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Computational complexity analysis can help, but first we need a theory

  • Todd Wareham (a1), Iris van Rooij (a2) and Moritz Müller (a3)
  • DOI: http://dx.doi.org/10.1017/S0140525X0800469X
  • Published online: 01 July 2008
Abstract
Abstract

Leech et al. present a connectionist algorithm as a model of (the development) of analogizing, but they do not specify the algorithm's associated computational-level theory, nor its computational complexity. We argue that doing so may be essential for connectionist cognitive models to have full explanatory power and transparency, as well as for assessing their scalability to real-world input domains.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

J. Bruck & J. Goodman (1990) On the power of neural networks for solving hard problems. Journal of Complexity 6:129–35.

D. Gentner (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science 7:155–70.

C. Green (2001) Scientific models, connectionist networks, and cognitive science. Theory and Psychology 11:97117.

D. Marr (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.

I. van Rooij , S. Stege , & H. Kadlec (2005) Sources of complexity in subset choice. Journal of Mathematical Psychology 49:160–87.

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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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