Error discounting in probabilistic category learning.

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 discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results were indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Event category learning.

This research investigated the learning of event categories, in particular, categories of simple animated events, each involving a causal interaction between 2 characters. Four experiments examined whether correlations among attributes of events are easier to learn when they form part of a rich correlational structure than when they are independent of other correlations. Event attributes (e.g., state change, path of motion) were chosen to reflect distinctions made by verbs. Participants were presented with an unsupervised learning task and were then tested on whether the organization of correlations affected learning. Correlations forming part of a system of correlations were found to be better learned than isolated correlations. This finding of facilitation from correlational structure is explained in terms of a model that generates internal feedback to adjust the salience of attributes. These experiments also provide evidence regarding the role of object information in events, suggesting that this role is mediated by object category representations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

ALCOVE: An exemplar-based connectionist model of category learning.

ALCOVE (attention learning covering map) is a connectionist model of category learning that incorporates an exemplar-based representation (D. L. Medin and M. M. Schaffer, 1978; R. M. Nosofsky, 1986) with error-driven learning (M. A. Gluck and G. H. Bower, 1988; D. E. Rumelhart et al, 1986). ALCOVE selectively attends to relevant stimulus dimensions, is sensitive to correlated dimensions, can account for a form of base-rate neglect, does not suffer catastrophic forgetting, and can exhibit 3-stage (U-shaped) learning of high-frequency exceptions to rules, whereas such effects are not easily accounted for by models using other combinations of representation and learning method. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Depression and category learning.

Explores the relationship between depression and category learning. It was predicted that depression would impair categorization performance on criterial-attribute tasks, which require systematic hypothesis testing, but not on family-resemblance tasks, which allow processing strategies that are likely preserved in depression. These predictions were confirmed in 2 experiments using different stimuli tasks, and S populations. Implications of the empirical connections among depression, the use of less sophisticated cognitive strategies, and category learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Unitization during category learning.

Five experiments explored the question of whether new perceptual units can be developed if they are useful for a category learning task, and if so, what the constraints on this unitization process are. During category learning, participants were required to attend either a single component or a conjunction of 5 components. Consistent with unitization, the conjunctive task became much easier with practice; this improvement was not found for the single-component task or for conjunctive tasks in which the components could not be unitized. Influences of component organization (Experiment 1), component contiguity (Experiment 2), component proximity (Experiment 3), and number of components (Experiment 4) on practice effects were found. Deconvolved response times (Experiment 5) showed that prolonged practice yielded faster responses than predicted by an analytic model that integrates evidence from independently perceived components. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Blocking in category learning.

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

From conditioning to category learning: An adaptive network model.

We used adaptive network theory to extend the Rescorla-Wagner (1972) least mean squares (LMS) model of associative learning to phenomena of human learning and judgment. In three experiments subjects learned to categorize hypothetical patients with particular symptom patterns as having certain diseases. When one disease is far more likely than another, the model predicts that subjects will substantially overestimate the diagnosticity of the more valid symptom for the rare disease. The results of Experiments 1 and 2 provide clear support for this prediction in contradistinction to predictions from probability matching, exemplar retrieval, or simple prototype learning models. Experiment 3 contrasted the adaptive network model with one predicting pattern-probability matching when patients always had four symptoms (chosen from four opponent pairs) rather than the presence or absence of each of four symptoms, as in Experiment 1. The results again support the Rescorla-Wagner LMS learning rule as embedded within an adaptive network model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Category use and category learning.

Categorization models based on laboratory research focus on a narrower range of explanatory constructs than appears necessary for explaining the structure of natural categories. This mismatch is caused by the reliance on classification as the basis of laboratory studies. Category representations are formed in the process of interacting with category members. Thus, laboratory studies must explore a range of category uses. The authors review the effects of a variety of category uses on category learning. First, there is an extensive discussion contrasting classification with a predictive inference task that is formally equivalent to classification but leads to a very different pattern of learning. Then, research on the effects of problem solving, communication, and combining inference and classification is reviewed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Base rates in category learning.

