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)     

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)     

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)     

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)     

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.     

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)     

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)     

A model of probabilistic category learning.

A new connectionist model (named RASHNL) accounts for many "irrational" phenomena found in nonmetric multiple-cue probability learning, wherein people learn to utilize a number of discrete-valued cues that are partially valid indicators of categorical outcomes. Phenomena accounted for include cue competition, effects of cue salience, utilization of configural information, decreased learning when information is introduced after a delay, and effects of base rates. Exps 1 and 2 replicate previous experiments on cue competition and cue salience, and fits of the model provide parameter values for making qualitatively correct predictions for many other situations. The model also makes 2 new predictions, confirmed in Exps 3 and 4. The model formalizes 3 explanatory principles: rapidly shifting attention with learned shifts, decreasing learning rates, and graded similarity in exemplar representation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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)     

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)     

Combining exemplar-based category representations and connectionist learning rules.

Adaptive network and exemplar-similarity models were compared on their ability to predict category learning and transfer data. An exemplar-based network (J. K. Kruschke, 1990, 1992) that combines key aspects of both modeling approaches was also tested. The exemplar-based network incorporates an exemplar-based category representation in which exemplars become associated to categories through the same error-driven, interactive learning rules that are assumed in standard adaptive networks. Exp 1, which partially replicated and extended the probabilistic classification learning paradigm of M. A. Gluck and G. H. Bower (1988), demonstrated the importance of an error-driven learning rule. Exp 2, which extended the classification learning paradigm of D. L. Medin and M. M. Schaffer (1978) that discriminated between exemplar and prototype models, demonstrated the importance of an exemplar-based category representation. Only the exemplar-based network accounted for all the major qualitative phenomena; it also achieved good quantitative predictions of the learning and transfer data in both experiments. (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)     

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)     

Rules and exemplars in category learning

Psychological theories of categorization generally focus on either rule- or exemplar-based explanations. We present 2 experiments that show evidence of both rule induction and exemplar encoding as well as a connectionist model, ATRIUM, that specifies a mechanism for combining rule- and exemplar-based representation. In 2 experiments participants learned to classify items, most of which followed a simple rule, although there were a few frequently occurring exceptions. Experiment 1 examined how people extrapolate beyond the range of training. Experiment 2 examined the effect of instance frequency on generalization. Categorization behavior was well described by the model, in which exemplar representation is used for both rule and exception processing. A key element in correctly modeling these results was capturing the interaction between the rule- and exemplar-based representations by using shifts of attention between rules and exemplars.     

Retrieval of exemplar and feature information in category learning.

Two assumptions were tested regarding what information Ss use during category learning with independent features. One assumption is that Ss use only exemplar information and the other is that they use only feature information. Previous work has revealed no clear superiority of one over the other after training with independent features (W. K. Estes, see PA, Vol 74:334 and Vol 73:21175). The experiments presented here manipulate the opportunity for using whole exemplars when categorizing test patterns by providing either whole or fragmented training patterns. The results show that a feature-node network model was superior to feature-frequency and exemplar models at predicting asymptotic test performance after fragmented-pattern training. However, to achieve this result, all models were modified to account for possible attentional differences among the training conditions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning.

The authors contrast exemplar-based and prototype-based processes in dot-pattern categorization. In Experiments 1A and 1B, participants provided similarity ratings of dot-distortion pairs that were distortions of the same originating prototype. The results show that comparisons to training exemplars surrounding the prototype create flat typicality gradients within a category and small prototype-enhancement effects, whereas comparisons to a prototype center create steep typicality gradients within a category and large prototype-enhancement effects. Thus, prototype and exemplar theories make different predictions regarding common versions of the dot-distortion task. Experiment 2 tested these different predictions by having participants learn dot-pattern categories. The steep typicality gradients, the large prototype effects, and the superior fit of prototype models suggest that participants refer to-be-categorized items to a representation near the category's center (the prototype), and not to the training exemplars that surround the prototype. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Learning mode and exemplar sequencing in unsupervised category learning.

Exemplar sequencing effects in incidental and intentional unsupervised category learning were investigated to illuminate how people form categories without an external teacher. Stimuli were perfectly separable into 2 categories based on 1 of 2 dimensions of variation. Sequencing of the first 20 training stimuli was manipulated. In the blocked condition, 10 Category A stimuli were followed by 10 Category B stimuli. In the intermixed condition, these 20 stimuli were ordered randomly. Experiment 1 revealed an interaction between learning mode and sequence, with better intentional learning for intermixed sequences but better incidental learning for blocked sequences. Experiment 2 showed that manipulating trial-to-trial variability along each dimension can impact intentional learning. Training sequences that emphasized variation along the category-relevant dimension resulted in better performance than sequences that emphasized variation along the category-irrelevant dimension. The results suggest that unsupervised category learning is influenced by the mode of learning and the order and nature of encountered exemplars. (PsycINFO Database Record (c) 2010 APA, all rights reserved)