informative about the nature of lexical organization in the human brain. This study examined retrieval of grammatical categories across three languages in a trilingual person with aphasia who spoke Arabic, French, and English. In order to delineate the nature of word production difficulty, comprehension was tested, and a variety of concomitant lexical-semantic variables were analysed. The patient demonstrated a consistent noun-verb dissociation in picture naming and narrative speech, with severely impaired production of verbs across all three languages. The cross-linguistically similar noun-verb dissociation, coupled with little evidence of semantic impairment, suggests that (a) the patient has a true "nonsemantic" grammatical category specific deficit, and (b) lexical organization in multilingual speakers shares grammatical class information between languages. The findings of this study contribute to our understanding of the architecture of lexical organization in bilinguals.

Using a model of human visual perception to improve deep learning.

Science.gov (United States)

Stettler, Michael; Francis, Gregory

2018-04-17

Deep learning algorithms achieve human-level (or better) performance on many tasks, but there still remain situations where humans learn better or faster. With regard to classification of images, we argue that some of those situations are because the human visual system represents information in a format that promotes good training and classification. To demonstrate this idea, we show how occluding objects can impair performance of a deep learning system that is trained to classify digits in the MNIST database. We describe a human inspired segmentation and interpolation algorithm that attempts to reconstruct occluded parts of an image, and we show that using this reconstruction algorithm to pre-process occluded images promotes training and classification performance. Copyright © 2018 Elsevier Ltd. All rights reserved.

To call a cloud 'cirrus': sound symbolism in names for categories or items.

Science.gov (United States)

Ković, Vanja; Sučević, Jelena; Styles, Suzy J

2017-01-01

The aim of the present paper is to experimentally test whether sound symbolism has selective effects on labels with different ranges-of-reference within a simple noun-hierarchy. In two experiments, adult participants learned the make up of two categories of unfamiliar objects ('alien life forms'), and were passively exposed to either category-labels or item-labels, in a learning-by-guessing categorization task. Following category training, participants were tested on their visual discrimination of object pairs. For different groups of participants, the labels were either congruent or incongruent with the objects. In Experiment 1, when trained on items with individual labels, participants were worse (made more errors) at detecting visual object mismatches when trained labels were incongruent. In Experiment 2, when participants were trained on items in labelled categories, participants were faster at detecting a match if the trained labels were congruent, and faster at detecting a mismatch if the trained labels were incongruent. This pattern of results suggests that sound symbolism in category labels facilitates later similarity judgments when congruent, and discrimination when incongruent, whereas for item labels incongruence generates error in judgements of visual object differences. These findings reveal that sound symbolic congruence has a different outcome at different levels of labelling within a noun hierarchy. These effects emerged in the absence of the label itself, indicating subtle but pervasive effects on visual object processing.

Picture-Word Differences in Discrimination Learning: 11. Effects of Conceptual Categories

Science.gov (United States)

Bourne, Lyle E.; And Others

1976-01-01

Investigates the prediction that the usual superiority of pictures over words for repetitions of the same items would disappear for items that were different instances of repeated categories. (Author/RK)

Development of Human Resources Using New Technologies in Long-Life Learning

Directory of Open Access Journals (Sweden)

Micu Bogdan Ghilic

2011-01-01

Full Text Available Information and communication technologies (ICT offer new opportunities to reinvent the education and to make people and makes learning more fun and contemporary but poses many problems to educational institutions. Implementation of ICT determines major structural changes in the organizations and mental switch from bureaucratic mentality to customer-oriented one. In this paper I try to evaluate methods of developing the lifelong learning programs, impact to human resources training and development and the impact of this process on educational institutions. E-learning usage in training the human resources can make a new step in development of the education institutions, human resources and companies.

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…

Human reinforcement learning subdivides structured action spaces by learning effector-specific values

OpenAIRE

Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

2009-01-01

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality...

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

Specific neural traces for intonational discourse categories as revealed by human-evoked potentials.

Science.gov (United States)

Borràs-Comes, Joan; Costa-Faidella, Jordi; Prieto, Pilar; Escera, Carles

2012-04-01

The neural representation of segmental and tonal phonological distinctions has been shown by means of the MMN ERP, yet this is not the case for intonational discourse contrasts. In Catalan, a rising-falling intonational sequence can be perceived as a statement or as a counterexpectational question, depending exclusively on the size of the pitch range interval of the rising movement. We tested here, using the MMN, whether such categorical distinctions elicited distinct neurophysiological patterns of activity, supporting their specific neural representation. From a behavioral identification experiment, we set the boundary between the two categories and defined four stimuli across the continuum. Although the physical distance between each pair of stimuli was kept constant, the central pair represented an across-category contrast, whereas the other pairs represented within-category contrasts. These four auditory stimuli were contrasted by pairs in three different oddball blocks. The mean amplitude of the MMN was larger for the across-category contrast, suggesting that intonational contrasts in the target language can be encoded automatically in the auditory cortex. These results are in line with recent findings in other fields of linguistics, showing that, when a boundary between categories is crossed, the MMN response is not just larger but rather includes a separate subcomponent.

Learning to Segment Human by Watching YouTube.

Science.gov (United States)

Liang, Xiaodan; Wei, Yunchao; Chen, Yunpeng; Shen, Xiaohui; Yang, Jianchao; Lin, Liang; Yan, Shuicheng

2016-08-05

An intuition on human segmentation is that when a human is moving in a video, the video-context (e.g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body. Inspired by this, based on popular deep convolutional neural networks (CNN), we explore a very-weakly supervised learning framework for human segmentation task, where only an imperfect human detector is available along with massive weakly-labeled YouTube videos. In our solution, the video-context guided human mask inference and CNN based segmentation network learning iterate to mutually enhance each other until no further improvement gains. In the first step, each video is decomposed into supervoxels by the unsupervised video segmentation. The superpixels within the supervoxels are then classified as human or non-human by graph optimization with unary energies from the imperfect human detection results and the predicted confidence maps by the CNN trained in the previous iteration. In the second step, the video-context derived human masks are used as direct labels to train CNN. Extensive experiments on the challenging PASCAL VOC 2012 semantic segmentation benchmark demonstrate that the proposed framework has already achieved superior results than all previous weakly-supervised methods with object class or bounding box annotations. In addition, by augmenting with the annotated masks from PASCAL VOC 2012, our method reaches a new stateof- the-art performance on the human segmentation task.

"Folksonomies" on the Net: constructing alternative, creative and intercultural categories.

Directory of Open Access Journals (Sweden)

Corrado Petrucco

2006-01-01

Full Text Available Analysis of the creation of categories and classifications, with particular reference to the approach based on each person's unique perspective and sharing in a community 'practices on the Web This approach can' generate interesting side effects that stimulate creativity 'and the learning in an intercultural perspective.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

OpenAIRE

Kelling, Steve; Gerbracht, Jeff; Fink, Daniel; Lagoze, Carl; Wong, Weng-Keen; Yu, Jun; Damoulas, Theodoros; Gomes, Carla

2012-01-01

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network,...

Prediction of human protein function according to Gene Ontology categories

DEFF Research Database (Denmark)

Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

2003-01-01

developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

Analysis of e-learning implementation readiness based on integrated elr model

Science.gov (United States)

Adiyarta, K.; Napitupulu, D.; Rahim, R.; Abdullah, D.; Setiawan, MI

2018-04-01

E-learning nowadays has become a requirement for institutions to support their learning activities. To adopt e-learning, an institution requires a large strategy and resources for optimal application. Unfortunately, not all institutions that have used e-learning got the desired results or expectations. This study aims to identify the extent of the level of readiness of e-learning implementation in institution X. The degree of institutional readiness will determine the success of future e-learning utilization. In addition, institutional readiness measurement are needed to evaluate the effectiveness of strategies in e-learning development. The research method used is survey with questionnaire designed based on integration of 8 best practice ELR (e-learning readiness) model. The results showed that from 13 factors of integrated ELR model being measured, there are 3 readiness factors included in the category of not ready and needs a lot of work. They are human resource (2.57), technology skill (2.38) and content factors (2.41). In general, e-learning implementation in institutions is in the category of not ready but needs some of work (3.27). Therefore, the institution should consider which factors or areas of ELR factors are considered still not ready and needs improvement in the future.

A Human Capabilities Framework for Evaluating Student Learning

Science.gov (United States)

Walker, Melanie

2008-01-01

This paper proposes a human capabilities approach for evaluating student learning and the social and pedagogical arrangements that support equality in capabilities for all students. It outlines the focus on valuable beings and doings in the capability approach developed by Amartya Sen, and Martha Nussbaum's capabilities focus on human flourishing.…

Human dorsal striatum encodes prediction errors during observational learning of instrumental actions.

Science.gov (United States)

Cooper, Jeffrey C; Dunne, Simon; Furey, Teresa; O'Doherty, John P

2012-01-01

The dorsal striatum plays a key role in the learning and expression of instrumental reward associations that are acquired through direct experience. However, not all learning about instrumental actions require direct experience. Instead, humans and other animals are also capable of acquiring instrumental actions by observing the experiences of others. In this study, we investigated the extent to which human dorsal striatum is involved in observational as well as experiential instrumental reward learning. Human participants were scanned with fMRI while they observed a confederate over a live video performing an instrumental conditioning task to obtain liquid juice rewards. Participants also performed a similar instrumental task for their own rewards. Using a computational model-based analysis, we found reward prediction errors in the dorsal striatum not only during the experiential learning condition but also during observational learning. These results suggest a key role for the dorsal striatum in learning instrumental associations, even when those associations are acquired purely by observing others.

COGNITIVE-COMMUNICATIVE PERSONALITY CATEGORY IN THE KAZAKH LANGUAGE

Directory of Open Access Journals (Sweden)

Orynay Sagingalievna Zhubaeva

2018-02-01

Full Text Available The purpose of the research is to reveal the character of anthropocentricity of grammatical categories in their meaning and functioning. Materials and methods. According to the research objectives and goals, the methods used were as follows: the descriptive method, general scientific methods of analysis and synthesis, cognitive analysis, method of experiment, contextual analysis, structural-semantic analysis, transformation technique, comparative analysis. Results. For the first time in the Kazakh linguistics the substantial aspect of grammatical categories is characterized being a result of both conceptualization and categorization processes. Based on generalization and the comparative analysis of nature and forms of the human factor reflection in the Kazakh grammatical categories there has been revealed the national-cultural specific character of grammatical categories. Practical implications. The research materials can be used in theoretical courses onf grammar and linguistics, as well as in the development of special courses on cognitive linguistics, cognitive grammar, etc.

Debriefing after Human Patient Simulation and Nursing Students' Learning

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Benhuri, Gloria

2014-01-01

Human Patient Simulation (HPS) exercises with life-like computerized manikins provide clinical experiences for nursing students in a safe environment followed by debriefing that promotes learning. Quantitative research in techniques to support learning from debriefing is limited. The purpose of the quantitative quasi-experimental study using a…

Utilizing Service Learning in a College-Level Human Sexuality Course

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Jenkins, Dusty D.

2017-01-01

Implementing service learning into college courses has been shown to have positive benefits for both students and community members; however, service learning has not been largely evaluated in the literature on human sexuality courses. Thus, the purpose of the current study was to design, implement, and evaluate a service learning project in a…

Theorising Learning and Nature: Post-Human Possibilities and Problems

Science.gov (United States)

Quinn, Jocey

2013-01-01

In their predominantly theoretical turn to the material, post-humanist feminists often focus on "nature", arguing that the nature/culture binary has collapsed and that fixed distinctions between human and non-human spheres no longer hold. Conversely, outdoor learning sees nature as a space where humans act and has been more concerned…

Infants can use distributional cues to form syntactic categories.

Science.gov (United States)

Gerken, LouAnn; Wilson, Rachel; Lewis, William

2005-05-01

Nearly all theories of language development emphasize the importance of distributional cues for segregating words and phrases into syntactic categories like noun, feminine or verb phrase. However, questions concerning whether such cues can be used to the exclusion of referential cues have been debated. Using the headturn preference procedure, American children aged 1;5 were briefly familiarized with a partial Russian gender paradigm, with a subset of the paradigm members withheld. During test, infants listened on alternate trials to previously withheld grammatical items and ungrammatical items with incorrect gender markings on previously heard stems. Across three experiments, infants discriminated new grammatical from ungrammatical items, but like adults in previous studies, were only able to do so when a subset of familiarization items was double marked for gender category. The results suggest that learners can use distributional cues to category structure, to the exclusion of referential cues, from relatively early in the language learning process.

