Hostname: page-component-784d4fb959-hmhk7 Total loading time: 0 Render date: 2025-07-15T07:59:51.722Z Has data issue: false hasContentIssue false
  • English
  • Français

The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science

Published online by Cambridge University Press:  25 August 2011

Nick Chater ,
Noah Goodman ,
Thomas L. Griffiths ,
Charles Kemp ,
Mike Oaksford  and
Joshua B. Tenenbaum
Nick Chater
Affiliation:
Behavioural Science Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom. Nick.chater@wbs.ac.ukhttp://www.wbs.ac.uk/faculty/members/Nick/Chater
Noah Goodman
Affiliation:
Department of Psychology, Stanford University, Stanford, CA 94305. ngoodman@stanford.eduhttp://stanford.edu/~ngoodman/
Thomas L. Griffiths
Affiliation:
Department of Psychology, University of California, Berkeley, CA 94720-1650. tom_griffiths@berkeley.eduhttp://psychology.berkeley.edu/faculty/profiles/tgriffiths.html
Charles Kemp
Affiliation:
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213. ckemp@cmu.eduhttp://www.charleskemp.com
Mike Oaksford
Affiliation:
Department of Psychological Sciences, Birkbeck College, University of London, London WC1E 7HX, United Kingdom. m.oaksford@bbk.ac.ukhttp://www.bbk.ac.uk/psychology/our-staff/academic/mike-oaksford
Joshua B. Tenenbaum
Affiliation:
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139. jbt@mit.eduhttp://web.mit.edu/cocosci/josh.html
Get access Rights & Permissions [Opens in a new window]

Abstract

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 34 , Issue 4 , August 2011 , pp. 194 - 196
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.)

