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Connectionist Modelling of Word Recognition

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Abstract

Connectionist models offer concretemechanisms for cognitive processes. When these modelsmimic the performance of human subjects theycan offer insights into the computationswhich might underlie human cognition. We illustratethis with the performance of a recurrentconnectionist network which produces the meaningof words in response to their spellingpattern. It mimics a paradoxical pattern oferrors produced by people trying to read degradedwords. The reason why the network produces thesurprising error pattern lies in the nature ofthe attractors which it develops as it learns tomap spelling patterns to semantics. The keyrole of attractor structure in the successfulsimulation suggests that the normal adult semanticreading route may involve attractor dynamics, andthus the paradoxical error pattern isexplained.

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Authors and Affiliations

  1. Department of Experimental Psychology, South Parks Road, Oxford, OX1 3UD, U.K.

    Peter McLeod

  2. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, 15213-3890, U.S.A.

    David C. Plaut

  3. SISSA, 2-4 Via Beirut, Trieste, 34013, Italy

    Tim Shallice

Authors
  1. Peter McLeod
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  2. David C. Plaut
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  3. Tim Shallice
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McLeod, P., Plaut, D.C. & Shallice, T. Connectionist Modelling of Word Recognition. Synthese 129, 173–183 (2001). https://doi.org/10.1023/A:1013003423323

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  • DOI: https://doi.org/10.1023/A:1013003423323

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