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.

[Which learning methods are expected for ultrasound training? Blended learning on trial].

Science.gov (United States)

Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

2014-10-01

Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

Social learning in humans and other animals.

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Jean-François eGariépy

2014-03-01

Full Text Available Decisions made by individuals can be influenced by what others think and do. Social learning includes a wide array of behaviors such as imitation, observational learning of novel foraging techniques, peer or parental influences on individual preferences, as well as outright teaching. These processes are believed to underlie an important part of cultural variation among human populations and may also explain intraspecific variation in behavior between geographically distinct populations of animals. Recent neurobiological studies have begun to uncover the neural basis of social learning. Here we review experimental evidence from the past few decades showing that social learning is a widespread set of skills present in multiple animal species. In mammals, the temporoparietal junction, the dorsomedial and dorsolateral prefrontal cortex, as well as the anterior cingulate gyrus, appear to play critical roles in social learning. Birds, fish and insects also learn from others, but the underlying neural mechanisms remain poorly understood. We discuss the evolutionary implications of these findings and highlight the importance of emerging animal models that permit precise modification of neural circuit function for elucidating the neural basis of social learning.

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

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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).

Human resource management and learning for innovation: pharmaceuticals in Mexico

OpenAIRE

Santiago-Rodriguez, Fernando

2010-01-01

This paper investigates the influence of human resource management on learning from internal and external sources of knowledge. Learning for innovation is a key ingredient of catching-up processes. The analysis builds on survey data about pharmaceutical firms in Mexico. Results show that the influence of human resource management is contingent on the knowledge flows and innovation goals pursued by the firm. Practices such as training-- particularly from external partners; and remuneration for...

Learning in human-dolphin interactions at zoological facilities

Science.gov (United States)

Sweeney, Diane L.

This research aimed to better understand learning in zoological settings, particularly learning about marine mammals, by investigating the research question, what do people learn through interacting with dolphins in zoological facilities? Sociocultural situated learning theory, specifically a Community of Practice (CoP) model of learning (Lave & Wenger, 1991), was the theoretical framework. The CoP model allowed for diversity of knowledge, interest, motivations, and goals that existed among the community of animal enthusiasts at three commercial zoological facilities, and also for peripheral to more central types of participation. I collected data through interviews of spectators, visitors, and trainers (n=51), observations (n=16), and an online questionnaire of past-visitors (n=933). Data were coded, categorized, and analyzed based on the National Science Foundation's (Friedman, 2008) and the National Research Council's (2009) frameworks for informal science education. Five principal findings answered the research question. First, all participants gained new knowledge within three broad categories: (a) dolphin physiology and natural history, (b) care and training of dolphins, and (c) conservation. Second, all participants constructed personal meanings by connecting the activity to experiences, beliefs, and practices outside the interaction context. Almost all participants made associations with conservation. Third, most participants shifted their attitudes and gained a sense of personal agency about beginning or increasing stewardship actions. Fourth, visitors learned interspecies etiquette skills; trainers learned skills in dolphin training and management, people management, and teaching. Fifth, visitors had long-lasting memories of the experience that occurred eight months to 18 years in the past. Popular cultural ideas about dolphins and the ways the dolphins were represented influenced visitors' expectations and the types of learning. Potential physical

Is better beautiful or is beautiful better? Exploring the relationship between beauty and category structure.

Science.gov (United States)

Sanders, Megan; Davis, Tyler; Love, Bradley C

2013-06-01

We evaluate two competing accounts of the relationship between beauty and category structure. According to the similarity-based view, beauty arises from category structure such that central items are favored due to their increased fluency. In contrast, the theory-based view holds that people's theories of beauty shape their perceptions of categories. In the present study, subjects learned to categorize abstract paintings into meaningfully labeled categories and rated the paintings' beauty, value, and typicality. Inconsistent with the similarity-based view, beauty ratings were highly correlated across conditions despite differences in fluency and assigned category structure. Consistent with the theory-based view, beautiful paintings were treated as central members for categories expected to contain beautiful paintings (e.g., art museum pieces), but not in others (e.g., student show pieces). These results suggest that the beauty of complex, real-world stimuli is not determined by fluency within category structure but, instead, interacts with people's prior knowledge to structure categories.

Comparing two K-category assignments by a K-category correlation coefficient

DEFF Research Database (Denmark)

Gorodkin, Jan

2004-01-01

Predicted assignments of biological sequences are often evaluated by Matthews correlation coefficient. However, Matthews correlation coefficient applies only to cases where the assignments belong to two categories, and cases with more than two categories are often artificially forced into two...... categories by considering what belongs and what does not belong to one of the categories, leading to the loss of information. Here, an extended correlation coefficient that applies to K-categories is proposed, and this measure is shown to be highly applicable for evaluating prediction of RNA secondary...

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

Science.gov (United States)

Ofli, Ferda; Meier, Patrick; Imran, Muhammad; Castillo, Carlos; Tuia, Devis; Rey, Nicolas; Briant, Julien; Millet, Pauline; Reinhard, Friedrich; Parkan, Matthew; Joost, Stéphane

2016-03-01

Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center (JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap ). The

Movement-related theta rhythm in humans: coordinating self-directed hippocampal learning.

Directory of Open Access Journals (Sweden)

Raphael Kaplan

Full Text Available The hippocampus is crucial for episodic or declarative memory and the theta rhythm has been implicated in mnemonic processing, but the functional contribution of theta to memory remains the subject of intense speculation. Recent evidence suggests that the hippocampus might function as a network hub for volitional learning. In contrast to human experiments, electrophysiological recordings in the hippocampus of behaving rodents are dominated by theta oscillations reflecting volitional movement, which has been linked to spatial exploration and encoding. This literature makes the surprising cross-species prediction that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. We examined the links between theta, spatial exploration, and memory encoding by designing an interactive human spatial navigation paradigm combined with multimodal neuroimaging. We used both non-invasive whole-head Magnetoencephalography (MEG to look at theta oscillations and Functional Magnetic Resonance Imaging (fMRI to look at brain regions associated with volitional movement and learning. We found that theta power increases during the self-initiation of virtual movement, additionally correlating with subsequent memory performance and environmental familiarity. Performance-related hippocampal theta increases were observed during a static pre-navigation retrieval phase, where planning for subsequent navigation occurred. Furthermore, periods of the task showing movement-related theta increases showed decreased fMRI activity in the parahippocampus and increased activity in the hippocampus and other brain regions that strikingly overlap with the previously observed volitional learning network (the reverse pattern was seen for stationary periods. These fMRI changes also correlated with participant's performance. Our findings suggest that the human hippocampal theta rhythm supports memory by coordinating

Sequential causal learning in humans and rats

NARCIS (Netherlands)

Lu, H.; Rojas, R.R.; Beckers, T.; Yuille, A.; Love, B.C.; McRae, K.; Sloutsky, V.M.

2008-01-01

Recent experiments (Beckers, De Houwer, Pineño, & Miller, 2005;Beckers, Miller, De Houwer, & Urushihara, 2006) have shown that pretraining with unrelated cues can dramatically influence the performance of humans in a causal learning paradigm and rats in a standard Pavlovian conditioning paradigm.

Casual Games and Casual Learning About Human Biological Systems

Science.gov (United States)

Price, C. Aaron; Gean, Katherine; Christensen, Claire G.; Beheshti, Elham; Pernot, Bryn; Segovia, Gloria; Person, Halcyon; Beasley, Steven; Ward, Patricia

2016-02-01

Casual games are everywhere. People play them throughout life to pass the time, to engage in social interactions, and to learn. However, their simplicity and use in distraction-heavy environments can attenuate their potential for learning. This experimental study explored the effects playing an online, casual game has on awareness of human biological systems. Two hundred and forty-two children were given pretests at a Museum and posttests at home after playing either a treatment or control game. Also, 41 children were interviewed to explore deeper meanings behind the test results. Results show modest improvement in scientific attitudes, ability to identify human biological systems and in the children's ability to describe how those systems work together in real-world scenarios. Interviews reveal that children drew upon their prior school learning as they played the game. Also, on the surface they perceived the game as mainly entertainment but were easily able to discern learning outcomes when prompted. Implications for the design of casual games and how they can be used to enhance transfer of knowledge from the classroom to everyday life are discussed.

The Future of Contextual Fear Learning for PTSD Research: A Methodological Review of Neuroimaging Studies.

Science.gov (United States)

Glenn, Daniel E; Risbrough, Victoria B; Simmons, Alan N; Acheson, Dean T; Stout, Daniel M

2017-10-21

There has been a great deal of recent interest in human models of contextual fear learning, particularly due to the use of such paradigms for investigating neural mechanisms related to the etiology of posttraumatic stress disorder. However, the construct of "context" in fear conditioning research is broad, and the operational definitions and methods used to investigate contextual fear learning in humans are wide ranging and lack specificity, making it difficult to interpret findings about neural activity. Here we will review neuroimaging studies of contextual fear acquisition in humans. We will discuss the methodology associated with four broad categories of how contextual fear learning is manipulated in imaging studies (colored backgrounds, static picture backgrounds, virtual reality, and configural stimuli) and highlight findings for the primary neural circuitry involved in each paradigm. Additionally, we will offer methodological recommendations for human studies of contextual fear acquisition, including using stimuli that distinguish configural learning from discrete cue associations and clarifying how context is experimentally operationalized.

Teaching medical humanities in the digital world: affordances of technology-enhanced learning.

Science.gov (United States)

Kemp, Sandra Joy; Day, Giskin

2014-12-01

Medical humanities courses are typically taught in face-to-face teaching environments, but now medical humanities educators, alongside educators from other disciplines, are facing shifts in higher education towards online (and sometimes open) courses. For the medical humanities educator, there is limited guidance regarding how technology-enhanced learning design can support the learning outcomes associated with medical humanities. This article aims to provide useful direction for such educators on how digital technologies can be used through learner-focused pedagogies. Specific examples are provided as to how the affordances of Web 2.0 and other tools can be realised in innovative ways to help achieve skills development within the medical humanities. The guidance, alongside the practical suggestions for implementation, can provide important conceptual background for medical humanities educators who wish to embrace technology-enhanced learning, and reconceptualise or redesign medical humanities for an online or blended teaching environment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Social Fear Learning: from Animal Models to Human Function.

Science.gov (United States)

Debiec, Jacek; Olsson, Andreas

2017-07-01

Learning about potential threats is critical for survival. Learned fear responses are acquired either through direct experiences or indirectly through social transmission. Social fear learning (SFL), also known as vicarious fear learning, is a paradigm successfully used for studying the transmission of threat information between individuals. Animal and human studies have begun to elucidate the behavioral, neural and molecular mechanisms of SFL. Recent research suggests that social learning mechanisms underlie a wide range of adaptive and maladaptive phenomena, from supporting flexible avoidance in dynamic environments to intergenerational transmission of trauma and anxiety disorders. This review discusses recent advances in SFL studies and their implications for basic, social and clinical sciences. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Enquiry concerning the nature of Conceptual Categories: a case-study on the social dimension of human cognition

Directory of Open Access Journals (Sweden)

John eStewart

2014-06-01

Full Text Available Cognitive Science, in all its guises, has not yet accorded any fundamental importance to the social dimension of human cognition. In order to illustrate the possibilities that have not so far been developed, this article seeks to pursue the idea, first put forward by Durkheim, that the major categories which render conceptual thought possible may actually have a social origin. Durkheim illustrated his thesis, convincingly enough, by examining the societies of Australian aborigines. The aim here is to extend this idea to cover the case of the conceptual categories underpinning modern Western science, as they developed historically first in Ancient Greece, and then at the Renaissance. These major non-empirical concepts include those of abstract Space (Euclidean space, perfectly homogeneous in all its dimensions; abstract Time (conceived as spatially linearized, with the possibility of imaginatively going back and forth; and a number of canonical logical categories (equality, abstract quantity, essential versus accidental properties, the continuous and the discontinuous, the transcendental…. Sohn-Rethel has proposed that the heart of the conceptual categories in question is to be found in an analysis of the exchange abstraction. This hypothesis will be fleshed out by examining the co-emergence of new social structures and new forms of conceptual thought in the course of historical evolution. This includes the Renaissance, which saw the emergence of both Capitalism and Modern Science; and on the contemporary situation, where the form of social life is dominated by financial speculation which goes together with the advent of automation in the processes of production. It is concluded that Cognitive Science, and in particular the nascent paradigm of Enaction, would do well to broaden its transdisciplinary scope to include the dimensions of sociology and anthropology.

