Thirty-Something Categorization Results Explained: Selective Attention, Eyetracking, and Models of Category Learning.

An eyetracking study testing D. L. Medin and M. M. Schaffer's (1978) 5-4 category structure was conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5-4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5-4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates' eye movements while the students learned the 5-4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5-4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

SUSTAIN: A Network Model of Category Learning.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Category Representation for Classification and Feature Inference.

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas classification learning encourages exemplar representation. Experiment 1 supported this hypothesis. Averaged and individual participant data from transfer after inference training were better fit by a prototype than by an exemplar model. However, Experiment 2, with contrasting inference learning conditions, indicated that the prototype model was mimicking a set of label-based bidirectional rules, as determined by the inference learning task demands in Experiment 1. Only the set of rules model accounted for all the inference learning conditions in these experiments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Speeded Classification in a Probabilistic Category Structure: Contrasting Exemplar-Retrieval, Decision-Boundary, and Prototype Models.

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Diagnosticity and Prototypicality in Category Learning: A Comparison of Inference Learning and Classification Learning.

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning, classification and inference learning, in terms of sensitivity to diagnostic and prototypical information. Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic, but prototypical, features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic, prototypical, features. Discussion focuses on aspects of the 2 learning tasks that might lead to this differential sensitivity and the implications for learning real-world categories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exemplar effects in the context of a categorization rule: Featural and holistic influences.

Brooks and colleagues (S. W. Allen & L. R. Brooks, 1991; G. Regehr & L. R. Brooks, 1993) have shown that the classification of transfer stimuli is influenced by their similarity to training stimuli, even when a perfect classification rule is available. It is argued that the original effect obtained by Brooks and colleagues might have resulted from two potential confounding variables. Once these confounds were controlled, the current authors did not replicate Brooks and colleagues' results in Experiment 1. Exemplar effects appeared in Experiment 2 when transfer stimuli were perceptually more similar to training stimuli than in Experiment 1. In Experiment 3, the authors obtained exemplar effects with separated stimuli, a finding that was not predicted by Brooks and colleagues' model. The authors suggest that a close perceptual match between training and transfer stimuli is necessary for the effect to occur, for both integrated and separated stimuli. The nature of this perceptual match, holistic or featural, is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Attentional Allocation During the Perception of Scenes.

Semantic influences on attention during the 1st fixation on a scene were explored in 3 experiments. Subjects viewed briefly presented scenes; following scene presentation, a spatial probe was presented at the location of an object whose identity was consistent or inconsistent with the scene category. Responses to the probe served as an index of attention. The results of Experiment 1 suggest that within approximately 150 ms of scene onset, subjects attend preferentially to inconsistent objects. The results of Experiment 2, in which additional scene durations were used, confirm the presence of an inconsistent-object advantage that emerges within approximately 150 ms. Finally, the results of Experiment 3 demonstrate that the inconsistent-object advantage does not reflect strategic allocation of attention to likely probe locations. Implications of the results for scene perception and exploration are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Category Number Impacts Rule-Based but Not Information-Integration Category Learning: Further Evidence for Dissociable Category-Learning Systems.

Category number effects on rule-based and information-integration category learning were investigated. Category number affected accuracy and the distribution of best-fitting models in the rule-based task but had no effect on accuracy and little effect on the distribution of best-fining models in the information-integration task. In the 2 category conditions, rule-based learning was better than information-integration learning, whereas in the 4 category conditions, unidimensional and conjunctive rule-based learning was worse than information-integration learning. Rule-based strategies were used in the 2-category/rule-based condition, but about half of the observers used rule-based strategies in the 4-category unidimensional and conjunctive rule-based conditions. Information-integration strategies were used in the 4-category/ information-integration condition and by the end of training were used in the 2-category/information-integration condition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization.

J. D. Smith and colleagues (J. P. Minda & J. D. Smith, 2001; J. D. Smith & J. P. Minda, 1998, 2000; J. D. Smith, M. J. Murray, & J. P. Minda, 1997) presented evidence that they claimed challenged the predictions of exemplar models and that supported prototype models. In the authors' view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al's experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Knowledge Partitioning in Categorization: Constraints on Exemplar Models.

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Quantitative Model-Based Approach to Examining Aging Effects on Information-Integration Category Learning.

Information-integration category learning was examined in older and younger adults. Accuracy results indicated that older participants learned less well than younger participants in both linear and nonlinear conditions. Model-based analyses indicated that both groups in the linear condition tended to use information integration but that later in training younger participants were more likely to do so. In contrast, the 2 groups in the nonlinear condition were equally likely to use information integration. Further analysis indicated that younger adults were more accurate than older adults when an information-integration approach was adopted, whereas fewer age-related differences were observed when a rule-based approach was used, suggesting that age can have a negative impact on information-integration category learning processes but less impact on rule-based learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Inference and Classification Learning of Abstract Coherent Categories.

Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks.

