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Semantic cognition or data mining?

  • Denny Borsboom (a1) and Ingmar Visser (a1)
Abstract

We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.

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References
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Rogers, T. T. & McClelland, J. L. (2004) Semantic cognition: A parallel distributed processing approach. MIT Press.
Sarle, W. S. (1994) Neural networks and statistical models. In: Proceedings of the Nineteenth Annual SAS Users Group International Conference. pp. 1538–50. SAS Institute.
Schmittmann, V. D., Visser, I. & Raijmakers, M. E. J. (2006) Multiple learning modes in the development of rule-based category-learning task performance. Neuropsychologia 44:2079–91.
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Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
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