An enquiry concerning the nature of conceptual categories: a case-study on the social dimension of human cognition.

Science.gov (United States)

Stewart, John

2014-01-01

Cognitive Science, in all its guises, has not yet accorded any fundamental importance to the social dimension of human cognition. In order to illustrate the possibilities that have not so far been developed, this article seeks to pursue the idea, first put forward by Durkheim, that the major categories which render conceptual thought possible may actually have a social origin. Durkheim illustrated his thesis, convincingly enough, by examining the societies of Australian aborigines. The aim here is to extend this idea to cover the case of the conceptual categories underpinning modern Western science, as they developed historically first in Ancient Greece, and then at the Renaissance. These major non-empirical concepts include those of abstract Space (Euclidean space, perfectly homogeneous in all its dimensions); abstract Time (conceived as spatially linearized, with the possibility of imaginatively going back and forth); and a number of canonical logical categories (equality, abstract quantity, essential versus accidental properties, the continuous and the discontinuous, the transcendental…). Sohn-Rethel (1978) has proposed that the heart of the conceptual categories in question is to be found in an analysis of the exchange abstraction. This hypothesis will be fleshed out by examining the co-emergence of new social structures and new forms of conceptual thought in the course of historical evolution. This includes the Renaissance, which saw the emergence of both Capitalism and Modern Science; and on the contemporary situation, where the form of social life is dominated by financial speculation which goes together with the advent of automation in the processes of production. It is concluded that Cognitive Science, and in particular the nascent paradigm of Enaction, would do well to broaden its transdisciplinary scope to include the dimensions of sociology and anthropology.

Toward a tactile language for human-robot interaction: two studies of tacton learning and performance.

Science.gov (United States)

Barber, Daniel J; Reinerman-Jones, Lauren E; Matthews, Gerald

2015-05-01

Two experiments were performed to investigate the feasibility for robot-to-human communication of a tactile language using a lexicon of standardized tactons (tactile icons) within a sentence. Improvements in autonomous systems technology and a growing demand within military operations are spurring interest in communication via vibrotactile displays. Tactile communication may become an important element of human-robot interaction (HRI), but it requires the development of messaging capabilities approaching the communication power of the speech and visual signals used in the military. In Experiment 1 (N = 38), we trained participants to identify sets of directional, dynamic, and static tactons and tested performance and workload following training. In Experiment 2 (N = 76), we introduced an extended training procedure and tested participants' ability to correctly identify two-tacton phrases. We also investigated the impact of multitasking on performance and workload. Individual difference factors were assessed. Experiment 1 showed that participants found dynamic and static tactons difficult to learn, but the enhanced training procedure in Experiment 2 produced competency in performance for all tacton categories. Participants in the latter study also performed well on two-tacton phrases and when multitasking. However, some deficits in performance and elevation of workload were observed. Spatial ability predicted some aspects of performance in both studies. Participants may be trained to identify both single tactons and tacton phrases, demonstrating the feasibility of developing a tactile language for HRI. Tactile communication may be incorporated into multi-modal communication systems for HRI. It also has potential for human-human communication in challenging environments. © 2014, Human Factors and Ergonomics Society.

Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.

Science.gov (United States)

Wilson, Benjamin; Smith, Kenny; Petkov, Christopher I

2015-03-01

Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans. © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Categories from scratch

NARCIS (Netherlands)

Poss, R.

2014-01-01

The concept of category from mathematics happens to be useful to computer programmers in many ways. Unfortunately, all "good" explanations of categories so far have been designed by mathematicians, or at least theoreticians with a strong background in mathematics, and this makes categories

Timing of quizzes during learning: Effects on motivation and retention.

Science.gov (United States)

Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson

2017-06-01

This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Human Factors Throughout the Life Cycle: Lessons Learned from the Shuttle Program. [Human Factors in Ground Processing

Science.gov (United States)

Kanki, Barbara G.

2011-01-01

With the ending of the Space Shuttle Program, it is critical that we not forget the Human Factors lessons we have learned over the years. At every phase of the life cycle, from manufacturing, processing and integrating vehicle and payload, to launch, flight operations, mission control and landing, hundreds of teams have worked together to achieve mission success in one of the most complex, high-risk socio-technical enterprises ever designed. Just as there was great diversity in the types of operations performed at every stage, there was a myriad of human factors that could further complicate these human systems. A single mishap or close call could point to issues at the individual level (perceptual or workload limitations, training, fatigue, human error susceptibilities), the task level (design of tools, procedures and aspects of the workplace), as well as the organizational level (appropriate resources, safety policies, information access and communication channels). While we have often had to learn through human mistakes and technological failures, we have also begun to understand how to design human systems in which individuals can excel, where tasks and procedures are not only safe but efficient, and how organizations can foster a proactive approach to managing risk and supporting human enterprises. Panelists will talk about their experiences as they relate human factors to a particular phase of the shuttle life cycle. They will conclude with a framework for tying together human factors lessons-learned into system-level risk management strategies.

Can theories of animal discrimination explain perceptual learning in humans?

Science.gov (United States)

Mitchell, Chris; Hall, Geoffrey

2014-01-01

We present a review of recent studies of perceptual learning conducted with nonhuman animals. The focus of this research has been to elucidate the mechanisms by which mere exposure to a pair of similar stimuli can increase the ease with which those stimuli are discriminated. These studies establish an important role for 2 mechanisms, one involving inhibitory associations between the unique features of the stimuli, the other involving a long-term habituation process that enhances the relative salience of these features. We then examine recent work investigating equivalent perceptual learning procedures with human participants. Our aim is to determine the extent to which the phenomena exhibited by people are susceptible to explanation in terms of the mechanisms revealed by the animal studies. Although we find no evidence that associative inhibition contributes to the perceptual learning effect in humans, initial detection of unique features (those that allow discrimination between 2 similar stimuli) appears to depend on an habituation process. Once the unique features have been detected, a tendency to attend to those features and to learn about their properties enhances subsequent discrimination. We conclude that the effects obtained with humans engage mechanisms additional to those seen in animals but argue that, for the most part, these have their basis in learning processes that are common to animals and people. In a final section, we discuss some implications of this analysis of perceptual learning for other aspects of experimental psychology and consider some potential applications. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

T-category remains an important prognostic factor for oropharyngeal carcinoma in the era of human papillomavirus.

Science.gov (United States)

Mackenzie, P; Pryor, D; Burmeister, E; Foote, M; Panizza, B; Burmeister, B; Porceddu, S

2014-10-01

To determine prognostic factors for locoregional relapse (LRR), distant relapse and all-cause death in a contemporary cohort of locoregionally advanced oropharyngeal squamous cell carcinoma (OSCC) treated with definitive chemoradiotherapy or radiotherapy alone. OSCC patients treated with definitive radiotherapy between 2005 and 2010 were identified from a prospective head and neck database. Patient age, gender, smoking history, human papillomavirus (HPV) status, T- and N-category, lowest involved nodal level and gross tumour volume of the primary (GTV-p) and nodal (GTV-n) disease were analysed in relation to LRR, distant relapse and death by way of univariate and multivariate analysis. In total, 130 patients were identified, 88 HPV positive, with a median follow-up of 42 months. On multivariate analysis HPV status was a significant predictor of LRR (hazard ratio 0.15; 95% confidence interval 0.05-0.51) and death (hazard ratio 0.29; 95% confidence interval 0.14-0.59) but not distant relapse (hazard ratio 0.53, 95% confidence interval 0.22-1.27). Increasing T-category was associated with a higher risk of LRR (hazard ratio 1.80 for T3/4 versus T1/2; 95% confidence interval 1.08-2.99), death (hazard ratio 1.37, 95% confidence interval 1.06-1.77) and distant relapse (hazard ratio 1.35; 95% confidence interval 1.00-1.83). Increasing GTV-p was associated with increased risk of distant relapse and death. N3 disease and low neck nodes were significant for LRR, distant relapse and death on univariate analysis only. Tumour HPV status was the strongest predictor of LRR and death. T-category is more predictive of distant relapse and may provide additional prognostic value for LRR and death when accounting for HPV status. Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Saint rulers as a category of sanctity in Orthodoxy

OpenAIRE

Jarosław Charkiewicz

2015-01-01

In the wide spectre of Orthodox human sanctity, among others, there is also a category of saint rulers. Despite the fact that it’s represented by a significant number of saints, it remains, utterly undeservedly, in the shadow of the others – more known or more deeply rooted historically. On top of that, the situation may result as well from the fact that saint rulers, as a distinct category of saint, are not separately mentioned neither during the proskomedia, nor during the intercessory pray...

The Impacts of System and Human Factors on Online Learning Systems Use and Learner Satisfaction

Science.gov (United States)

Alshare, Khaled A.; Freeze, Ronald D.; Lane, Peggy L.; Wen, H. Joseph

2011-01-01

Success in an online learning environment is tied to both human and system factors. This study illuminates the unique contributions of human factors (comfort with online learning, self-management of learning, and perceived Web self-efficacy) to online learning system success, which is measured in terms of usage and satisfaction. The research model…

Human resource recommendation algorithm based on ensemble learning and Spark

Science.gov (United States)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Modeling human learning involved in car driving

NARCIS (Netherlands)

Wewerinke, P.H.

1994-01-01

In this paper, car driving is considered at the level of human tracking and maneuvering in the context of other traffic. A model analysis revealed the most salient features determining driving performance and safety. Learning car driving is modelled based on a system theoretical approach and based

A dictionary learning approach for human sperm heads classification.

Science.gov (United States)

Shaker, Fariba; Monadjemi, S Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza

2017-12-01

To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Category-length and category-strength effects using images of scenes.

Science.gov (United States)

Baumann, Oliver; Vromen, Joyce M G; Boddy, Adam C; Crawshaw, Eloise; Humphreys, Michael S

2018-06-21

Global matching models have provided an important theoretical framework for recognition memory. Key predictions of this class of models are that (1) increasing the number of occurrences in a study list of some items affects the performance on other items (list-strength effect) and that (2) adding new items results in a deterioration of performance on the other items (list-length effect). Experimental confirmation of these predictions has been difficult, and the results have been inconsistent. A review of the existing literature, however, suggests that robust length and strength effects do occur when sufficiently similar hard-to-label items are used. In an effort to investigate this further, we had participants study lists containing one or more members of visual scene categories (bathrooms, beaches, etc.). Experiments 1 and 2 replicated and extended previous findings showing that the study of additional category members decreased accuracy, providing confirmation of the category-length effect. Experiment 3 showed that repeating some category members decreased the accuracy of nonrepeated members, providing evidence for a category-strength effect. Experiment 4 eliminated a potential challenge to these results. Taken together, these findings provide robust support for global matching models of recognition memory. The overall list lengths, the category sizes, and the number of repetitions used demonstrated that scene categories are well-suited to testing the fundamental assumptions of global matching models. These include (A) interference from memories for similar items and contexts, (B) nondestructive interference, and (C) that conjunctive information is made available through a matching operation.

Duo: A Human/Wearable Hybrid for Learning About Common Manipulate Objects

National Research Council Canada - National Science Library

Kemp, Charles C

2002-01-01

... with them. Duo is a human/wearable hybrid that is designed to learn about this important domain of human intelligence by interacting with natural manipulable objects in unconstrained environments...

Associationism and cognition: human contingency learning at 25.

Science.gov (United States)

Shanks, David R

2007-03-01

A major topic within human learning, the field of contingency judgement, began to emerge about 25 years ago following publication of an article on depressive realism by Alloy and Abramson (1979). Subsequently, associationism has been the dominant theoretical framework for understanding contingency learning but this has been challenged in recent years by an alternative cognitive or inferential approach. This article outlines the key conceptual differences between these approaches and summarizes some of the main methods that have been employed to distinguish between them.