Previous researchers have discovered perplexing inconsistencies in how people appear to utilize category base rates when making category judgments. In particular, D. L. Medin and S. M. Edelson (see record 1988-31640-001) found an inverse base-rate effect, in which participants tended to select a rare category when tested with a combination of conflicting cues, and M. A. Gluck and G. H. Bower (see record 1989-00340-001) reported apparent base-rate neglect, in which participants tended to select a rare category when tested with a single symptom for which objective diagnosticity was equal for all categories. This article suggests that common principles underlie both effects. First, base-rate information is learned and consistently applied to all training and testing cases. Second, the crucial effect of base rates is to cause frequent categories to be learned before rare categories so that the frequent categories are encoded by their typical features and the rare categories are encoded by their distinctive features. Four new experiments provide evidence consistent with those principles. The principles are formalized in a new connectionist model that can rapidly shift attention to distinctive features. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A connectionist account of asymmetric category learning in early infancy.

Young infants show unexplained asymmetries in the exclusivity of categories formed on the basis of visually presented stimuli. A connectionist model is described that shows similar exclusivity asymmetries when categorizing the same stimuli presented to infants. The asymmetries can be explained in terms of an associative learning mechanism, distributed internal representations, and the statistics of the feature distributions in the stimuli. The model was used to explore the robustness of this asymmetry. The model predicts that the asymmetry will persist when a category is acquired in the presence of mixed category exemplars. An experiment with 3–4-month-olds showed that asymmetric exclusivity persisted in the presence of mixed-exemplar familiarization, thereby confirming the model's prediction. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A probabilistic contrast model of causal induction.

Deviations from the predictions of covariational models of causal attribution have often been reported in the literature. These include a bias against using a consensus information, a bias toward attributing effects to a person, and a tendency to make a variety of unpredicted conjunctive attributions. It is contended that these deviations, rather than representing irrational biases, could be due to (a) unspecified information over which causal inferences are computed and (b) the questionable normativeness of the models against which these deviations have been measured. A probabilistic extension of Kelley's analysis-of-variance analogy is proposed. An experiment was performed to assess the above biases and evaluate the proposed model against competing ones. The results indicate that the inference process is unbiased. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparison-based learning: Effects of comparing instances during category learning.

When learning about a category, people often compare new instances with similar old instances and notice features common to the compared instances. Five experiments demonstrate that such comparisons cause features common to compared instances to be considered more important for the category than equally frequent features that are not common to compared instances. Exp 1 shows that what is learned depends on which instances are compared. Exp 2 investigates the conditions under which comparison-based learning occurs. The next experiments find that these comparisons affect subjective feature frequency (Exp 3) and sensitivity to feature correlations (Exp 4). Exp 5 shows that comparisons during early learning affect what is learned from later instances. The discussion focuses on the implications for models of category representation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Alignment and category learning

Recent research shows that similarity comparisons involve an alignment process in which features are placed into correspondence. In 6 studies, the authors showed that alignment is involved in category learning as well. Within a category, aligned matches (feature matches occurring on the same dimension) facilitate learning more than nonaligned matches do (matches on different dimensions), although nonaligned matches still facilitate learning relative to nonmatches. Analogously, feature matches that cross category boundaries hurt learning more if they occur on the same versus a different dimension, and cross-category feature matches on different dimensions hurt learning relative to nonmatching features. Representational assumptions of category learning models must be modified to account for the differences between aligned and nonaligned feature matches.     

Prototypes in category learning: The effects of category size, category structure, and stimulus complexity.

