What Have Computational Models Ever Done for Us?: A Case Study in Classical Conditioning

What Have Computational Models Ever Done for Us?: A Case Study in Classical Conditioning

Eduardo Alonso (School of Informatics, City University London, London, UK & Centre for Computational and Animal Learning Research, St. Albans, UK) and Esther Mondragón (Centre for Computational and Animal Learning Research, St. Albans, UK)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/ijalr.2014010101
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Abstract

The last 50 years have seen the progressive refinement of our understanding of the mechanisms of classical conditioning and this has resulted in the development of several influential theories that are able to explain with considerable precision a wide variety of experimental findings, and to make non-intuitive predictions that have been confirmed. This success has spurred the development of increasingly sophisticated models that encompass more complex phenomena. In such context, it is widely acknowledged that computational modeling plays a fundamental part. In this paper the authors analyze critically the role that computational models, as simulators and as psychological models by proxy, have played in this enterprise.
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Computational Models As Models Of Learning

Computational models of learning have been considered as psychological models in themselves. This position, that constitutes a milestone in the annals of cognitive science and artificial intelligence, is in fact a misuse of the term. We are illustrating our contention by means of a paradigmatic example, the use of Artificial Neural Networks (ANNs) in conditioning theory. In what follows we discuss the inadequacy of such approach at different levels of analysis, namely, ontological, formal, representational, functional, and structural.

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