Hedging Your Bets by Learning Reward Correlations in the Human Brain

Science.gov (United States)

Wunderlich, Klaus; Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2011-01-01

Summary Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling. PMID:21943609

Observing tutorial dialogues collaboratively: insights about human tutoring effectiveness from vicarious learning.

Science.gov (United States)

Chi, Michelene T H; Roy, Marguerite; Hausmann, Robert G M

2008-03-01

The goals of this study are to evaluate a relatively novel learning environment, as well as to seek greater understanding of why human tutoring is so effective. This alternative learning environment consists of pairs of students collaboratively observing a videotape of another student being tutored. Comparing this collaboratively observing environment to four other instructional methods-one-on-one human tutoring, observing tutoring individually, collaborating without observing, and studying alone-the results showed that students learned to solve physics problems just as effectively from observing tutoring collaboratively as the tutees who were being tutored individually. We explain the effectiveness of this learning environment by postulating that such a situation encourages learners to become active and constructive observers through interactions with a peer. In essence, collaboratively observing combines the benefit of tutoring with the benefit of collaborating. The learning outcomes of the tutees and the collaborative observers, along with the tutoring dialogues, were used to further evaluate three hypotheses explaining why human tutoring is an effective learning method. Detailed analyses of the protocols at several grain sizes suggest that tutoring is effective when tutees are independently or jointly constructing knowledge: with the tutor, but not when the tutor independently conveys knowledge. 2008 Cognitive Science Society, Inc.

Combining fMRI and behavioral measures to examine the process of human learning.

Science.gov (United States)

Karuza, Elisabeth A; Emberson, Lauren L; Aslin, Richard N

2014-03-01

Prior to the advent of fMRI, the primary means of examining the mechanisms underlying learning were restricted to studying human behavior and non-human neural systems. However, recent advances in neuroimaging technology have enabled the concurrent study of human behavior and neural activity. We propose that the integration of behavioral response with brain activity provides a powerful method of investigating the process through which internal representations are formed or changed. Nevertheless, a review of the literature reveals that many fMRI studies of learning either (1) focus on outcome rather than process or (2) are built on the untested assumption that learning unfolds uniformly over time. We discuss here various challenges faced by the field and highlight studies that have begun to address them. In doing so, we aim to encourage more research that examines the process of learning by considering the interrelation of behavioral measures and fMRI recording during learning. Copyright © 2013 Elsevier Inc. All rights reserved.

Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

Science.gov (United States)

Kim, Su Kyoung; Kirchner, Elsa Andrea; Stefes, Arne; Kirchner, Frank

2017-12-14

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.

Human Systems Integration in Practice: Constellation Lessons Learned

Science.gov (United States)

Zumbado, Jennifer Rochlis

2012-01-01

NASA's Constellation program provided a unique testbed for Human Systems Integration (HSI) as a fundamental element of the Systems Engineering process. Constellation was the first major program to have HSI mandated by NASA's Human Rating document. Proper HSI is critical to the success of any project that relies on humans to function as operators, maintainers, or controllers of a system. HSI improves mission, system and human performance, significantly reduces lifecycle costs, lowers risk and minimizes re-design. Successful HSI begins with sufficient project schedule dedicated to the generation of human systems requirements, but is by no means solely a requirements management process. A top-down systems engineering process that recognizes throughout the organization, human factors as a technical discipline equal to traditional engineering disciplines with authority for the overall system. This partners with a bottoms-up mechanism for human-centered design and technical issue resolution. The Constellation Human Systems Integration Group (HSIG) was a part of the Systems Engineering and Integration (SE&I) organization within the program office, and existed alongside similar groups such as Flight Performance, Environments & Constraints, and Integrated Loads, Structures and Mechanisms. While the HSIG successfully managed, via influence leadership, a down-and-in Community of Practice to facilitate technical integration and issue resolution, it lacked parallel top-down authority to drive integrated design. This presentation will discuss how HSI was applied to Constellation, the lessons learned and best practices it revealed, and recommendations to future NASA program and project managers. This presentation will discuss how Human Systems Integration (HSI) was applied to NASA's Constellation program, the lessons learned and best practices it revealed, and recommendations to future NASA program and project managers on how to accomplish this critical function.

Human-simulation-based learning to prevent medication error: A systematic review.

Science.gov (United States)

Sarfati, Laura; Ranchon, Florence; Vantard, Nicolas; Schwiertz, Vérane; Larbre, Virginie; Parat, Stéphanie; Faudel, Amélie; Rioufol, Catherine

2018-01-31

In the past 2 decades, there has been an increasing interest in simulation-based learning programs to prevent medication error (ME). To improve knowledge, skills, and attitudes in prescribers, nurses, and pharmaceutical staff, these methods enable training without directly involving patients. However, best practices for simulation for healthcare providers are as yet undefined. By analysing the current state of experience in the field, the present review aims to assess whether human simulation in healthcare helps to reduce ME. A systematic review was conducted on Medline from 2000 to June 2015, associating the terms "Patient Simulation," "Medication Errors," and "Simulation Healthcare." Reports of technology-based simulation were excluded, to focus exclusively on human simulation in nontechnical skills learning. Twenty-one studies assessing simulation-based learning programs were selected, focusing on pharmacy, medicine or nursing students, or concerning programs aimed at reducing administration or preparation errors, managing crises, or learning communication skills for healthcare professionals. The studies varied in design, methodology, and assessment criteria. Few demonstrated that simulation was more effective than didactic learning in reducing ME. This review highlights a lack of long-term assessment and real-life extrapolation, with limited scenarios and participant samples. These various experiences, however, help in identifying the key elements required for an effective human simulation-based learning program for ME prevention: ie, scenario design, debriefing, and perception assessment. The performance of these programs depends on their ability to reflect reality and on professional guidance. Properly regulated simulation is a good way to train staff in events that happen only exceptionally, as well as in standard daily activities. By integrating human factors, simulation seems to be effective in preventing iatrogenic risk related to ME, if the program is

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

Science.gov (United States)

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as

The Law Review Approach: What the Humanities Can Learn

Science.gov (United States)

Mendenhall, Allen

2013-01-01

Readers of this journal probably know how the peer review process works in the humanities disciplines and at various journals. Therefore the author explains how the law review process generally works and then what the humanities can learn and borrow from the law review process. He ends by advocating for a hybrid law review/peer review approach to…

Mobile human-computer interaction perspective on mobile learning

CSIR Research Space (South Africa)

Botha, Adèle

2010-10-01

Full Text Available Applying a Mobile Human Computer Interaction (MHCI) view to the domain of education using Mobile Learning (Mlearning), the research outlines its understanding of the influences and effects of different interactions on the use of mobile technology...

The impact of category prices on store price image formation : An empirical analysis

NARCIS (Netherlands)

Da Silva Lourenço, C.J.; Gijsbrechts, E.; Paap, R.

2015-01-01

The authors empirically explore how consumers update beliefs about a store's overall expensiveness. They estimate a learning model of store price image (SPI) formation with the impact of actual prices linked to category characteristics, on a unique dataset combining store visit and purchase

An apple a day keeps the doctor away: children's evaluative categories of food.

Science.gov (United States)

Nguyen, Simone P

2007-01-01

This study explores how children evaluatively categorize foods based on their nutritional value. Three-year-olds, four-year-olds, seven-year-olds, and adults completed a task in which they categorized a list of 70 foods as healthy or junky. The results showed important developmental differences in participants' ability to accurately classify foods as healthy/junky and to provide relevant justifications for these classifications. These results suggest that a large amount of category learning occurs with development, especially as children incorporate different types of information about food nutrition into their evaluative category representations.

CERN to introduce new Local Staff employment category

CERN Multimedia

2003-01-01

At the June meeting of CERN Council, a new Local Staff employment category was approved. This will cover some 250-300 people in technical and administrative positions between now and 2010, satisfying an urgent need for manpower over the coming years. This article explains the main features of this new category. The Local Staff employment category is an important building block in CERN's new Human Resources Plan, and is essential in the run-up to the LHC. In the immediate future, it will allow some Industrial Services activities to be insourced - corresponding to about 150 additional CERN staff positions. In the longer run, it will allow the Organization to replace more retiring staff members than formerly foreseen - corresponding to 100-150 staff positions. The activities that will lead to Local Staff vacancies were identified at last year's resources planning exercise (the "Morges-III" meetings) as those which could not be outsourced in a Field Support Unit or other type of result-oriented Industrial Serv...

Study of maintenance skill-work based on PSFs and error category

International Nuclear Information System (INIS)

Nagata, Manabu; Yukimachi, Takeo; Hasegawa, Toshio

2001-01-01

In this investigation, the skill-types of skill-work are clarified according to the human error data on the maintenance works at nuclear power plants. At first, the causal PSFs of the errors are extracted from the data and some of the skill-types are characterized as results from factor analysis. Moreover, the skill-work model is reexamined on the basis of the contents of the human error data and the error category corresponding to the data. Furthermore, integrating the tendency of the causal PSFs and the actual error category concerning each skill-type, an extended skill-work model was developed with a flow-chart representation as a tentative stage of the investigation. (author)

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 ...

Social learning as a way to overcome choice-induced preferences? Insights from humans and rhesus macaques.

Directory of Open Access Journals (Sweden)

ELISABETTA eMONFARDINI

2012-09-01

Full Text Available Much theoretical attention is currently devoted to social learning. Yet, empirical studies formally comparing its effectiveness relative to individual learning are rare. Here, we focus on free choice, which is at the heart of individual reward-based learning, but absent in social learning. Choosing among two equally valued options is known to create a preference for the selected option in both humans and monkeys. We thus surmised that social learning should be more helpful when choice-induced preferences retard individual learning than when they optimize it. To test this prediction, the same task requiring to find which among two items concealed a reward was applied to rhesus macaques and humans. The initial trial was individual or social, rewarded or unrewarded. Learning was assessed on the second trial. Choice-induced preference strongly affected individual learning. Monkeys and humans performed much more poorly after an initial negative choice than after an initial positive choice. Comparison with social learning verified our prediction. For negative outcome, social learning surpassed or at least equaled individual learning in all subjects. For positive outcome, the predicted superiority of individual learning did occur in a majority of subjects (5/6 monkeys and 6/12 humans. A minority kept learning better socially though, perhaps due to a more dominant/aggressive attitude toward peers. Poor learning from errors due to over-valuation of personal choices is among the decision-making biases shared by humans and animals. The present study suggests that choice-immune social learning may help curbing this potentially harmful tendency. Learning from successes is an easier path. The present data suggest that whether one tends to walk it alone or with a peer's help might depend on the social dynamics within the actor/observer dyad.

An analysis of medical students’ reflective essays in problem-based learning

Directory of Open Access Journals (Sweden)

Jihyun Si

2018-03-01

Full Text Available Purpose This study aimed to explore students’ learning experience in problem-based learning (PBL particularly in terms of what they learned and how they learned in one Korean medical school by analyzing their reflective essays with qualitative research methods. Methods This study included 44 first-year medical students. They took three consecutive PBL courses and wrote reflective essays 3 times anonymously on the last day of each course. Their reflective essays were analyzed using an inductive content analysis method. Results The coding process yielded 16 sub-categories and these categories were grouped into six categories according to the distinctive characteristics of PBL learning experience: integrated knowledge base, clinical problem solving, collaboration, intrinsic motivation, self-directed learning, and professional attitude. Among these categories, integrated knowledge base (34.68% and professional attitude (2.31% were the categories mentioned most and least frequently. Conclusion The findings of this study provide an overall understanding of the learning experience of Korean medical students during PBL in terms of what they learned and how they learned with rich descriptive commentaries from their perspectives as well as several thoughtful insights to help develop instructional strategies to enhance the effectiveness of PBL.

Can height categories replace weight categories in striking martial arts competitions? A pilot study.

