Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-14T08:13:52.311Z Has data issue: false hasContentIssue false
  • English
  • Français

Explaining more by drawing on less

Published online by Cambridge University Press:  12 February 2009

Ulrike Hahn
Ulrike Hahn
Affiliation:
School of Psychology, Cardiff University, Cardiff CF10 3AT, United Kingdom. hahnu@Cardiff.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

One of the most striking features of “Bayesian rationality” is the detail with which behavior on logical reasoning tasks can now be predicted and explained. This detail is surprising, given the state of the field 10 to 15 years ago, and it has been brought about by a theoretical program that largely ignores consideration of cognitive processes, that is, any kind of internal behavior that generates overt responding. It seems that an increase in explanatory power can be achieved by restricting a psychological theory.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 32 , Issue 1 , February 2009 , pp. 90 - 91
Copyright
Copyright © Cambridge University Press 2009

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

Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Bullinaria, J. A. & Chater, N. (1995) Connectionist modelling: Implications for neuropsychology. Language and Cognitive Processes 10:227–64.Google Scholar
Chater, N. (1995) Neural networks: The new statistical models of mind. In: Connectionist models of memory and language, ed. Levy, J. P., Bairaktaris, D., Bullinaria, J. A. & Cairns, P., pp. 207–27. University College London Press.Google Scholar
Chater, N. & Oaksford, M.eds. (2008a) The probabilistic mind: Prospects for Bayesian cognitive science. Oxford University Press.Google Scholar
Chater, N., Tenenbaum, J. B. & Yuille, A. eds. (2006) Probabilistic models of cognition: Where next? Trends in Cognitive Sciences 10:335–44. special Issue.Google Scholar
Edgington, D. (1995) On conditionals. Mind 104:235–329.Google Scholar
Evans, J. St. B. T. & Over, D. E. (2004) If. Oxford University Press.CrossRefGoogle Scholar
Fodor, J. A. (1983) The modularity of mind. MIT Press.CrossRefGoogle Scholar
Fodor, J. A. & Pylyshyn, Z. (1988) Connectionism and cognitive architecture: A critical analysis. Cognition 28:183–204.CrossRefGoogle ScholarPubMed
Klauer, K. C., Stahl, C. & Erdfelder, E. (2007) The abstract selection task: New data and an almost comprehensive model. Journal of Experimental Psychology: Learning, Memory and Cognition 33:680–703.Google Scholar
Oaksford, M. & Brown, G. D. A., eds. (1994) Neurodynamics and psychology. Academic Press.Google Scholar
Oaksford, M. & Chater, N. (1994) A rational analysis of the selection task as optimal data selection. Psychological Review 101:608–31.CrossRefGoogle Scholar
Oaksford, M. & Chater, N.eds. (1998b) Rational models of cognition. Oxford University Press.Google Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.CrossRefGoogle Scholar
Oberauer, K. (2006) Reasoning with conditionals: A test of formal models of four theories. Cognitive Psychology 53:238–83.CrossRefGoogle ScholarPubMed
Schroyens, W. & Schaeken, W. (2003) A critique of Oaksford, Chater and Larkin's (2000) conditional probability model of conditional reasoning. Journal of Experimental Psychology: Learning, Memory and Cognition 29:140–49.Google Scholar
Smolensky, P. (1990) Tensor product variable binding and the representation of symbolic structures in connectionist networks. Artificial Intelligence 46:159–216.CrossRefGoogle Scholar
Tenenbaum, J. B., Kemp, C. & Shafto, P. (2007) Theory based Bayesian models of inductive reasoning. In: Inducive reasoning, ed. Feeney, A. & Heit, E., pp. 167–204. Oxford University Press.Google Scholar