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Transcending inductive category formation in learning

Published online by Cambridge University Press:  04 February 2010

Roger C. Schank ,
Gregg C. Collins  and
Lawrence E. Hunter
Roger C. Schank
Affiliation:
Computer Science Department, Yale University, New Haven, Conn. 06520
Gregg C. Collins
Affiliation:
Computer Science Department, Yale University, New Haven, Conn. 06520
Lawrence E. Hunter
Affiliation:
Computer Science Department, Yale University, New Haven, Conn. 06520
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Abstract

The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial roles of other cognitive processes (such as explanation and credit assignment) are ignored or oversimplified. Learning necessarily involves pragmatic considerations that can only be handled by complex cognitive processes.

We provide an alternative framework for learning according to which category definitions must be based on category function. The learning system invokes other cognitive processes to accomplish difficult tasks, makes inferences, analyses and decides among potential features, and specifies how and when categories are to be generated and modified. We also examine the methodological underpinnings of the two approaches and compare their motivations.

Keywords

categorizationcausal inferencecomputer modelsconcept formationexpectationsfeaturesgoalsinductioni learningmemoryplanningproblem-solvingrepresentation
Type
Target article
Information
Behavioral and Brain Sciences , Volume 9 , Issue 4 , December 1986 , pp. 639 - 651
Copyright
Copyright © Cambridge University Press 1986

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