Science.gov (United States)

Dubnov-Raz, Gal; Mashiach-Arazi, Yael; Nouriel, Ariella; Raz, Raanan; Constantini, Naama W

2015-09-29

In most combat sports and martial arts, athletes compete within weight categories. Disordered eating behaviors and intentional pre-competition rapid weight loss are commonly seen in this population, attributed to weight categorization. We examined if height categories can be used as an alternative to weight categories for competition, in order to protect the health of athletes. Height and weight of 169 child and adolescent competitive karate athletes were measured. Participants were divided into eleven hypothetical weight categories of 5 kg increments, and eleven hypothetical height categories of 5 cm increments. We calculated the coefficient of variation of height and weight by each division method. We also calculated how many participants fit into corresponding categories of both height and weight, and how many would shift a category if divided by height. There was a high correlation between height and weight (r = 0.91, p<0.001). The mean range of heights seen within current weight categories was reduced by 83% when participants were divided by height. When allocating athletes by height categories, 74% of athletes would shift up or down one weight category at most, compared with the current categorization method. We conclude that dividing young karate athletes by height categories significantly reduced the range of heights of competitors within the category. Such categorization would not cause athletes to compete against much heavier opponents in most cases. Using height categories as a means to reduce eating disorders in combat sports should be further examined.

A Benefit/Cost/Deficit (BCD) model for learning from human errors

International Nuclear Information System (INIS)

Vanderhaegen, Frederic; Zieba, Stephane; Enjalbert, Simon; Polet, Philippe

2011-01-01

This paper proposes an original model for interpreting human errors, mainly violations, in terms of benefits, costs and potential deficits. This BCD model is then used as an input framework to learn from human errors, and two systems based on this model are developed: a case-based reasoning system and an artificial neural network system. These systems are used to predict a specific human car driving violation: not respecting the priority-to-the-right rule, which is a decision to remove a barrier. Both prediction systems learn from previous violation occurrences, using the BCD model and four criteria: safety, for identifying the deficit or the danger; and opportunity for action, driver comfort, and time spent; for identifying the benefits or the costs. The application of learning systems to predict car driving violations gives a rate over 80% of correct prediction after 10 iterations. These results are validated for the non-respect of priority-to-the-right rule.

Developing physics learning media using 3D cartoon

Science.gov (United States)

Wati, M.; Hartini, S.; Hikmah, N.; Mahtari, S.

2018-03-01

This study focuses on developing physics learning media using 3D cartoon on the static fluid topic. The purpose of this study is to describe: (1) the validity of the learning media, (2) the practicality of the learning media, and (3) the effectiveness of the learning media. This study is a research and development using ADDIE model. The subject of the implementation of media used class XI Science of SMAN 1 Pulau Laut Timur. The data were obtained from the validation sheet of the learning media, questionnaire, and the test of learning outcomes. The results showed that: (1) the validity of the media category is valid, (2) the practicality of the media category is practice, and (3) the effectiveness of the media category is effective. It is concluded that the learning using 3D cartoon on the static fluid topic is eligible to use in learning.

Adults' and Children's Understanding of How Expertise Influences Learning.

Science.gov (United States)

Danovitch, Judith H; Shenouda, Christine K

2018-01-01

Adults and children use information about expertise to infer what a person is likely to know, but it is unclear whether they realize that expertise also has implications for learning. We explore adults' and children's understanding that expertise in a particular category supports learning about a closely related category. In four experiments, 5-year-olds and adults (n = 160) judged which of two people would be better at learning about a new category. When faced with an expert and a nonexpert, adults consistently indicated that expertise supports learning in a closely related category; however, children's judgments were inconsistent and were strongly influenced by the description of the nonexpert. The results suggest that although children understand what it means to be an expert, they may judge an individual's learning capacity based on different considerations than adults.

When it hurts (and helps to try: the role of effort in language learning.

Directory of Open Access Journals (Sweden)

Amy S Finn

Full Text Available Compared to children, adults are bad at learning language. This is counterintuitive; adults outperform children on most measures of cognition, especially those that involve effort (which continue to mature into early adulthood. The present study asks whether these mature effortful abilities interfere with language learning in adults and further, whether interference occurs equally for aspects of language that adults are good (word-segmentation versus bad (grammar at learning. Learners were exposed to an artificial language comprised of statistically defined words that belong to phonologically defined categories (grammar. Exposure occurred under passive or effortful conditions. Passive learners were told to listen while effortful learners were instructed to try to 1 learn the words, 2 learn the categories, or 3 learn the category-order. Effortful learners showed an advantage for learning words while passive learners showed an advantage for learning the categories. Effort can therefore hurt the learning of categories.

When It Hurts (and Helps) to Try: The Role of Effort in Language Learning

Science.gov (United States)

Finn, Amy S.; Lee, Taraz; Kraus, Allison; Hudson Kam, Carla L.

2014-01-01

Compared to children, adults are bad at learning language. This is counterintuitive; adults outperform children on most measures of cognition, especially those that involve effort (which continue to mature into early adulthood). The present study asks whether these mature effortful abilities interfere with language learning in adults and further, whether interference occurs equally for aspects of language that adults are good (word-segmentation) versus bad (grammar) at learning. Learners were exposed to an artificial language comprised of statistically defined words that belong to phonologically defined categories (grammar). Exposure occurred under passive or effortful conditions. Passive learners were told to listen while effortful learners were instructed to try to 1) learn the words, 2) learn the categories, or 3) learn the category-order. Effortful learners showed an advantage for learning words while passive learners showed an advantage for learning the categories. Effort can therefore hurt the learning of categories. PMID:25047901

Detecting anomalous nuclear materials accounting transactions: Applying machine learning to plutonium processing facilities

International Nuclear Information System (INIS)

Vaccaro, H.S.

1989-01-01

Nuclear materials accountancy is the only safeguards measure that provides direct evidence of the status of nuclear materials. Of the six categories that gives rise to inventory differences, the technical capability is now in place to implement the technical innovations necessary to reduce the human error categories. There are really three main approaches to detecting anomalies in materials control and accountability (MC ampersand A) data: (1) Statistical: numeric methods such as the Page's Test, CUSUM, CUMUF, SITMUF, etc., can detect anomalies in metric (numeric) data. (2) Expert systems: Human expert's rules can be encoded into software systems such as ART, KEE, or Prolog. (3) Machine learning: Training data, such as historical MC ampersand A records, can be fed to a classifier program or neutral net or other machine learning algorithm. The Wisdom ampersand Sense (W ampersand S) software is a combination of approaches 2 and 3. The W ampersand S program includes full features for adding administrative rules and expert judgment rules to the rule base. if desired, the software can enforce consistency among all rules in the rule base

Distributional Language Learning: Mechanisms and Models of ategory Formation.

Science.gov (United States)

Aslin, Richard N; Newport, Elissa L

2014-09-01

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

Learning of Alignment Rules between Concept Hierarchies

Science.gov (United States)

Ichise, Ryutaro; Takeda, Hideaki; Honiden, Shinichi

With the rapid advances of information technology, we are acquiring much information than ever before. As a result, we need tools for organizing this data. Concept hierarchies such as ontologies and information categorizations are powerful and convenient methods for accomplishing this goal, which have gained wide spread acceptance. Although each concept hierarchy is useful, it is difficult to employ multiple concept hierarchies at the same time because it is hard to align their conceptual structures. This paper proposes a rule learning method that inputs information from a source concept hierarchy and finds suitable location for them in a target hierarchy. The key idea is to find the most similar categories in each hierarchy, where similarity is measured by the κ(kappa) statistic that counts instances belonging to both categories. In order to evaluate our method, we conducted experiments using two internet directories: Yahoo! and LYCOS. We map information instances from the source directory into the target directory, and show that our learned rules agree with a human-generated assignment 76% of the time.

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.

From groups to categorial algebra introduction to protomodular and mal’tsev categories

CERN Document Server

Bourn, Dominique

2017-01-01

This book gives a thorough and entirely self-contained, in-depth introduction to a specific approach to group theory, in a large sense of that word. The focus lie on the relationships which a group may have with other groups, via “universal properties”, a view on that group “from the outside”. This method of categorical algebra, is actually not limited to the study of groups alone, but applies equally well to other similar categories of algebraic objects. By introducing protomodular categories and Mal’tsev categories, which form a larger class, the structural properties of the category Gp of groups, show how they emerge from four very basic observations about the algebraic litteral calculus and how, studied for themselves at the conceptual categorical level, they lead to the main striking features of the category Gp of groups. Hardly any previous knowledge of category theory is assumed, and just a little experience with standard algebraic structures such as groups and monoids. Examples and exercises...

Extending human potential in a technical learning environment

Science.gov (United States)

Fielden, Kay A.

This thesis is a report of a participatory inquiry process looking at enhancing the learning process in a technical academic field in high education by utilising tools and techniques which go beyond the rational/logical, intellectual domain in a functional, objective world. By empathising with, nurturing and sustaining the whole person, and taking account of past patterning as well as future visions including technological advances to augment human awareness, the scene is set for depth learning. Depth learning in a tertiary environment can only happen as a result of the dynamic that exists between the dominant, logical/rational, intellectual paradigm and the experiential extension of the boundaries surrounding this domain. Any experiences which suppress the full, holistic expression of our being alienate us from the fullness of the expression and hence from depth learning. Depth learning is indicated by intrinsic motivation, which is more likely to occur in a trusting and supporting environment. The research took place within a systemic intellectual framework, where emergence is the prime characteristic used to evaluate results.

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Science.gov (United States)

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

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...

Proactive Interference in Human Predictive Learning

OpenAIRE

Castro, Leyre; Ortega, Nuria; Matute, Helena

2002-01-01

The impairment in responding to a secondly trained association because of the prior training of another (i.e., proactive interference) is a well-established effect in human and animal research, and it has been demonstrated in many paradigms. However, learning theories have been concerned with proactive interference only when the competing stimuli have been presented in compound at some moment of the training phase. In this experiment we investigated the possibility of proactive interference b...

Pragmatic Frames for Teaching and Learning in Human-Robot Interaction: Review and Challenges.

Science.gov (United States)

Vollmer, Anna-Lisa; Wrede, Britta; Rohlfing, Katharina J; Oudeyer, Pierre-Yves

2016-01-01

One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning-teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human-human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; (2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching.

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.

Categorial Ontology of Complex Systems, Meta-Systems and Levels: The Emergence of Life, Human Consciousness and Society

Directory of Open Access Journals (Sweden)

James F. Glazebrook

2010-06-01

Full Text Available Relational structures of organisms and the human mind are naturally represented in terms of novel variable topology concepts, non-Abelian categories and Higher Dimensional Algebra{ relatively new concepts that would be defined in
this tutorial paper. A unifying theme of local-to-global approaches to organismic development, evolution and human consciousness leads to novel patterns of relations that emerge in super- and ultra- complex systems in terms of compositions of local procedures [1]. The claim is defended in this paper that human consciousness is unique and should be viewed as an ultra-complex, global process of processes, at a meta-level not sub{summed by, but compatible with, human brain dynamics [2]-[5]. The emergence of consciousness and its existence
are considered to be dependent upon an extremely complex structural and functional unit with an asymmetric network topology and connectivities{the human brain. However, the appearance of human consciousness is shown to be critically dependent upon societal co-evolution, elaborate language-symbolic communication and `virtual', higher dimensional, non{commutative processes involving separate space and time perceptions. Theories of the mind are approached from the theory of levels and ultra-complexity viewpoints that throw
new light on previous semantic models in cognitive science. Anticipatory systems and complex causality at the top levels of reality are discussed in the context of psychology, sociology and ecology. A paradigm shift towards non-commutative, or more generally, non-Abelian theories of highly complex dynamics [6] is suggested to unfold now in physics, mathematics, life and cognitive sciences, thus leading to the realizations of higher dimensional algebras in neurosciences and psychology, as well as in human genomics, bioinformatics and interactomics. The presence of strange attractors in modern society dynamics gives rise to very serious concerns for the future

Medical humanities: a closer look at learning.

