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A simple model from a powerful framework that spans levels of analysis

Published online by Cambridge University Press:  11 December 2008

Timothy T. Rogers  and
James L. McClelland
Timothy T. Rogers
Affiliation:
University of Wisconsin-Madison, Department of Psychology, Madison, WI 53706; [email protected]://concepts.psych.wisc.edu
James L. McClelland
Affiliation:
Center for Mind, Brain and Computation, and Department of Psychology, Stanford University, Stanford, CA 94305. [email protected]://psychology.stanford.edu/~jlm
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Abstract

The commentaries reflect three core themes that pertain not just to our theory, but to the enterprise of connectionist modeling more generally. The first concerns the relationship between a cognitive theory and an implemented computer model. Specifically, how does one determine, when a model departs from the theory it exemplifies, whether the departure is a useful simplification or a critical flaw? We argue that the answer to this question depends partially upon the model's intended function, and we suggest that connectionist models have important functions beyond the commonly accepted goals of fitting data and making predictions. The second theme concerns perceived in-principle limitations of the connectionist approach to cognition, and the specific concerns these perceived limitations raise for our theory. We argue that the approach is not in fact limited in the ways our critics suggest. One common misconception, that connectionist models cannot address abstract or relational structure, is corrected through new simulations showing directly that such structure can be captured. The third theme concerns the relationship between parallel distributed processing (PDP) models and structured probabilistic approaches. In this case we argue that there the difference between the approaches is not merely one of levels. Our PDP approach differs from structured statistical approaches at all of Marr's levels, including the characterization of the goals of cognitive computations, and of the representations and algorithms used.

Type
Authors' Response
Information
Behavioral and Brain Sciences , Volume 31 , Issue 6 , December 2008 , pp. 729 - 749
Copyright
Copyright © Cambridge University Press 2008

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