Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-27T18:48:18.541Z Has data issue: false hasContentIssue false
  • English
  • Français

Time for a re-think: Problems with the parallel distributed approach to semantic cognition

Published online by Cambridge University Press:  11 December 2008

Philip Quinlan
Philip Quinlan
Affiliation:
Department of Psychology, University of York, Heslington, York, North Yorkshire YO10 5DD, United Kingdom. [email protected]://www.york.ac.uk/depts/psych/www/people/biogs/ptq1.html
Get access Rights & Permissions [Opens in a new window]

Abstract

Rogers & McClelland (R&M) have provided an impressive outline of the capabilities of a class of multi-layered perceptrons that mimic many aspects of human knowledge acquisition. Despite this success, in the literature several basic issues are raised and concerns are expressed. Indeed, the problems are so acute that a different way of thinking is called for. In this commentary it is suggested that rational models approach provides a promising alternative.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 31 , Issue 6 , December 2008 , pp. 724
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Fodor, J. A. & McLaughlin, B. P. (1990) Connectionism and the problem of systematicity: Why Smolensky's solution doesn't work. Cognition 35:183–204.CrossRefGoogle ScholarPubMed
Hinton, G. E. (1984) Distributed representations. (Technical Report CMU-CS-84–157). Department of Computer Science, Carnegie Mellon University.Google Scholar
Rogers, T. T. & McClelland, J. L. (2004) Semantic cognition: A parallel distributed processing approach. MIT Press.CrossRefGoogle Scholar
Steyvers, M., Griffiths, T. L. & Dennis, S. (2006) Probabilistic inference in human semantic memory. Trends in Cognitive Science 10:327–34.CrossRefGoogle ScholarPubMed
Xu, F. & Tenenbaum, J. B. (2007a) Word learning as Bayesian inference. Psychological Review 114:245–72.CrossRefGoogle ScholarPubMed
Xu, F. & Tenenbaum, J. B. (2007b) Sensitivity to sampling in Bayesian word learning. Developmental Science 10:288–97.CrossRefGoogle ScholarPubMed