Science.gov (United States)

Patterson, A; Sharek, D; Hennessy, M; Phillips, M; Schofield, S

2016-06-01

The inclusion of medical humanities with medical curricula is a question that has been the focus of attention for many within the evolving field. This study addressed the question from a medical education perspective and aimed to investigate what students at Trinity College Dublin learned from participating in a short medical humanities student-selected module in their first year of an undergraduate medical programme. A total of 156 students provided a written reflection on a memorable event that occurred during their student-selected module. The reflections were analysed using the Reflection Evaluation for Learners' Enhanced Competencies Tool (REFLECT) and through qualitative thematic analysis of the written reflections. Evidence of learning from the REFLECT quantitative analysis showed that 50% of students displayed higher levels of reflection when describing their experience. The reflection content analysis supported the heterogeneous nature of learning outcome for students, with evidence to support the idea that the module provided opportunities for students to explore their beliefs, ideas and feelings regarding a range of areas outside their current experience or world view, to consider the views of others that they may have not previously been aware of, to reflect on their current views, and to consider their future professional practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Developing Autonomous Vehicles That Learn to Navigate by Mimicking Human Behavior

Science.gov (United States)

2006-09-28

navigate in an unstructured environment to a specific target or location. 15. SUBJECT TERMS autonomous vehicles , fuzzy logic, learning behavior...ANSI-Std Z39-18 Developing Autonomous Vehicles That Learn to Navigate by Mimicking Human Behavior FINAL REPORT 9/28/2006 Dean B. Edwards Department...the future, as greater numbers of autonomous vehicles are employed, it is hoped that lower LONG-TERM GOALS Use LAGR (Learning Applied to Ground Robots

A Conceptual Framework over Contextual Analysis of Concept Learning within Human-Machine Interplays

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

This research provides a contextual description concerning existential and structural analysis of ‘Relations’ between human beings and machines. Subsequently, it will focus on conceptual and epistemological analysis of (i) my own semantics-based framework [for human meaning construction] and of (ii......) a well-structured machine concept learning framework. Accordingly, I will, semantically and epistemologically, focus on linking those two frameworks for logical analysis of concept learning in the context of human-machine interrelationships. It will be demonstrated that the proposed framework provides...

Inquiry-Based Learning in Teacher Education: A Primary Humanities Example

Science.gov (United States)

Preston, Lou; Harvie, Kate; Wallace, Heather

2015-01-01

Inquiry-based learning features strongly in the new Australian Humanities and Social Sciences curriculum and increasingly in primary school practice. Yet, there is little research into, and few exemplars of, inquiry approaches in the primary humanities context. In this article, we outline and explain the implementation of a place-based simulation…

Climate for Learning: A Symposium. Creating a Climate for Learning, and the Humanizing Process. The Principal and School Discipline. Curriculum Bulletin Vol. XXXII, No. 341.

Science.gov (United States)

Johnson, Simon O.; Chaky, June

This publication contains two articles focusing on creating a climate for learning. In "Creating a Climate for Learning, and the Humanizing Process," Simon O. Johnson offers practical suggestions for creating a humanistic learning environment. The author begins by defining the basic concepts--humanism, affective education, affective situation,…

A Continuum of Learning: From Rote Memorization to Meaningful Learning in Organic Chemistry

Science.gov (United States)

Grove, Nathaniel P.; Bretz, Stacey Lowery

2012-01-01

The Assimilation Theory of Ausubel and Novak has typically been used in the research literature to describe two extremes to learning chemistry: meaningful learning "versus" rote memorization. It is unlikely, however, that such discrete categories of learning exist. Rote and meaningful learning, rather, are endpoints along a continuum of…

Functional categories in comparative linguistics

DEFF Research Database (Denmark)

Rijkhoff, Jan

, Roger M. 1979. Linguistic knowledge and cultural knowledge: some doubts and speculation. American Anthropologist 81-1, 14-36. Levinson, Stephen C. 1997. From outer to inner space: linguistic categories and non-linguistic thinking. In J. Nuyts and E. Pederson (eds.), Language and Conceptualization, 13......). Furthermore certain ‘ontological categories’ are language-specific (Malt 1995). For example, speakers of Kalam (New Guinea) do not classify the cassowary as a bird, because they believe it has a mythical kinship relation with humans (Bulmer 1967).       In this talk I will discuss the role of functional...

Picture-Word Differences in Discrimination Learning: II. Effects of Conceptual Categories.

Science.gov (United States)

Bourne, Lyle E., Jr.; And Others

A well established finding in the discrimination learning literature is that pictures are learned more rapidly than their associated verbal labels. It was hypothesized in this study that the usual superiority of pictures over words in a discrimination list containing same-instance repetitions would disappear in a discrimination list containing…

Neural computations mediating one-shot learning in the human brain.

Directory of Open Access Journals (Sweden)

Sang Wan Lee

2015-04-01

Full Text Available Incremental learning, in which new knowledge is acquired gradually through trial and error, can be distinguished from one-shot learning, in which the brain learns rapidly from only a single pairing of a stimulus and a consequence. Very little is known about how the brain transitions between these two fundamentally different forms of learning. Here we test a computational hypothesis that uncertainty about the causal relationship between a stimulus and an outcome induces rapid changes in the rate of learning, which in turn mediates the transition between incremental and one-shot learning. By using a novel behavioral task in combination with functional magnetic resonance imaging (fMRI data from human volunteers, we found evidence implicating the ventrolateral prefrontal cortex and hippocampus in this process. The hippocampus was selectively "switched" on when one-shot learning was predicted to occur, while the ventrolateral prefrontal cortex was found to encode uncertainty about the causal association, exhibiting increased coupling with the hippocampus for high-learning rates, suggesting this region may act as a "switch," turning on and off one-shot learning as required.

Back-dropout Transfer Learning for Action Recognition

DEFF Research Database (Denmark)

Ren, Huamin; Kanhabua, Nattiya; Møgelmose, Andreas

2018-01-01

transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category...

A learning-based autonomous driver: emulate human driver's intelligence in low-speed car following

Science.gov (United States)

Wei, Junqing; Dolan, John M.; Litkouhi, Bakhtiar

2010-04-01

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver's decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

Saint rulers as a category of sanctity in Orthodoxy

Directory of Open Access Journals (Sweden)

Jarosław Charkiewicz

2015-11-01

Full Text Available In the wide spectre of Orthodox human sanctity, among others, there is also a category of saint rulers. Despite the fact that it’s represented by a significant number of saints, it remains, utterly undeservedly, in the shadow of the others – more known or more deeply rooted historically. On top of that, the situation may result as well from the fact that saint rulers, as a distinct category of saint, are not separately mentioned neither during the proskomedia, nor during the intercessory prayer of the anaphora. Still, the saint rulers definitely should be considered a separate type of sanctity, deserving a somehow wider presentation. Such is therefore the aim of this article.It is also an attempt of suggesting an interior systematic of this category of sanctity. Preserving, appearing in science, classifications of the saint rulers, the author gives also his own proposition. In the category of saint rulers there are four following groups: (1 rulerswho weren’t neither martyrs nor monks or passion-bearers, (2 rulers who died martyrs, (3 rulers who long before their death became monks and led monastic life, (4 rulers-passion-bearers.

Discrimination of complex human behavior by pigeons (Columba livia and humans.

Directory of Open Access Journals (Sweden)

Muhammad A J Qadri

Full Text Available The cognitive and neural mechanisms for recognizing and categorizing behavior are not well understood in non-human animals. In the current experiments, pigeons and humans learned to categorize two non-repeating, complex human behaviors ("martial arts" vs. "Indian dance". Using multiple video exemplars of a digital human model, pigeons discriminated these behaviors in a go/no-go task and humans in a choice task. Experiment 1 found that pigeons already experienced with discriminating the locomotive actions of digital animals acquired the discrimination more rapidly when action information was available than when only pose information was available. Experiments 2 and 3 found this same dynamic superiority effect with naïve pigeons and human participants. Both species used the same combination of immediately available static pose information and more slowly perceived dynamic action cues to discriminate the behavioral categories. Theories based on generalized visual mechanisms, as opposed to embodied, species-specific action networks, offer a parsimonious account of how these different animals recognize behavior across and within species.

Towards Semantic Analysis of Training-Learning Relationships within Human-Machine Interactions

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

In this article First-Order Predicate Logic (FOL) is employed for analysing some relationships between human beings and machines. Based on FOL, I will be conceptually and logically concerned with semantic analysis of training-learning relationships in human-machine interaction. The central focus...

Learning Object Names at Different Hierarchical Levels Using Cross-Situational Statistics.

Science.gov (United States)

Chen, Chi-Hsin; Zhang, Yayun; Yu, Chen

2018-05-01

Objects in the world usually have names at different hierarchical levels (e.g., beagle, dog, animal). This research investigates adults' ability to use cross-situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co-occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected the learning of individual object labels, but not the learning of category labels. Learners' subsequent generalization of category labels indicated sensitivity to the structure of statistical input. Copyright © 2017 Cognitive Science Society, 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.

Office of Medicare Hearings and Appeals (OMHA) - Receipts by Appeal Category

Data.gov (United States)

U.S. Department of Health & Human Services — This data set provides information about the appeals and claims received by the Office of Medicare and Hearings by appeal category for Fiscal Year 2006 - 2012.

SnapAnatomy, a computer-based interactive tool for independent learning of human anatomy.

Science.gov (United States)

Yip, George W; Rajendran, Kanagasuntheram

2008-06-01

Computer-aided instruction materials are becoming increasing popular in medical education and particularly in the teaching of human anatomy. This paper describes SnapAnatomy, a new interactive program that the authors designed for independent learning of anatomy. SnapAnatomy is primarily tailored for the beginner student to encourage the learning of anatomy by developing a three-dimensional visualization of human structure that is essential to applications in clinical practice and the understanding of function. The program allows the student to take apart and to accurately put together body components in an interactive, self-paced and variable manner to achieve the learning outcome.

Perceptions of nursing undergraduate students concerning the human dimension in the learning process.

Science.gov (United States)

de Oliveira Camillo, Simone; Lúcia da Silva, Ana; Jefferson do Nascimento, Alan

2007-01-01

This study aimed to identify and interpret the perceptions presented by undergraduate students of a Nursing course after internship in Mental Health. Twelve nursing undergraduate students at the Nursing School of ABC Foundation - Santo André, São Paulo, Brazil were interviewed. These interviews using a semi-structure script were performed and recorded in August 2004. Through Content Analysis, thematic modality, four categories were identified, 1. mental health: providing understanding of the other; 2. respect for the human being: the importance of listening, 3. mental health: contributing for a contextualized view of the patient and 4. nursing graduation: undesirable "signs and symptoms" of the profession. The analysis and the discussion of these categories suggest the possibility of teaching based on the human condition. Thus, we support the idea of new research been carried out, considering that the Mental Health discipline must be valued in the Political and Pedagogical projects of the Nursing Undergraduate Courses.

Encoding tasks dissociate the effects of divided attention on category-cued recall and category-exemplar generation.

Science.gov (United States)

Parker, Andrew; Dagnall, Neil; Munley, Gary

2012-01-01

The combined effects of encoding tasks and divided attention upon category-exemplar generation and category-cued recall were examined. Participants were presented with pairs of words each comprising a category name and potential example of that category. They were then asked to indicate either (i) their liking for both of the words or (ii) if the exemplar was a member of the category. It was found that divided attention reduced performance on the category-cued recall task under both encoding conditions. However, performance on the category-exemplar generation task remained invariant across the attention manipulation following the category judgment task. This provides further evidence that the processes underlying performance on conceptual explicit and implicit memory tasks can be dissociated, and that the intentional formation of category-exemplar associations attenuates the effects of divided attention on category-exemplar generation.

Contingency learning in human fear conditioning involves the ventral striatum.

Science.gov (United States)

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

2009-11-01

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

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

Learning to walk before we run: what can medical education learn from the human body about integrated care?

Directory of Open Access Journals (Sweden)

Eron G. Manusov

2013-05-01

Full Text Available True integration requires a shift in all levels of medical and allied health education; one that emphasizes team learning, practicing, and evaluating from the beginning of each students' educational experience whether that is as physician, nurse, psychologist, or any other health profession.  Integration of healthcare services will not occur until medical education focuses, like the human body, on each system working inter-dependently and cohesively to maintain balance through continual change and adaptation.  The human body develops and maintains homeostasis by a process of communication: true integrated care relies on learned interprofessionality and ensures shared responsibility and practice.

Learning to walk before we run: what can medical education learn from the human body about integrated care.

