Theory of categorization based on distributed memory storage.

Abstract : As an alternative to probabalistic and examplar models of categorization, we develop a model based on the assumption of distributed memory storage. Subjects in two experiments performed tasks related to the categorization of random dot patterns. First, the perceived similarity was measured between two such dot patterns, one a distortion of the other. Second, groups of examplar patterns derived from a category prototype were classified together in a category learning task. When the number of examples was small, new dot patterns were classified according to their similarity to learned exemplars; when the number was large, accuracy depended on a dot pattern's similarity to the prototype pattern. The distributed memory model is used to explain a number of aspects of the experimental findings. Detailed computer simulations are described for the similarity, categorization, and prototype enhancement results.

[1]  I. S. Gradshteyn,et al.  Table of Integrals, Series, and Products , 1976 .

[2]  Granino A. Korn,et al.  Mathematical handbook for scientists and engineers , 1961 .

[3]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[4]  M. Posner,et al.  On the genesis of abstract ideas. , 1968, Journal of experimental psychology.

[5]  M. Posner,et al.  Retention of Abstract Ideas. , 1970 .

[6]  J. Bransford,et al.  Abstraction of visual patterns. , 1971, Journal of experimental psychology.

[7]  Stephen K. Reed,et al.  Pattern recognition and categorization , 1972 .

[8]  D. Homa Prototype abstraction and classification of new instances as a function of number of instances defining the prototype , 1973 .

[9]  K. Nelson Concept, word, and sentence: Interrelations in acquisition and development. , 1974 .

[10]  Stephen A. Ritz,et al.  Distinctive features, categorical perception, and probability learning: some applications of a neural model , 1977 .

[11]  F. Hayes-Roth,et al.  Concept learning and the recognition and classification of exemplars , 1977 .

[12]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[13]  Jack W. Silverstein,et al.  Reply to Grossberg , 1978 .

[14]  D. Robbins,et al.  The genesis and use of exemplar vs. prototype knowledge in abstract category learning , 1978 .

[15]  D. L. Hintzman,et al.  Differential forgetting of prototypes and old instances: Simulation by an exemplar-based classification model , 1980, Memory & cognition.

[16]  D. Homa,et al.  Search for Abstracted Information , 1981 .

[17]  John R. Anderson,et al.  The effects of category generalizations and instance similarity on schema abstraction. , 1981 .

[18]  E. Rosch,et al.  Categorization of Natural Objects , 1981 .

[19]  Edward E. Smith,et al.  Categories and concepts , 1984 .

[20]  J. Eich A composite holographic associative recall model. , 1982 .

[21]  B. Murdock A Theory for the Storage and Retrieval of Item and Associative Information. , 1982 .