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Survival in a world of probable objects: A fundamental reason for Bayesian enlightenment

Published online by Cambridge University Press:  25 August 2011

Shimon Edelman
Affiliation:
Department of Psychology, Cornell University, Ithaca, NY 14853. [email protected]@cornell.eduhttp://kybele.psych.cornell.edu/~edelman
Reza Shahbazi
Affiliation:
Department of Psychology, Cornell University, Ithaca, NY 14853. [email protected]@cornell.eduhttp://kybele.psych.cornell.edu/~edelman

Abstract

The only viable formulation of perception, thinking, and action under uncertainty is statistical inference, and the normative way of statistical inference is Bayesian. No wonder, then, that even seemingly non-Bayesian computational frameworks in cognitive science ultimately draw their justification from Bayesian considerations, as enlightened theorists know fully well.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2011

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References

Bishop, C. M. (2006) Pattern recognition and machine learning. Springer.Google Scholar
Chater, N., Tenenbaum, J. & Yuille, A. (2006) Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences 10(7):287–91.CrossRefGoogle ScholarPubMed
Edelman, S. (2008a) A swan, and pike, and a crawfish walk into a bar. Journal of Experimental and Theoretical Artificial Intelligence 20:261–68.CrossRefGoogle Scholar
Edelman, S. (2008b) Computing the mind: How the mind really works. Oxford University Press.CrossRefGoogle Scholar
Edelman, S. (2008c) On the nature of minds, or: Truth and consequences. Journal of Experimental and Theoretical Artificial Intelligence 20:181–96.CrossRefGoogle Scholar
Gibson, J. J. (1957) Survival in a world of probable objects. Contemporary Psychology 2:3335.CrossRefGoogle Scholar
Glimcher, P. W., Camerer, C., Poldrack, R. A. & Fehr, E. (2008) Neuroeconomics: Decision making and the brain. Academic Press.Google Scholar
Hastie, T., Tibshirani, R. & Friedman, J. H. (2009) The elements of statistical learning, 2nd edition. Springer.CrossRefGoogle Scholar
Heit, E. (2000) Properties of inductive reasoning. Psychonomic Bulletin and Review 7:569–92.CrossRefGoogle ScholarPubMed
Howson, C. & Urbach, P. (1991) Bayesian reasoning in science. Nature 350:371–74.CrossRefGoogle Scholar
Hume, D. (1740) A treatise of human nature. [Available online through a variety of sources, including Project Gutenberg at: http://www.gutenberg.org/ebooks/4705]Google Scholar
Kawato, M. (1999) Internal models for motor control and trajectory planning. Current Opinion in Neurobiology 9:718–27.CrossRefGoogle ScholarPubMed
Knill, D. & Richards, W., eds. (1996) Perception as Bayesian inference. Cambridge University Press.CrossRefGoogle Scholar
Körding, K. P. & Wolpert, D. M. (2006) Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences 10:319–26.CrossRefGoogle ScholarPubMed
Marroquin, J., Mitter, S. & Poggio, T. (1987) Probabilistic solution of ill-posed problems in computational vision. Journal of the American Statistical Association 82:7689.CrossRefGoogle Scholar
Mussa-Ivaldi, F. A. & Giszter, S. F. (1992) Vector field approximation: A computational paradigm for motor control and learning. Biological Cybernetics 67:491500.CrossRefGoogle ScholarPubMed
Poggio, T. (1990) A theory of how the brain might work. Cold Spring Harbor Symposia on Quantitative Biology 55:899910.CrossRefGoogle ScholarPubMed
Schervish, M. J. (1995) Theory of statistics. Springer Series in Statistics. Springer.CrossRefGoogle Scholar
Tikhonov, A. N. & Arsenin, V. Y. (1977) Solutions of ill-posed problems. W. H. Winston.Google Scholar
Wasserman, L. (2003) All of statistics: A concise course in statistical inference. Springer Texts in Statistics. Springer.Google Scholar