Attention and learning processes in the identification and categorization of integral stimuli (original) (raw)

Attention and learning processes in the identification and categorization of integral stimuli

Journal of Experimental Psychology: Learning, Memory, and Cognition, 1987

The relationship between subjects' identification and categorization learning of integral-dimension stimuli was studied within the framework of an exemplar-based generalization model. The model was used to predict subjects' learning in six different categorization conditions on the basis of data obtained in a single identification learning condition. A crucial assumption in the model is that because of selective attention to component dimensions, similarity relations may change in systematic ways across different experimental contexts. The theoretical analysis provided evidence that, at least under unspeeded conditions, selective attention may play a critical role in determining the identification-categorization relationship for integral stimuli. Evidence was also provided that similarity among exemplars decreased as a function of identification learning. Various alternative classification models, including prototype, multiple-prototype, average distance, and "value-on-dimensions" models, were unable to account for the results. This article seeks to characterize performance relations between the two fundamental classification paradigms of identification and categorization. Whereas in an identification paradigm people identify stimuli as unique items (a one-to-one stimulus-response mapping), in a categorization paradigm people classify items into groups (a many-to-one stimulus-response mapping). The present study of the identification-categorization relationship is motivated by the recent "exemplar view" of categorization proposed by investigators such as Brooks (1978), Hintzman and Ludlam (1980), and Medin and Schaffer (1978). According to the exemplar view, people represent categories by storing individual category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. This view contrasts with some other approaches that assume that people form category "summary" representations such as a prototype or a rule. A suggestion that follows from the exemplar view is that there may be highly regular and systematic relations between identification and categorization performance. Presumably, when subjects learn to identify stimuli, a unique representation of each stimulus is stored in memory. Furthermore, the extent to which individual stimuli are confused during identification is This article is b~tsed on portions of a PhD dissertation submitted to Harvard University and on subsequent work conducted at Indiana University.

Further tests of an exemplar-similarity approach to relating identification and categorization

Perception & Psychophysics, 1989

Further tests were provided of an exemplar-similarity model for relating the identification and categorization of separable-dimension stimuli (Nosofsky, 1986). On the basis of confusion errors in an identification paradigm, a multidimensional scaling (MDS) solution was derived for a set of 16 separable-dimension stimuli. This MDS solution was then used in conjunction with the exemplar-similarity model to accurately predict performance in four separate categorization paradigms with the same stimuli. A key to achieving the accurate quantitative fits was the assumption that a selective attention process systematically modifies similarities among exemplars across different category structures. The tests reported go well beyond earlier ones (Nosofsky, 1986) in demonstrating the generalizability and utility of the theoretical approach. Implications of the results for alternative quantitative models of classification performance, including Ashby and Perrin's (1988) general recognition theory, were also considered.

Attention, similarity, and the identification-categorization relationship

The similarity choice model of identification (Lute, 1963; Shepard, 1957) and the context model of categorization (Medin & SchaNer, 1978; Nosofsky, 1986) are shown to be closely related to a variety of likelihood-based models. In particular, it is shown that: (1) for category distributions defined over independent dimensions, general versions of the context model and Estes' (1986) similarity-likelihood model are formally identical;

Rules and Exemplars in Categorization, Identification, and Recognition

Subjects learned to classify perceptual stimuli varying along continuous, separable dimensions into rule-described categories. The categories were designed to contrast the predictions of a selective-attention exemplar model and a simple rule-based model formalizing an economy-of-description view. Converging evidence about categorization strategies was obtained by also collecting identification and recognition data and by manipulating strategies via instructions. In free-strategy conditions, the exemplar model generally provided an accurate quantitative account of identification, categorization, and recognition performance, and it allowed for the interrelationship of these paradigms within a unified framework. Analyses of individual subject data also provided some evidence for the use of rules, but in general, the rules seemed to have a great deal in common with exemplar storage processes. Classification and recognition performance for subjects given explicit instructions to use specific rules contrasted dramatically with performance in the free-strategy conditions and could not be predicted by the exemplar model.

Classification in well-defined and ill-defined categories: evidence for common processing strategies

Journal of experimental psychology. General, 1980

Early work in perceptual and conceptual categorization assumed that categories had criterial features and that category membership could be determined by logical rules for the combination of features. More recent theories have assumed that categories have an ill-defined structure and have prosposed probabilistic or global similarity models for the verification of category membership. In the experiments reported here, several models of categorization were compared, using one set of categories having criterial features and another set having an ill-defined structure. Schematic faces were used as exemplars in both cases. Because many models depend on distance in a multidimensional space for their predictions, in Experiment 1 a multidimensional scaling study was performed using the faces of both sets as stimuli, In Experiment 2, subjects learned the category membership of faces for the categories having criterial features. After learning, reaction times for category verification and typ...

