Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T22:44:13.921Z Has data issue: false hasContentIssue false
  • English
  • Français

Pinning down the theoretical commitments of Bayesian cognitive models

Published online by Cambridge University Press:  25 August 2011

Matt Jones  and
Bradley C. Love
Matt Jones
Affiliation:
Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309. [email protected]
Bradley C. Love
Affiliation:
Department of Psychology, University of Texas, Austin, TX 78712. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Mathematical developments in probabilistic inference have led to optimism over the prospects for Bayesian models of cognition. Our target article calls for better differentiation of these technical developments from theoretical contributions. It distinguishes between Bayesian Fundamentalism, which is theoretically limited because of its neglect of psychological mechanism, and Bayesian Enlightenment, which integrates rational and mechanistic considerations and is thus better positioned to advance psychological theory. The commentaries almost uniformly agree that mechanistic grounding is critical to the success of the Bayesian program. Some commentaries raise additional challenges, which we address here. Other commentaries claim that all Bayesian models are mechanistically grounded, while at the same time holding that they should be evaluated only on a computational level. We argue this contradictory stance makes it difficult to evaluate a model's scientific contribution, and that the psychological commitments of Bayesian models need to be made more explicit.

Type
Authors' Response
Information
Behavioral and Brain Sciences , Volume 34 , Issue 4 , August 2011 , pp. 215 - 231
Copyright
Copyright © Cambridge University Press 2011

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
Anderson, J. R. (1991b) The adaptive nature of human categorization. Psychological Review 98:409–29.CrossRefGoogle Scholar
Cohen, J. D., McClure, S. M. & Yu, A. J. (2007) Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences 362:933–42.CrossRefGoogle ScholarPubMed
Colunga, E. & Smith, L. (2005) From the lexicon to expectations about kinds: A role for associative learning. Psychological Review 112(2):347–82.CrossRefGoogle ScholarPubMed
Daw, N. & Courville, A. (2007) The pigeon as particle filter. Advances in Neural Information Processing Systems 20:1528–35.Google Scholar
Elliott, S. W. & Anderson, J. R. (1995) Effect of memory decay on predictions from changing categories. Journal of Experimental Psychology: Learning, Memory, and Cognition 21:815–36.Google ScholarPubMed
Fried, L. S. & Holyoak, K. J. (1984) Induction of category distributions: A framework for classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 10:234–57.Google ScholarPubMed
Gibson, J. J. (1979) The ecological approach to visual perception. Houghton Mifflin.Google Scholar
Gigerenzer, G. & Brighton, H. (2009) Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science 1:107–43.CrossRefGoogle ScholarPubMed
Gold, J. I. & Shadlen, M. N. (2001) Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences 5:10–16.CrossRefGoogle ScholarPubMed
Griffiths, T. L., Steyvers, M. & Tenenbaum, J. B. (2007) Topics in semantic representation. Psychological Review 114:211–44.CrossRefGoogle ScholarPubMed
Kemp, C., Perfors, A. & Tenenbaum, J. B. (2007) Learning overhypotheses with hierarchical Bayesian models. Developmental Science 10:307–21.CrossRefGoogle ScholarPubMed
Love, B. C. (2005) Environment and goals jointly direct category acquisition. Current Directions in Psychological Science 14:195–99.CrossRefGoogle Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
Murphy, G. L. & Ross, B. H. (2007) Use of single or multiple categories in category-based induction. In: Inductive reasoning: Experimental, developmental, and computational approaches, ed. Feeney, A. & Heit, E., p. 205–25. Cambridge Press.Google Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.CrossRefGoogle Scholar
Pearl, J. (2000) Causality: Models, reasoning, and inference. Cambridge University Press.Google Scholar
Rescorla, R. A. & Wagner, A. R. (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In: Classical conditioning II: Current theory and research, ed. Black, A. H. & Prokasy, W. F., p. 64–99. Appleton-Century-Crofts.Google Scholar
Sakamoto, Y., Jones, M. & Love, B. C. (2008) Putting the psychology back into psychological models: Mechanistic versus rational approaches. Memory and Cognition 36(6):1057–65.CrossRefGoogle ScholarPubMed
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010a) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117:1144–67.CrossRefGoogle ScholarPubMed
Shiffrin, R. M. & Steyvers, M. (1998) The effectiveness of retrieval from memory. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., p. 73–95. Oxford University Press.Google Scholar
Skinner, B. F. (1938) The behavior of organisms: An experimental analysis. Appleton-Century.Google Scholar
Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L. & Samuelson, L. (2002) Object name learning provides on-the-job training for attention. Psychological Science 13:13–19.CrossRefGoogle ScholarPubMed
Spirtes, P., Glymour, C. & Scheines, R. (2000) Causation, prediction, and search, 2nd edition. (original edition published in 1993) MIT Press.Google Scholar
Sternberg, S. (1966) High-speed scanning in human memory. Science 153:652–54.CrossRefGoogle ScholarPubMed
Wilder, M. H., Jones, M. & Mozer, M. C. (2009) Sequential effects reflect parallel learning of multiple environmental regularities. Advances in Neural Information Processing Systems 22:2053–61.Google Scholar