Although research in categorization has sometimes been motivated by prototype theory, recent studies have favored exemplar theory. However, some of these studies focused on small, poorly differentiated categories composed of simple, 4-dimensional stimuli. Some analyzed the aggregate data of entire groups. Some compared powerful multiplicative exemplar models to less powerful additive prototype models. Here, comparable prototype and exemplar models were fit to individual-participant data in 4 experiments that sampled category sets varying in size, level of category structure, and stimulus complexity (dimensionality). The prototype model always fit the observed data better than the exemplar model did. Prototype-based processes seemed especially relevant when participants learned categories that were larger or contained more complex stimuli. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Two competing attentional mechanisms in category learning.

This research provides evidence that there are 2 competing attentional mechanisms in category learning. Attentional persistence directs attention to attributes previously found to be predictive, whereas attentional contrast directs attention to attribute values that have not already been associated with a category. Three experiments provided evidence for these mechanisms. Experiments 1 and 2 provided evidence for persistence because increased attention to an attribute followed training in which that attribute was relevant. These experiments also provided evidence for contrast because attention was also increased to the values of an attribute when the values of another, more salient attribute had already been associated with categories. Experiment 3 provided evidence that persistence operates primarily at the level of attributes, whereas contrast operates at the level of attribute values. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Contrast and accentuation effects in category learning.

This study examines the accentuation of perceived intercategory differences. In Experiment 1, 2 sets of trait adjectives were presented, a neutral set and a set of either favorable traits or unfavorable traits. Ss estimated the mean favorability of each set. The mean favorability of the neutral set was then increased or decreased by adding new traits. As predicted, the estimated mean favorability of the neutral set changed more when the set became more distinct from a contextual set than when it became more similar. In Experiment 2, estimated category means were displaced away from each other (contrast effect), and they moved even farther apart when new information increased the variability of trait favorability (accentuation effect). This change was illusory because the actual category means remained constant. Experiment 3, in which trait adjectives described members of 2 novel groups, replicated Experiment 2. The relevance of contrast and accentuation effects to the development and maintenance of differentiated intergroup perceptions is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Impairment of probabilistic reward-based learning in schizophrenia.

Recent models assume that some symptoms of schizophrenia originate from defective reward processing mechanisms. Understanding the precise nature of reward-based learning impairments might thus make an important contribution to the understanding of schizophrenia and the development of treatment strategies. The present study investigated several features of probabilistic reward-based stimulus association learning, namely the acquisition of initial contingencies, reversal learning, generalization abilities, and the effects of reward magnitude. Compared to healthy controls, individuals with schizophrenia exhibited attenuated overall performance during acquisition, whereas learning rates across blocks were similar to the rates of controls. On the group level, persons with schizophrenia were, however, unable to learn the reversal of the initial reward contingencies. Exploratory analysis of only the subgroup of individuals with schizophrenia who showed significant learning during acquisition yielded deficits in reversal learning with low reward magnitudes only. There was further evidence of a mild generalization impairment of the persons with schizophrenia in an acquired equivalence task. In summary, although there was evidence of intact basic processing of reward magnitudes, individuals with schizophrenia were impaired at using this feedback for the adaptive guidance of behavior. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Memory storage and retrieval processes in category learning.

Studied the detailed course of learning for categorization tasks defined by independent or contingent probability distributions over the features of category exemplars in 2 experiments with 72 college-age Ss. Ss viewed sequences of bar charts that simulated symptom patterns and responded to each chart with a recognition and a categorization judgment. Fuzzy, probabilistically defined categories were learned relatively rapidly when individual features were correlated with category assignment, more slowly when only patterns carried category information. Limits of performance were suboptimal, evidently because of capacity limitations on judgmental processes as well as limitations on memory. Categorization proved systematically related to feature and exemplar probabilities, under different circumstances, and to similarity among exemplars of categories. Unique retrieval cues for exemplar patterns facilitated recognition but entered into categorization only at retention intervals within the range of short-term memory. Findings are interpreted within the framework of a general array model that yields both exemplar-similarity and feature-frequency models as special cases and provides quantitative accounts of the course of learning in each of the categorization tasks studied. (46 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)