Science.gov (United States)

Manusov, Eron G; Marlowe, Daniel P; Teasley, Deborah J

2013-04-01

True integration requires a shift in all levels of medical and allied health education; one that emphasizes team learning, practicing, and evaluating from the beginning of each students' educational experience whether that is as physician, nurse, psychologist, or any other health profession. Integration of healthcare services will not occur until medical education focuses, like the human body, on each system working inter-dependently and cohesively to maintain balance through continual change and adaptation. The human body develops and maintains homeostasis by a process of communication: true integrated care relies on learned interprofessionality and ensures shared responsibility and practice.

Web-based e-learning and virtual lab of human-artificial immune system.

Science.gov (United States)

Gong, Tao; Ding, Yongsheng; Xiong, Qin

2014-05-01

Human immune system is as important in keeping the body healthy as the brain in supporting the intelligence. However, the traditional models of the human immune system are built on the mathematics equations, which are not easy for students to understand. To help the students to understand the immune systems, a web-based e-learning approach with virtual lab is designed for the intelligent system control course by using new intelligent educational technology. Comparing the traditional graduate educational model within the classroom, the web-based e-learning with the virtual lab shows the higher inspiration in guiding the graduate students to think independently and innovatively, as the students said. It has been found that this web-based immune e-learning system with the online virtual lab is useful for teaching the graduate students to understand the immune systems in an easier way and design their simulations more creatively and cooperatively. The teaching practice shows that the optimum web-based e-learning system can be used to increase the learning effectiveness of the students.

Neural Computations Mediating One-Shot Learning in the Human Brain

Science.gov (United States)

Lee, Sang Wan; O’Doherty, John P.; Shimojo, Shinsuke

2015-01-01

Incremental learning, in which new knowledge is acquired gradually through trial and error, can be distinguished from one-shot learning, in which the brain learns rapidly from only a single pairing of a stimulus and a consequence. Very little is known about how the brain transitions between these two fundamentally different forms of learning. Here we test a computational hypothesis that uncertainty about the causal relationship between a stimulus and an outcome induces rapid changes in the rate of learning, which in turn mediates the transition between incremental and one-shot learning. By using a novel behavioral task in combination with functional magnetic resonance imaging (fMRI) data from human volunteers, we found evidence implicating the ventrolateral prefrontal cortex and hippocampus in this process. The hippocampus was selectively “switched” on when one-shot learning was predicted to occur, while the ventrolateral prefrontal cortex was found to encode uncertainty about the causal association, exhibiting increased coupling with the hippocampus for high-learning rates, suggesting this region may act as a “switch,” turning on and off one-shot learning as required. PMID:25919291

Significant Learning and Civic Education: Shifting Frameworks for Teaching in Light of Learning about the Financial Crisis

Directory of Open Access Journals (Sweden)

KimMarie McGoldrick

2011-10-01

Full Text Available The recent financial crisis has motivated economic educators to rethink what economics should be taught, acknowledging disconnects between classroom content and real world events. We introduce a learning theory approach that is broader, one that goes beyond such context specific discussions of foundational knowledge and application (i.e., teaching about this specific crisis and provide a framework to address the broader issue of how teaching practices can, by their very nature, minimize such disconnects and provide more effective processes for teaching about current economic conditions. The theory of significant learning (Fink 2003 is presented as a model of how experiences can be used to develop a deep approach to learning, learning that lasts. Experiential learning pedagogies are timeless in that they can be readily modified to promote deeper understanding over a wide range of economic environments. Focusing on one category of significant learning, the human dimension, and one component of the financial crisis, unemployment, examples which modify existing experiential learning practices are described to demonstrate how such pedagogic practices can be readily adapted to teaching and learning about current economic conditions. In short, we demonstrate that incorporating student experiences into pedagogic practice provides a natural alignment of teaching content and real world events, regardless of how those change over time.

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

OpenAIRE

Phillips, Steven; Wilson, William H.

2010-01-01

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the P...

Category I structures program

International Nuclear Information System (INIS)

Endebrock, E.G.; Dove, R.C.

1981-01-01

The objective of the Category I Structure Program is to supply experimental and analytical information needed to assess the structural capacity of Category I structures (excluding the reactor cntainment building). Because the shear wall is a principal element of a Category I structure, and because relatively little experimental information is available on the shear walls, it was selected as the test element for the experimental program. The large load capacities of shear walls in Category I structures dictates that the experimental tests be conducted on small size shear wall structures that incorporates the general construction details and characteristics of as-built shear walls

Drug utilization and teratogenicity risk categories during pregnancy.

Science.gov (United States)

Basgül, Alin; Akici, Ahmet; Uzuner, Arzu; Kalaça, Sibel; Kavak, Zehra N; Tural, Alper; Oktay, Sule

2007-01-01

A limited number of studies have investigated in detail the use of drugs during pregnancy. Researchers in the present study investigated the details of drug utilization in pregnant women during the month before pregnancy, at the time that they became aware of the pregnancy, and during the first trimester. Face-to-face interviews were conducted with 359 pregnant women who were admitted to the fetal medicine unit at a university hospital for diagnosis and follow-up. A questionnaire was used to document sociodemographic characteristics and details of drug use. Drugs were categorized according to the US Food and Drug Administration fetal risk classification. Mean maternal age was 29.9+/-5.1 y, and mean gestational age was 19.6+/-9.5 wk. Many of the pregnant women studied (46.6%) were university graduates, and most (61.9%) had a relatively high annual income. Mean gestational age when participants first learned of their pregnancy was 39.8+/-16.4 d. One hundred seventeen participants (32.6%) used drugs during the month before conception, 54 (15%) at the time when they learned of their pregnancy, 180 (50.1%) at the time of the interview, and 289 (80.5%) during the first trimester. The percentages of drugs in categories D and X used by these subjects were 14%, 13.5%, 2.9%, and 5.9%, respectively. Most of the drugs were hormones. The total rate of drug utilization was not high before and during the first trimester of pregnancy. A considerable number of women were using drugs from the D and X categories; however, these numbers decreased significantly when women learned of their pregnancies. Intake of folic acid, vitamins, and iron was very low during the preconception period and was not high enough during the first trimester; this suggests that particular attention should be paid to the use of beneficial "safe" drugs during the preconception and early pregnancy periods.

An impoverished machine: challenges to human learning and instructional technology.

Science.gov (United States)

Taraban, Roman

2008-08-01

Many of the limitations to human learning and processing identified by cognitive psychologists over the last 50 years still hold true, including computational constraints, low learning rates, and unreliable processing. Instructional technology can be used in classrooms and in other learning contexts to address these limitations to learning. However, creating technological innovations is not enough. As part of psychological science, the development and assessment of instructional systems should be guided by theories and practices within the discipline. The technology we develop should become an object of research like other phenomena that are studied. In the present article, I present an informal account of my own work in assessing instructional technology for engineering thermodynamics to show not only the benefits, but also the limitations, in studying the technology we create. I conclude by considering several ways of advancing the development of instructional technology within the SCiP community, including interdisciplinary research and envisioning learning contexts that differ radically from traditional learning focused on lectures and testing.

[Eating, nourishment and nutrition: instrumental analytic categories in the scientific research field].

Science.gov (United States)

da Veiga Soares Carvalho, Maria Cláudia; Luz, Madel Therezinha; Prado, Shirley Donizete

2011-01-01

Eating, nourishment or nutrition circulate in our culture as synonyms and thus do not account for the changes that occur in nourishment, which intended or unintended, have a hybridization pattern that represents a change of rules and food preferences. This paper aims to take these common sense conceptions as analytic categories for analyzing and interpreting research for the Humanities and Health Sciences in a theoretical perspective, through conceptualization. The food is associated with a natural function (biological), a concept in which nature is opposed to culture, and nourishment takes cultural meanings (symbolic), expressing the division of labor, wealth, and a historical and cultural creation through which one can study a society. One attributes to Nutrition a sense of rational action, derived from the constitution of this science in modernity, inserted in a historical process of scientific rationalization of eating and nourishing. We believe that through the practice of conceptualization in interdisciplinary research, which involves a shared space of knowledge, we can be less constrained by a unified theoretical model of learning and be freer to think about life issues.

Evolution of social learning does not explain the origin of human cumulative culture.

Science.gov (United States)

Enquist, Magnus; Ghirlanda, Stefano

2007-05-07

Because culture requires transmission of information between individuals, thinking about the origin of culture has mainly focused on the genetic evolution of abilities for social learning. Current theory considers how social learning affects the adaptiveness of a single cultural trait, yet human culture consists of the accumulation of very many traits. Here we introduce a new modeling strategy that tracks the adaptive value of many cultural traits, showing that genetic evolution favors only limited social learning owing to the accumulation of maladaptive as well as adaptive culture. We further show that culture can be adaptive, and refined social learning can evolve, if individuals can identify and discard maladaptive culture. This suggests that the evolution of such "adaptive filtering" mechanisms may have been crucial for the birth of human culture.

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

Science.gov (United States)

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance. PMID:26422143

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Science.gov (United States)

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Directory of Open Access Journals (Sweden)

Joachim de Greeff

Full Text Available Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference; the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

Cross-cultural Comparison of Learning in Human Hunting : Implications for Life History Evolution.

Science.gov (United States)

MacDonald, Katharine

2007-12-01

This paper is a cross-cultural examination of the development of hunting skills and the implications for the debate on the role of learning in the evolution of human life history patterns. While life history theory has proven to be a powerful tool for understanding the evolution of the human life course, other schools, such as cultural transmission and social learning theory, also provide theoretical insights. These disparate theories are reviewed, and alternative and exclusive predictions are identified. This study of cross-cultural regularities in how children learn hunting skills, based on the ethnographic literature on traditional hunters, complements existing empirical work and highlights future areas for investigation.

Social learning solves the problem of narrow-peaked search landscapes : experimental evidence in humans

NARCIS (Netherlands)

Acerbi, A.; Tennie, C.; Mesoudi, A.

2016-01-01

The extensive use of social learning is considered a major reason for the ecological success of humans. Theoretical considerations, models and experiments have explored the evolutionary basis of social learning, showing the conditions under which learning from others is more adaptive than individual

Previous experience in manned space flight: A survey of human factors lessons learned

Science.gov (United States)

Chandlee, George O.; Woolford, Barbara

1993-01-01

Previous experience in manned space flight programs can be used to compile a data base of human factors lessons learned for the purpose of developing aids in the future design of inhabited spacecraft. The objectives are to gather information available from relevant sources, to develop a taxonomy of human factors data, and to produce a data base that can be used in the future for those people involved in the design of manned spacecraft operations. A study is currently underway at the Johnson Space Center with the objective of compiling, classifying, and summarizing relevant human factors data bearing on the lessons learned from previous manned space flights. The research reported defines sources of data, methods for collection, and proposes a classification for human factors data that may be a model for other human factors disciplines.

Human Cadavers vs. Multimedia Simulation: A Study of Student Learning in Anatomy

Science.gov (United States)

Saltarelli, Andrew J.; Roseth, Cary J.; Saltarelli, William A.

2014-01-01

Multimedia and simulation programs are increasingly being used for anatomy instruction, yet it remains unclear how learning with these technologies compares with learning with actual human cadavers. Using a multilevel, quasi-experimental-control design, this study compared the effects of "Anatomy and Physiology Revealed" (APR) multimedia…

Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain.

Science.gov (United States)

Niv, Yael; Edlund, Jeffrey A; Dayan, Peter; O'Doherty, John P

2012-01-11

Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric-psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms.