The Influence of Stimulus Properties on Category Construction

Journal of Experimental Psychology-learning Memory and Cognition, 2004

It has been demonstrated that when people free classify stimuli presented simultaneously in an array, they have a preference to categorize by a single dimension. However, when people are encouraged to categorize items sequentially, they sort by "family resemblance," grouping by overall similarity. The present studies extended this research, producing 3 main findings. First, the sequential procedure introduced by G. Regehr and L. R. Brooks (1995) does not always produce a preference for family resemblance sorts. Second, sort strategy in a sequential procedure is sensitive to subtle variations in stimulus properties. Third, spatially separable stimuli evoked more family resemblance sorts than stimuli of greater spatial integration. It is suggested that the family resemblance sorting observed is due to an analytic strategy. Monsell for their helpful comments and suggestions on earlier versions of this article. We also thank Lee Brooks and an anonymous reviewer for their invaluable suggestions.

Cue abstraction and exemplar memory in categorization

Journal of Experimental Psychology: Learning, Memory, and Cognition, 2003

In this article, the authors compare 3 generic models of the cognitive processes in a categorization task. The cue abstraction model implies abstraction in training of explicit cue-criterion relations that are mentally integrated to form a judgment, the lexicographic heuristic uses only the most valid cue, and the exemplar-based model relies on retrieval of exemplars. The results from 2 experiments showed that, in lieu of the lexicographic heuristic, most participants spontaneously integrate cues. In contrast to single-system views, exemplar memory appeared to dominate when the feedback was poor, but when the feedback was rich enough to allow the participants to discern the task structure, it was exploited for abstraction of explicit cue-criterion relations.

Comparing exemplar- and rule-based theories of categorization

Current Directions in Psychological Science, 2006

We address whether human categorization behavior is based on abstracted rules or stored exemplars. Although predictions of both theories often mimic each other in many designs, they can be differentiated. Experimental data reviewed does not support either theory exclusively. We find participants use rules when the stimuli are confusable and exemplars when they are distinct. By drawing on the distinction between simple stimuli (such as lines of various lengths) and complex ones (such as words and objects), we offer a dynamic view of category learning. Initially, categorization is based on rules. During learning, suitable features for discriminating stimuli may be gradually learned. Then, stimuli can be stored as exemplars and used to categorize novel stimuli without recourse to rules.

Alternative strategies of categorization

Cognition, 1998

Psychological studies of categorization often assume that all concepts are of the same general kind, and are operated on by the same kind of categorization process. In this paper, we argue against this unitary view, and for the existence of qualitatively different categorization processes. In particular, we focus on the distinction between categorizing an item by: (a) applying a category-defining rule to the item vs. (b) determining the similarity of that item to remembered exemplars of a category. We begin by characterizing rule application and similarity computations as strategies of categorization. Next, we review experimental studies that have used artificial categories and shown that differences in instructions or time pressure can lead to either rule-based categorization or similarity-based categorization. Then we consider studies that have used natural concepts and again demonstrated that categorization can be done by either rule application or similarity calculations. Lastly, we take up evidence from cognitive neuroscience relevant to the rule vs. similarity issue. There is some indirect evidence from brain-damaged patients for neurological differences between categorization based on rules vs. that based on similarity (with the former involving frontal regions, and the latter relying more on posterior areas). For more direct evidence, we present the results of a recent neuroimaging experiment, which indicates that different neural circuits are involved when people categorize items on the basis of a rule as compared with when they categorize the same items on the basis of similarity.

Category variability, exemplar similarity, and perceptual classification

Memory & Cognition, 2001

Experiments were conducted in which observers learned to classify simple perceptual stimuli into low-variability and high-variability categories. Similarities between objects were measured in independent psychological-scalingtasks. The results showed that observers classified transfer stimuli into the high-variability categories with greater probability than was predicted by a baseline version of an exemplar-similarity model. Qualitative evidence for the role of category variability on perceptual classification, which could not be explained in terms of the baseline exemplar-similaritymodel, was obtained as well. Possible accounts of the effects of category variability are considered in the General Discussion section.