COGNITIVE THEORY AND NEURAL MODEL: THE ROLE OF LOCAL REPRESENTATIONS
Commentary on Green on Connectionist-Explanation

Abstract

Green raises a number of questions regarding the role of "connectionist" models in scientific theories of cognition, one of which concerns exactly what it is that units in artificial neural networks (ANNs) stand for, if not specific neurones or groups of neurones, or indeed, specific theoretical entities. In placing all connectionist models in the same basket, Green seems to have ignored the fundamental differences which distinguish classes of models from each other. In this commentary, we address the issue of distributed versus localised representations in ANNs, arguing that it is difficult (but not impossible) to investigate what units stand for in the former case, but that units do correspond to specific theoretical entities in the latter case. We review the role of localised representations in a neural network model of a semantic system in which each unit corresponds to a letter, word, word sense, or semantic feature, and whose dynamics and behaviour match those predicted from a cognitive theory of skilled reading. Thus, we argue that ANNs might be useful in developing general mathematical models of processes for existing cognitive theories that already enjoy empirical support.

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