haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail and constructs dams by cutting down trees with its This work was supported by Indiana University Cognitive Science Program Fellowships and by NIMH ResearchTraining Grant PHS-T32-MH19879-03 to Erickson, and in part by NIMH FIRST Award 1-R29-MH51572-01 to Kruschke. This research was reported as a poster at the 1996 Cognitive Science Society Conference in San Diego, CA. We than

Two connectionist models of attention in associative learning, previously used to model human category learning, are shown to have special cases that are essentially equivalent to N. J. Mackintosh's (1975, Psychological Review, 82, 276 298) classic model of attention in animal learning. The models unify formulas for associative weight change with formulas for attentional change, under a common goal of error reduction. Error-driven attentional shifting accelerates learning of new associations but also protects previously learned associations from retroactive interference. The models are fit to data from a recent experiment in human associative learning (J. K. Kruschke 6 N. J. Blair, 2000, Psychonomic Bulletin 6 Review, 7, 636 645), which shows that blocking of learning involves learned inattention. The approach also provides a novel and unifying theory of latent inhibition (the preexposure effect) in terms of blocking. The discussion summarizes how the approach accounts for a variety of other ``irrational' ' phenomena in associative learning, including base rate effects, perseveration of attention through relevance

A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to back-propagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trial-by-trial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The back-propagation of target values to attention allows the model to show trial-order effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.

Concepts are interrelated to the extent that the characterization each concept is influenced by the other concepts, and isolated to the extent that the characterization of one concept is independent of other concepts. The relative categorization accuracy of the prototype and caricature of a concept can be used as a measure of concept interrelatedness. The prototype is the central tendency of a concept, whereas a caricature deviates from the concept's central tendency in the direction opposite to the central tendency of other acquired concepts. The prototype is predicted to be relatively well categorized when a concept is relatively independent of other concepts, but the caricature is predicted to be relatively well categorized when a concept is highly related to other concepts. Support for these predictions comes from manipulations of the labels given to simultaneously acquired concepts (Experiment 1) and the order of categories during learning (Experiment 2). 3 Concepts seem to be simultaneously connected to each other and to the external world. On the one hand, concepts seem to gain their meaning by the role that they play within a network of concepts (Collins & Quillian, 1969; Field, 1977). The notion of a "conceptual web" by which concepts all mutually define one another has been highly influential in all of the major fields that comprise cognitive science, including linguistics (Saussure, 1915/1959), computer science (Lenat & Feigenbaum, 1991), psychology (Landauer & Dumais, 1997), and philosophy (Block, 1999). However, there is also dissatisfaction in some quarters with the circularity of this conceptual web account. Researchers have argued that concepts must be grounded in the external world rather than merely related to each other (Harnad, 1990). The British e...

If first language is rational in the sense that acquisition produces an end-state model of language that is a proper reflection of input and that optimally prepares speakers for comprehension and production, second language is usually not. This paper considers the apparent irrationalities of L2 acquisition, that is the shortcomings where input fails to become intake. It describes how ‘learned attention’, a key concept in contemporary associative and connectionist theories of animal and human learning, explains these effects. The fragile features of L2 acquisition are those which, however available as a result of frequency, recency, or context, fall short of intake because of one of the factors of contingency, cue competition, salience, interference, overshadowing, blocking, or perceptual learning, which are all shaped by the L1. Each phenomenon is explained within associative learning theory and exemplified in language learning. Paradoxically, the successes of L1 acquisition and the limitations of L2 acquisition both derive from the same basic learning principles.

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of withincategory information. Accordingly, we predicted that classification learning would produce a deficit in people’s ability to draw novel contrasts—distinctions that were not part of training—compared with feature inference learning. Two experiments confirmed that classification learners were at a disadvantage at making novel distinctions. Eye movement data indicated that this conceptual inflexibility was due to (a) a narrower attention profile that reduces the encoding of many category features and (b) learned inattention that inhibits the reallocation of attention to newly relevant information. Implications of these costs of supervised classification learning for views of conceptual structure are discussed.

Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility of important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we have developed a general approach to modeling individual differences using families of cognitive models in which different groups of subjects are identified as having different psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We evaluate this individual differences approach in a simulation study and show that it is superior in terms of the key modeling goals of prediction and understanding. We also provide two practical demonstrations of the approach, one using the ALCOVE model of category learning with data from four previously analyzed category learning experiments, the other using multidimensional scaling representational models with previously analyzed similarity data for colors. In both demonstrations, meaningful individual differences are found and the psychological models are able to account for this variation through interpretable differences in parameterization. The results highlight the potential of extending cognitive models to consider individual differences. Much of cognitive psychology, like other empirical sciences, involves the development and evaluation of models. Models provide formal accounts of the explanations proposed by theories and have been developed to address diverse cognitive phenomena, ranging from

Bayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST). Ironically, specific Bayesian models of cognition and perception may not long endure the ravages of empirical verification, but generic Bayesian methods for data analysis will eventually dominate. It is time that Bayesian data analysis became the norm for empirical methods in cognitive science. This article reviews a fatal flaw of NHST and introduces the reader to some benefits of Bayesian data analysis. The article presents illustrative examples of multiple comparisons in Bayesian ANOVA and Bayesian approaches to statistical power.

Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility that there are important individual di#erences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we develop a general approach to modeling individual di#erences using families of cognitive models, where di#erent groups of subjects are identified as having di#erent psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We demonstrate the general approach in a concrete and detailed way using the ALCOVE model of category learning, and data from four previously analysed category learning experiments. Meaningful individual di#erences are found for three of the four experiments, and ALCOVE is able to account for this variation through psychologically interpretable di#erences in parameterization.

It is well-established that (non-linguistic) categorization is driven by a functional demand of prediction. We suggest that prediction likewise may well play a role in motivating the learning of semantic generalizations about argument structure constructions. We report corpora statistics that indicate that the argument frame or construction has roughly equivalent cue validity as a predictor of overall sentence meaning as the morphological form of the verb, and has greater category validity. That is, the construction is at least as reliable and more available. Moreover, given the fact that many verbs have quite low cue validity in isolation, attention to the contribution of the construction is essential.