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Connectionist Models of Learning, Development and Evolution pp 53–62Cite as

Modularity and Specialized Learning in the Organization of Behaviour

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Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

Research in artificial neural networks (ANN) has provided new insights for psychologists, particularly in the areas of memory, perception, representation and learning. However, the types and levels of psychological modelling possible in artificial neural systems is limited by the current state of the technology. This chapter discusses modularity as illuminated from research in complete agents, such as autonomous robots or virtual reality characters. We describe the sorts of modularity that have been found useful in agent research. We then consider the issues involved in modelling such systems neurally, particularly with respect to the implications of this work for learning and development. We conclude that such a syStem would be highly desirable, but currently poses serious technical challenges to the field of ANN. We propose that in the mean time, psychologists may want to consider modelling learning in specialised hybrid systems which can support both complex behaviour and neural learning.

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Authors
  1. Joanna Bryson
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  2. Lynn Andrea Stein
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Editor information

Editors and Affiliations

  1. Quantitative Psychology and Cognitive Science, Department of Psychology (B32), University of Liège, 4000, Liège, Belgium

    Robert M. French PhD  & Jacques P. Sougné PhD  & 

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© 2001 Springer-Verlag London

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Bryson, J., Stein, L.A. (2001). Modularity and Specialized Learning in the Organization of Behaviour. In: French, R.M., Sougné, J.P. (eds) Connectionist Models of Learning, Development and Evolution. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0281-6_6

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  • DOI: https://doi.org/10.1007/978-1-4471-0281-6_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-354-6

  • Online ISBN: 978-1-4471-0281-6

  • eBook Packages: Springer Book Archive

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