In influential research, R. N. Shepard, C. I. Hovland, and H. M. Jenkins (1961) surveyed humans' categorization abilities using tasks based in rules, exclusive-or (XOR) relations, and exemplar memorization. Humans' performance was poorly predicted by cue-conditioning or stimulus-generalization theories, causing Shepard et al. to describe it in terms of hypothesis selection and rule application that were possibly supported by verbal mediation. The authors of the current article surveyed monkeys' categorization abilities similarly. Monkeys, like humans, found category tasks with a single relevant dimension the easiest and perceptually chaotic tasks requiring exemplar memorization the most difficult. Monkeys, unlike humans, found tasks based in XOR relations very difficult. The authors discuss the character and basis of the species difference in categorization and consider whether monkeys are the generalization-based cognitive system that humans are not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Dynamics of Learning and Allocation of Study Time to a Region of Proximal Learning.

In contrast to the dominant discrepancy reduction model, which favors the most difficult items, people, given free choice, devoted most time to medium-difficulty items and studied the easiest items first. When study time was experimentally manipulated, best performance resulted when most time was given to the medium-difficulty items. Empirically determined information uptake functions revealed steep initial learning for easy items with little subsequent increase. For medium-difficulty items, initial gains were smaller but more sustained, suggesting that the strategy people had used, when given free choice, was largely appropriate. On the basis of the information uptake functions, a negative spacing effect was predicted and observed in the final experiment. Overall, the results favored the region of proximal learning framework. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Overconfidence effects in category learning: A comparison of connectionist and exemplar memory models.

Exemplar and connectionist models were compared on their ability to predict overconfidence effects in category learning data. In the standard task, participants learned to classify hypothetical patients with particular symptom patterns into disease categories and reported confidence judgments in the form of probabilities. The connectionist model asserts that classifications and confidence are based on the strength of learned associations between symptoms and diseases. The exemplar retrieval model (ERM) proposes that people learn by storing examples and that their judgments are often based on the first example they happen to retrieve. Experiments 1 and 2 established that overconfidence increases when the classification step of the process is bypassed. Experiments 2 and 3 showed that a direct instruction to retrieve many exemplars reduces overconfidence. Only the ERM predicted the major qualitative phenomena exhibited in these experiments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling the Effects of Prior Knowledge on Learning Incongruent Features of Category Members.

The authors conducted 3 experiments addressing the issue of how observations and multiple sources of prior knowledge are put together in category learning. In Experiments 1 and 2, learning was faster for critical features, which were predictable on the basis of prior knowledge, than for filler features, and this advantage increased as more observations were made. In addition, learning was fastest for incongruent features that could only be predicted using knowledge from other domains. In Experiment 3, presenting contradictory features that violated prior knowledge led to rote learning rather than use of prior knowledge. The results were simulated with the Baywatch model, which addresses how observations of category members lead to recruitment and selection of sources of prior knowledge. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Privileged and Exemplar Resource: Traumatic Avoidance Learning and the Early Triumph of Mathematical Psychology.

The relationship between a classic 1953 study by R. L. Solomon and L. C. Wynne on traumatic avoidance learning and the pioneering efforts by Robert Bush and Frederick Mosteller and others to develop mathematical models of learning is analyzed. The main purpose is to explore how Bush and Mosteller disembedded a carefully selected set of Solomon and Wynne's data from its original context, which allowed something as seemingly humble as a set of numbers to become a widely available and valuable resource for the newly emerging field of mathematical learning theory (MLT). The creative use that the MLT community made of these data once Bush and Mosteller had systematically reduced the empirical and conceptual uncertainties within Solomon and Wynne's study is also discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Role of the Basal Ganglia in Category Learning: How Do Patients With Parkinson's Disease Learn?

The purpose of the present study was to gain a deeper understanding of the role of the basal ganglia in learning and memory by examining learning strategies among patients with basal ganglia dysfunction. Using a probabilistic category learning task (the "weather prediction" task) previously shown to be sensitive to basal ganglia function, the authors examined patterns of performance during learning and used mathematical models to capture different learning strategies. Results showed that patients with Parkinson's disease exhibit different patterns of strategy use. Specifically, most controls initially used a simple, but suboptimal, strategy that focused on single-cue-outcome associations; eventually, however, most controls adopted a more complex, optimal learning strategy, integrating single-cue associations to predict outcomes for multiple-cue stimuli. In contrast, the majority of individuals with Parkinson's disease continued to rely on simple single-cue learning strategies throughout the experiment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Role of the Locus Coeruleus in Mediating the Attentional Blink: A Neurocomputational Theory.

The attentional blink refers to the transient impairment in perceiving the 2nd of 2 targets presented in close temporal proximity. In this article, the authors propose a neurobiological mechanism for this effect. The authors extend a recently developed computational model of the potentiating influence of the locus coeruleus-norepinephrine system on information processing and hypothesize that a refractoriness in the function of this system may account for the attentional blink. The model accurately simulates the time course of the attentional blink, including Lag 1 sparing. The theory also offers an account of the close relationship of the attentional blink to the electrophysiological P3 component. The authors report results from two behavioral experiments that support a critical prediction of their theory regarding the time course of Lag 1 sparing. Finally, the relationship between the authors' neurocomputational theory and existing cognitive theories of the attentional blink is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)