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

Energy Technology Data Exchange (ETDEWEB)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

2017-07-14

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

Economic analysis of pandemic influenza mitigation strategies for five pandemic severity categories

Science.gov (United States)

2013-01-01

Background The threat of emergence of a human-to-human transmissible strain of highly pathogenic influenza A(H5N1) is very real, and is reinforced by recent results showing that genetically modified A(H5N1) may be readily transmitted between ferrets. Public health authorities are hesitant in introducing social distancing interventions due to societal disruption and productivity losses. This study estimates the effectiveness and total cost (from a societal perspective, with a lifespan time horizon) of a comprehensive range of social distancing and antiviral drug strategies, under a range of pandemic severity categories. Methods An economic analysis was conducted using a simulation model of a community of ~30,000 in Australia. Data from the 2009 pandemic was used to derive relationships between the Case Fatality Rate (CFR) and hospitalization rates for each of five pandemic severity categories, with CFR ranging from 0.1% to 2.5%. Results For a pandemic with basic reproduction number R0 = 1.8, adopting no interventions resulted in total costs ranging from $441 per person for a pandemic at category 1 (CFR 0.1%) to $8,550 per person at category 5 (CFR 2.5%). For severe pandemics of category 3 (CFR 0.75%) and greater, a strategy combining antiviral treatment and prophylaxis, extended school closure and community contact reduction resulted in the lowest total cost of any strategy, costing $1,584 per person at category 5. This strategy was highly effective, reducing the attack rate to 5%. With low severity pandemics costs are dominated by productivity losses due to illness and social distancing interventions, whereas higher severity pandemic costs are dominated by healthcare costs and costs arising from productivity losses due to death. Conclusions For pandemics in high severity categories the strategies with the lowest total cost to society involve rigorous, sustained social distancing, which are considered unacceptable for low severity pandemics due to societal

Transfer between local and global processing levels by pigeons (Columba livia) and humans (Homo sapiens) in exemplar- and rule-based categorization tasks.

Science.gov (United States)

Aust, Ulrike; Braunöder, Elisabeth

2015-02-01

The present experiment investigated pigeons' and humans' processing styles-local or global-in an exemplar-based visual categorization task in which category membership of every stimulus had to be learned individually, and in a rule-based task in which category membership was defined by a perceptual rule. Group Intact was trained with the original pictures (providing both intact local and global information), Group Scrambled was trained with scrambled versions of the same pictures (impairing global information), and Group Blurred was trained with blurred versions (impairing local information). Subsequently, all subjects were tested for transfer to the 2 untrained presentation modes. Humans outperformed pigeons regarding learning speed and accuracy as well as transfer performance and showed good learning irrespective of group assignment, whereas the pigeons of Group Blurred needed longer to learn the training tasks than the pigeons of Groups Intact and Scrambled. Also, whereas humans generalized equally well to any novel presentation mode, pigeons' transfer from and to blurred stimuli was impaired. Both species showed faster learning and, for the most part, better transfer in the rule-based than in the exemplar-based task, but there was no evidence of the used processing mode depending on the type of task (exemplar- or rule-based). Whereas pigeons relied on local information throughout, humans did not show a preference for either processing level. Additional tests with grayscale versions of the training stimuli, with versions that were both blurred and scrambled, and with novel instances of the rule-based task confirmed and further extended these findings. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Macaque monkeys can learn token values from human models through vicarious reward.

Science.gov (United States)

Bevacqua, Sara; Cerasti, Erika; Falcone, Rossella; Cervelloni, Milena; Brunamonti, Emiliano; Ferraina, Stefano; Genovesio, Aldo

2013-01-01

Monkeys can learn the symbolic meaning of tokens, and exchange them to get a reward. Monkeys can also learn the symbolic value of a token by observing conspecifics but it is not clear if they can learn passively by observing other actors, e.g., humans. To answer this question, we tested two monkeys in a token exchange paradigm in three experiments. Monkeys learned token values through observation of human models exchanging them. We used, after a phase of object familiarization, different sets of tokens. One token of each set was rewarded with a bit of apple. Other tokens had zero value (neutral tokens). Each token was presented only in one set. During the observation phase, monkeys watched the human model exchange tokens and watched them consume rewards (vicarious rewards). In the test phase, the monkeys were asked to exchange one of the tokens for food reward. Sets of three tokens were used in the first experiment and sets of two tokens were used in the second and third experiments. The valuable token was presented with different probabilities in the observation phase during the first and second experiments in which the monkeys exchanged the valuable token more frequently than any of the neutral tokens. The third experiments examined the effect of unequal probabilities. Our results support the view that monkeys can learn from non-conspecific actors through vicarious reward, even a symbolic task like the token-exchange task.

Learning from video modeling examples : Effects of seeing the human model's face

NARCIS (Netherlands)

Van Gog, Tamara; Verveer, Ilse; Verveer, Lise

2014-01-01

Video modeling examples in which a human(-like) model shows learners how to perform a task are increasingly used in education, as they have become very easy to create and distribute in e-learning environments. However, little is known about design guidelines to optimize learning from video modeling

Auditory working memory predicts individual differences in absolute pitch learning.

Science.gov (United States)

Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

2015-07-01

Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

Phonetic diversity, statistical learning, and acquisition of phonology.

Science.gov (United States)

Pierrehumbert, Janet B

2003-01-01

In learning to perceive and produce speech, children master complex language-specific patterns. Daunting language-specific variation is found both in the segmental domain and in the domain of prosody and intonation. This article reviews the challenges posed by results in phonetic typology and sociolinguistics for the theory of language acquisition. It argues that categories are initiated bottom-up from statistical modes in use of the phonetic space, and sketches how exemplar theory can be used to model the updating of categories once they are initiated. It also argues that bottom-up initiation of categories is successful thanks to the perception-production loop operating in the speech community. The behavior of this loop means that the superficial statistical properties of speech available to the infant indirectly reflect the contrastiveness and discriminability of categories in the adult grammar. The article also argues that the developing system is refined using internal feedback from type statistics over the lexicon, once the lexicon is well-developed. The application of type statistics to a system initiated with surface statistics does not cause a fundamental reorganization of the system. Instead, it exploits confluences across levels of representation which characterize human language and make bootstrapping possible.

Organizational Learning: Some Basic Questions and Answers

Directory of Open Access Journals (Sweden)

Miran Mihelčič

2014-05-01

Full Text Available The term “organizational learning” raises a broad range of questions, specifically with regard to its contents. Following the thoughts of eminent philosophers, such as Aristotle and Confucius, the contribution of scientists in any research field to the corpus of human knowledge should also be based on the proper governing of the use of language. Therefore it is, first, of serious importance to be aware that organizational learning is just one dimension or element of the learning organization and not vice versa; second, a good comprehension of basic categories related to the organizational side of (formal social units’ functioning is an imperative part of organizational learning process. In writing this paper, the author started from his experiences acquired in his role as a lecturer on the subject “Theory of Organization”, in which the goal of lecturing was explained to students as gaining knowledge about cooperation and competition of people in the entities of rational production of goods. To generalize the presented questions and answers regarding the use of term “organization” in the field of management, certain similarities and comparisons were sought and found in other fields of science and, more generally, in life itself. After more detailed explanations of other relevant categories for the organizational learning process, the process itself is defined by its goals and steps where the overlapping of the learning process with the organizational change process and the process of increasing organizational capital is shown. Finally, it is also emphasized that the idea of improving internal relationships – as the substance of organization – between employees in a formal social unit through organizational learning could and should be exploited in external relationships between formal social units.

Evaluation of Machine Learning Methods for LHC Optics Measurements and Corrections Software

CERN Document Server

AUTHOR|(CDS)2206853; Henning, Peter

The field of artificial intelligence is driven by the goal to provide machines with human-like intelligence. However modern science is currently facing problems with high complexity that cannot be solved by humans in the same timescale as by machines. Therefore there is a demand on automation of complex tasks. To identify the category of tasks which can be performed by machines in the domain of optics measurements and correction on the Large Hadron Collider (LHC) is one of the central research subjects of this thesis. The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In High Energy Physics these concepts are mostly used in offline analysis of experiments data and to perform regression tasks. In Accelerator Physics the machine learning approach has not found a wide application yet. Therefore potential tasks for machine learning solutions can be specified in this domain. The appropriate methods and their suitability for...

More than one kind of inference: re-examining what's learned in feature inference and classification.

Science.gov (United States)

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Case of rhesus antigen weak D type 4.2. (DAR category detection

Directory of Open Access Journals (Sweden)

L. L. Golovkina

2015-01-01

Full Text Available Serological methods of Rhesus antigens identification in humans cannot identify D-antigen variants. In this article the serological characteristics of Rhesus antigen D weak type 4.2. (Category DAR are described.

Language categories in Russian morphology

OpenAIRE

زهرایی زهرایی

2009-01-01

When studying Russian morphology, one can distinguish two categories. These categories are “grammatical” and “lexico-grammatical”. Grammatical categories can be specified through a series of grammatical features of words. Considering different criteria, Russian grammarians and linguists divide grammatical categories of their language into different types. In determining lexico-grammatical types, in addition to a series of grammatical features, they also consider a series of lexico-semantic fe...

Non-Formal Learning: Clarification of the Concept and Its Application in Music Learning

Science.gov (United States)

Mok, On Nei Annie

2011-01-01

The concept of non-formal learning, which falls outside the categories of informal and formal learning, has not been as widely discussed, especially in the music education literature. In order to bridge this gap and to provide supplementary framework to the discussion of informal and formal learning, therefore, this paper will first summarize…

The Virtual Teacher (VT) Paradigm: Learning New Patterns of Interpersonal Coordination Using the Human Dynamic Clamp.

Science.gov (United States)

Kostrubiec, Viviane; Dumas, Guillaume; Zanone, Pier-Giorgio; Kelso, J A Scott

2015-01-01

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities.

Waist-High and Knee-Deep: Humane Learning beyond Polemics and Precincts

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Higgins, Chris

2015-01-01

In this essay, Chris Higgins sets out to disentangle the tradition of humane learning from contemporary distinctions and debates. The first section demonstrates how a bloated and incoherent "humanism" now functions primarily as a talisman or a target, that is, as a prompt to choose sides. It closes with the image of Doris Salcedo's…

Organizational Categories as Viewing Categories

OpenAIRE

Mik-Meyer, Nanna

2005-01-01

This paper explores how two Danish rehabilitation organizations textual guidelines for assessment of clients’ personality traits influence the actual evaluation of clients. The analysis will show how staff members produce institutional identities corresponding to organizational categories, which very often have little or no relevance for the clients evaluated. The goal of the article is to demonstrate how the institutional complex that frames the work of the organizations produces the client ...

Focal colors across languages are representative members of color categories.

Science.gov (United States)

Abbott, Joshua T; Griffiths, Thomas L; Regier, Terry

2016-10-04

Focal colors, or best examples of color terms, have traditionally been viewed as either the underlying source of cross-language color-naming universals or derived from category boundaries that vary widely across languages. Existing data partially support and partially challenge each of these views. Here, we advance a position that synthesizes aspects of these two traditionally opposed positions and accounts for existing data. We do so by linking this debate to more general principles. We show that best examples of named color categories across 112 languages are well-predicted from category extensions by a statistical model of how representative a sample is of a distribution, independently shown to account for patterns of human inference. This model accounts for both universal tendencies and variation in focal colors across languages. We conclude that categorization in the contested semantic domain of color may be governed by principles that apply more broadly in cognition and that these principles clarify the interplay of universal and language-specific forces in color naming.

How does the brain rapidly learn and reorganize view-invariant and position-invariant object representations in the inferotemporal cortex?

Science.gov (United States)

Cao, Yongqiang; Grossberg, Stephen; Markowitz, Jeffrey

2011-12-01

All primates depend for their survival on being able to rapidly learn about and recognize objects. Objects may be visually detected at multiple positions, sizes, and viewpoints. How does the brain rapidly learn and recognize objects while scanning a scene with eye movements, without causing a combinatorial explosion in the number of cells that are needed? How does the brain avoid the problem of erroneously classifying parts of different objects together at the same or different positions in a visual scene? In monkeys and humans, a key area for such invariant object category learning and recognition is the inferotemporal cortex (IT). A neural model is proposed to explain how spatial and object attention coordinate the ability of IT to learn invariant category representations of objects that are seen at multiple positions, sizes, and viewpoints. The model clarifies how interactions within a hierarchy of processing stages in the visual brain accomplish this. These stages include the retina, lateral geniculate nucleus, and cortical areas V1, V2, V4, and IT in the brain's What cortical stream, as they interact with spatial attention processes within the parietal cortex of the Where cortical stream. The model builds upon the ARTSCAN model, which proposed how view-invariant object representations are generated. The positional ARTSCAN (pARTSCAN) model proposes how the following additional processes in the What cortical processing stream also enable position-invariant object representations to be learned: IT cells with persistent activity, and a combination of normalizing object category competition and a view-to-object learning law which together ensure that unambiguous views have a larger effect on object recognition than ambiguous views. The model explains how such invariant learning can be fooled when monkeys, or other primates, are presented with an object that is swapped with another object during eye movements to foveate the original object. The swapping procedure is

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