Relational Versus Absolute Representation in Categorization

Cognitive Science, 2015

An important goal in the study of higher-order cognition is to understand how relational categories are acquired and applied. Previous work has explored the potential of withincategory comparison opportunities to promote relational category learning and transfer. This follows from predictions of structure mapping theory (Gentner, 1983, 2003) that alignment leads to highlighting and abstraction of common relational structure. However, a straightforward merging of traditional classification learning with comparison (i.e., trials presenting two same-category items) has not been effective. We explore the hypothesis that classification and comparison have an unforeseen incompatibility. In a 3x2 betweensubjects design we tested three presentation conditions (unconstrained item pairs, category-matched items pairs, single items) in two supervised category learning modes: classification and observation. The major finding is an interaction driven by highly accurate categorization for the obse...

Cognitive Processing

The question of what processes are involved in the acquisition and representation of categories remains unresolved despite several decades of research. Studies using the well-known prototype distortion task (Posner and Keele in J Exp Psychol 77:353-363, 1968) delineate three candidate models. According to exemplar-based models, we memorize each instance of a category and when asked to decide whether novel items are category members or not, the decision is explicitly based on a similarity comparison with each stored instance. By contrast, prototype models assume that categorization is based on the similarity of the target item to an implicit abstraction of the central tendency or average of previously encountered instances. A third model suggests that the categorization of prototype distortions does not depend on pre-exposure to study exemplars at all and instead reflects properties of the stimuli that are easily learned during the test. The four experiments reported here found evidence that categorization in this task is predicated on the first and third of these models, namely transfer at test and the exemplarbased model. But we found no evidence for the second candidate model that assumed that categorization is based on implicit prototype abstraction.

Journal of Experimental Psychology: Learning, Memory, and Cognition, 1994

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. Experiment 1 shows that what is learned depends on which instances are compared. Experiment 2 investigates the conditions under which comparison-based learning occurs. The next experiments find that these comparisons affect subjective feature frequency (Experiment 3) and sensitivity to feature correlations (Experiment 4). Experiment 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. A central issue for category research is how experience with instances gives rise to category knowledge. The goal of the current research is to explore how comparing particular instances with each other during category learning affects what is learned about the category. We begin by briefly outlining two main viewpoints on how category knowledge develops: abstraction models and instance models. Pure abstraction models such as prototype models (e.g., Posner & Keele, 1970; Reed, 1972; see Smith & Medin, 1981 for a review), claim that category knowledge is a single summary representation (usually the central tendency) abstracted over all the instances of a category. This single representation is used to categorize new instances. When a new instance is categorized, the abstraction is updated to reflect the values of the new instance. This view does not claim that one has no memory of the instances seen, but it does claim that the memories of the particular instances are not used to categorize new instances. Instance models (e.g., Brooks, 1978; Hintzman, 1986; Medin & Schaffer, 1978), on the other hand, deny the sufficiency of a single summary representation. According to pure instance models (models that do not store any abstractions), the category is represented by many instances, and new instances are categorized by similarity to a previous instance or set of instances. Despite the fundamental difference between the two types of models, instance models have proven capable of accounting for much of the data that had been taken as evidence of prototype formation (e.g.

Frontiers in Psychology, 2014

Historically, models of categorization have focused on how learners track frequencies and co-occurrence information to abstract relevant category features for generalization. The current study takes a different approach by examining how the temporal dynamics of categorization affect abstraction and generalization. In the learning phase of the experiment, all relevant category features were presented an equal number of times across category exemplars. However, the relevant features were presented on one of two learning schedules: massed or interleaved. At a series of immediate and delayed tests, learners were asked to generalize to novel exemplars that contained massed features, interleaved features, or all novel features. The results of this experiment revealed that, at an immediate test, learners more readily generalized based upon features presented on a massed schedule. Conversely, at a delayed test, learners more readily generalized based upon features presented on an interleaved schedule, until information was no longer readily retrievable from memory. These findings suggest that forgetting and retrieval processes engendered by the temporal dynamics of learning are used as a basis of abstraction, implicating forgetting as a central mechanism of generalization.

2003

1980

Interpreting the INDSCAL Dimensions,.

Memory & Cognition, 1983

This study examined the effects of category structure and category size on categorization reaction time, artificial categories being used to obtain greater experimental control than is usually found in semantic memory research. Four artificial categories varying in structure (hierarchical and nonhierarchical) and size (8 or 16 instances) were introduced to 20 adolescent subjects over a 12-week period by means of stories, exercises, and discussions. Significantly longer categorization reaction times were required for instances from the hierarchical categories, but no set size effect was found. The application of clustering and multidimensional scaling procedures to subjects’ free recall data revealed that subjects had acquired the hierarchical structures but imposed their own structures on the other categories. These findings were interpreted within a spreading activation framework.

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.

Memory & Cognition, 2002

Attention, perception & psychophysics, 2015

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit cat...