Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-14T08:02:54.239Z Has data issue: false hasContentIssue false

Identifying the optimal response is not a necessary step toward explaining function

Published online by Cambridge University Press:  12 February 2009

Henry Brighton
Affiliation:
Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, 14195 Berlin, Germany. hbrighton@mpib-berlin.mpg.deolsson@mpib-berlin.mpg.de
Henrik Olsson
Affiliation:
Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, 14195 Berlin, Germany. hbrighton@mpib-berlin.mpg.deolsson@mpib-berlin.mpg.de

Abstract

Oaksford & Chater (O&C) argue that a rational analysis is required to explain why a functional process model is successful, and that, when a rational analysis is intractable, the prospects for understanding cognition from a functional perspective are gloomy. We discuss how functional explanations can be arrived at without seeking the optimal response function demanded by a rational analysis, and argue that explaining function does not require optimality.

Type
Open Peer Commentary
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. (1991a) Is human cognition adaptive? Behavioral and Brain Sciences 14:471–84; discussion 485–517.CrossRefGoogle Scholar
Bookstaber, R. & Langsam, J. (1985) On the optimality of coarse behavior rules. Journal of Theoretical Biology 116:161–93.CrossRefGoogle ScholarPubMed
Brighton, H. & Gigerenzer, G. (2008) Bayesian brains and cognitive mechanisms: Harmony or dissonance? In: The probabilistic mind: Prospects for Bayesian cognitive science, ed. Chater, N. & Oaksford, M., pp. 189208, Oxford University Press.CrossRefGoogle Scholar
Cooper, G. F. (1990) The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42:393405.CrossRefGoogle Scholar
Danks, D. (2008) Rational analyses, instrumentalism, and implementations. In: The probabilistic mind: Prospects for Bayesian cognitive science, ed. Chater, N. & Oaksford, M.. Oxford University Press.Google Scholar
Domingos, P. & Pazzani, M. (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29:103–30.CrossRefGoogle Scholar
Gigerenzer, G., Todd, P. & the ABC Research Group. (1999) Simple heuristics that make us smart. Oxford University Press.Google Scholar
Kearns, M., Mansour, Y., Ng, A. Y. & Ron, D. (1997) An experimental and theoretical comparison of model selection methods. Machine Learning 27:750.CrossRefGoogle Scholar
Kuncheva, L. I. (2006) On the optimality of Naïve Bayes with dependent binary features. Pattern Recognition Letters 27:830–37.CrossRefGoogle Scholar
Lipsey, R. G. & Lancaster, K. (1956) The general theory of second best. Review of Economic Studies 24:1132.CrossRefGoogle Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.CrossRefGoogle Scholar
Pitt, M. A., Myung, I. J. & Zhang, S. (2002) Toward a method of selecting among computational models of cognition. Psychological Review 109:472–91.CrossRefGoogle Scholar
Todd, P. M. & Gigerenzer, G. (2003) Bounding rationality to the world. Journal of Economic Psychology 24:143–65.CrossRefGoogle Scholar