YEARS

1994-2000

AUTHORS

John K Kruschke

TITLE

EXPLORATIONS OF A CONNECTIONIST CATEGORY LEARNING MODEL

ABSTRACT

The phrase "category learning," as used in this proposal, refers to any situation in which a person must learn to classify stimuli, such as learning to classify lists of symptoms into the correct disease category. Theories of category learning have hypothesized various underlying representations ranging from rules to prototypes to exemplars, and various learning processes ranging from gradual association building to all-or- none rule acquisition. The proposed research explores applications of successively greater extensions of the ALCOVE model of category learning (Kruschke 1990, 1992). The ALCOVE model is a synthesis of three traditions in theories of category learning: It combines (i) exemplar-based representation with (ii) dimensional attention and (iii) error-driven learning. It is a theoretical synthesis that also quantitatively fits human-learning data in a variety of situations. The intermediate-range goal of this research is to explore the scope of applicability of ALCOVE and its extensions, thereby determining the scope of its underlying explanatory principles. The long-range goal of this research is to establish empirical evidence and theoretical accounts for relations between exemplar, rule-based, and other models of category learning. The significance of the research will be its role in covering a broad range of empirical data under a single explanatory umbrella, and in unifying alternative mechanisms and determining situations in which each seems to be dominant in human learning. All the proposed research is relevant to our knowledge of mental health insofar as it will help identify underlying learning mechanisms in normal adults. There is additional potential of direct relevance to mental health if it is found that exemplar and rule-based systems are dissociable components of category learning. For example, Pinker (1991) has argued that there are separate exemplar and rule systems in language learning, and has adduced behavioral effects of brain lesions to argue his case. The studies proposed here might lead to analogous decompositions for general category learning, which could in turn eventually have broad implications for teaching methods and therapies for both normals and the mentally challenged or brain damaged.

FUNDED PUBLICATIONS

  • Decision boundaries in one-dimensional categorization.
  • Rule-based extrapolation in perceptual categorization.
  • Base rates in category learning.
  • The role of attention shifts in the categorization of continuous dimensioned stimuli
  • The role of attention shifts in the categorization of continuous dimensioned stimuli.
  • A model of probabilistic category learning.
  • Rules and exemplars in category learning.
  • Blocking and backward blocking involve learned inattention.
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    24 TRIPLES      15 PREDICATES      25 URIs      7 LITERALS

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    21 sg:title EXPLORATIONS OF A CONNECTIONIST CATEGORY LEARNING MODEL
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