Article purchase

Temporarily unavailable

References

Ali, N., Chater, N. & Oaksford, M. (2011) The mental representation of causal conditional reasoning: Mental models or causal models. Cognition 119:403–18.CrossRefGoogle ScholarPubMed
Ali, N., Schlottmann, A., Shaw, C., Chater, N. & Oaksford, M. (2010) Conditionals and causal discounting in children. In: Cognition and conditionals: Probability and logic in human thinking, ed. Oaksford, M. & Chater, N., pp. 117–34. Oxford University Press.CrossRefGoogle Scholar
Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Anderson, J. R. (1991a) Is human cognition adaptive? Behavioral and Brain Sciences 14:471–517.CrossRefGoogle Scholar
Bishop, C. M. (1996) Neural networks for pattern recognition. Oxford University Press.Google Scholar
Chater, N. & Oaksford, M. (1990) Autonomy, implementation and cognitive architecture: A reply to Fodor and Pylyshyn. Cognition 34:93–107.CrossRefGoogle ScholarPubMed
Chater, N., Oaksford, M., Nakisa, R. & Redington, M. (2003) Fast, frugal, and rational: How rational norms explain behavior. Organizational Behavior and Human Decision Processes 90:63–86.CrossRefGoogle Scholar
Chater, N. & Vitányi, P. (2007) “Ideal learning” of natural language: Positive results about learning from positive evidence. Journal of Mathematical Psychology 51:135–63.CrossRefGoogle Scholar
Courville, A. C., Daw, N. D. & Touretzky, D. S. (2006) Bayesian theories of conditioning in a changing world. Trends in Cognitive Sciences 10:294–300.CrossRefGoogle Scholar
Feldman, J. A. & Ballard, D. H. (1982) Connectionist models and their properties. Cognitive Science 6:205–54.CrossRefGoogle Scholar
Foraker, S., Regier, T., Khetarpal, N., Perfors, A. & Tenenbaum, J. B. (2009) Indirect evidence and the poverty of the stimulus: The case of anaphoric one. Cognitive Science 33:287–300.CrossRefGoogle ScholarPubMed
Garey, M. R. & Johnson, D. S. (1979) Computers and intractability: A guide to the theory of NP-completeness. W. H. Freeman.Google Scholar
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. (2003) Bayesian data analysis, 2nd edition. Chapman and Hall.CrossRefGoogle Scholar
Goodman, N. D., Mansinghka, V. K. & Tenenbaum, J. B. (2007) Learning grounded causal models. In: Proceedings of the 29th Annual Conference of the Cognitive Science Society, ed. McNamara, D. S. & Trafton, G., pp. 305–10. Erlbaum.Google Scholar
Goodman, N. D., Mansinghka, V. K., Roy, D., Bonawitz, K. & Tenenbaum, J. B. (2008a) Church: A language for generative models. In: Proceedings of the Twenty-Fourth Annual Conference on Uncertainty in Artificial Intelligence (UAI), July 9–12, 2008, Helsinki, Finland, ed. McAllester, D. & Myllymaki, P., pp. 220–29. AUAI Press.Google Scholar
Goodman, N. D., Tenenbaum, J. B., Feldman, J. & Griffiths, T. L. (2008b) A rational analysis of rule-based concept learning. Cognitive Science 32(1):108–54.CrossRefGoogle ScholarPubMed
Goodman, N. D., Ullman, T. D. & Tenenbaum, J. B. (2011) Learning a theory of causality. Psychological Review 118:110–19.CrossRefGoogle ScholarPubMed
Gopnik, A., Glymour, C., Sobel, D., Schulz, L., Kushnir, T. & Danks, D. (2004) A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111(1):3–32.CrossRefGoogle ScholarPubMed
Griffiths, T. L. & Ghahramani, Z. (2006) Infinite latent feature models and the Indian buffet process. In: Advances in neural information processing systems, vol. 18, ed. Weiss, J., Schölkopf, B. & Platt, J., pp. 475–82. MIT Press.Google Scholar
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A. & Tenenbaum, J. (2010) Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14(8):357–64.CrossRefGoogle ScholarPubMed
Griffiths, T. L., Kemp, C. & Tenenbaum, J. B. (2008a) Bayesian models of cognition. In: Cambridge handbook of computational psychology, ed. Sun, R., pp. 59–100. Cambridge University Press.Google Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2009) Theory-based causal induction. Psychological Review 116:661–716.CrossRefGoogle ScholarPubMed
Heller, K., Sanborn, A. N. & Chater, N. (2009) Hierarchical learning of dimensional biases in human categorization. In: Advances in neural information processing systems, vol. 22, ed. Lafferty, J. & Williams, C.. pp. 727–35. MIT Press.Google Scholar
Hsu, A. & Chater, N. (2010) The logical problem of language acquisition: A probabilistic perspective. Cognitive Science 34:972–1016.CrossRefGoogle ScholarPubMed
Hsu, A., Chater, N. & Vitányi, P. M. B. (in press) The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis. Cognition.Google Scholar
Johnson, M., Griffiths, T. L. & Goldwater, S. (2007) Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. In: Advances in neural information processing systems, vol. 19, ed. Schölkopf, B., Platt, J. & Hofmann, T., pp. 641–48. MIT Press.Google Scholar
Kamin, L. J. (1969) Predictability, surprise, attention, and conditioning. In: Punishment and aversive behavior, ed. Campbell, B. A. & Church, R. M., pp. 279–96. Appleton-Century-Crofts.Google Scholar
Kemp, C. & Tenenbaum, J. B. (2008) The discovery of structural form. Proceedings of the National Academy of Sciences USA 105:10687–692.CrossRefGoogle ScholarPubMed
Kemp, C. & Tenenbaum, J. B. (2009) Structured statistical models of inductive reasoning. Psychological Review 116:20–58.CrossRefGoogle ScholarPubMed
Kemp, C., Goodman, N. D. & Tenenbaum, J. B. (2010a) Learning to learn causal relations. Cognitive Science 34:1185–1243.CrossRefGoogle Scholar
Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T. & Ueda, N. (2006) Learning systems of concepts with an infinite relational model. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, ed. Cohn, A., pp. 381–88. AAAI Press.Google Scholar
Kemp, C., Tenenbaum, J. B., Niyogi, S. & Griffiths, T. L. (2010b) A probabilistic model of theory formation. Cognition 114(2):165–96.CrossRefGoogle ScholarPubMed
MacKay, D. (2002) Information theory, inference, and learning algorithms. Cambridge University Press.Google Scholar
Manning, C. & Schütze, H. (1999) Foundations of statistical natural language processing. MIT Press.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
McClelland, J. L. (1998) Connectionist models and Bayesian inference. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., pp. 21–53. Oxford University Press.Google Scholar
McClelland, J. L. (2010) Emergence in cognitive science. Topics in Cognitive Science 2:751–70.CrossRefGoogle ScholarPubMed
Neal, R. M. (1992) Connectionist learning of belief networks. Artificial Intelligence 56:71–113.CrossRefGoogle 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., ed. (1998b) Rational models of cognition. Oxford University Press.Google Scholar
Oaksford, M. & Chater, N. (2003) Optimal data selection: Revision, review, and reevaluation. Psychonomic Bulletin and Review 10:289–318.CrossRefGoogle ScholarPubMed
Oaksford, M. & Chater, N. (2010) Conditionals and constraint satisfaction: Reconciling mental models and the probabilistic approach? In: Cognition and conditionals: Probability and logic in human thinking, ed. Oaksford, M. & Chater, N., pp. 309–34. Oxford University Press.CrossRefGoogle Scholar
Pearl, J. (1988) Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.Google Scholar
Perfors, A., Tenenbaum, J. B. & Regier, T. (2011) The learnability of abstract syntactic principles. Cognition 118:306–38.CrossRefGoogle ScholarPubMed
Perfors, A., Tenenbaum, J. & Wonnacott, E. (2010) Variability, negative evidence, and the acquisition of verb argument constructions. Journal of Child Language 37:607–42.CrossRefGoogle ScholarPubMed
Rehder, B. & Burnett, R. (2005) Feature inference and the causal structure of categories. Cognitive Psychology 50:264–314.CrossRefGoogle ScholarPubMed
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., pp. 64–99. Appleton-Century-Crofts.Google Scholar
Russell, S. & Norvig, P. (2011) Artificial intelligence: A modern approach, 3rd edition. Prentice-Hall.Google Scholar
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
Shi, L., Griffiths, T. L., Feldman, N. H. & Sanborn, A. N. (2010) Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin and Review 17:443–64.CrossRefGoogle ScholarPubMed
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science 331(6022):1279–85.CrossRefGoogle Scholar
Vul, E., Goodman, N. D., Griffiths, T. L. & Tenenbaum, J. B. (2009a) One and done: Optimal decisions from very few samples. In: Proceedings of the 31st Annual Meeting of the Cognitive Science Society, ed. Taatgen, N. & van Rijn, H., pp. 148–53. Erlbaum.Google Scholar
Vul, E., Hanus, D. & Kanwisher, N. (2009b) Attention as inference: Selection is probabilistic, responses are all-or-none samples. Journal of Experimental Psychology: General 138:546–60.CrossRefGoogle ScholarPubMed
Walsh, C. R. & Sloman, S. A. (2008) Updating beliefs with causal models: Violations of screening off. In: Memory and Mind: A festschrift for Gordon H. Bower, ed. Gluck, M. A., Anderson, J. R. & Kosslyn, S. M., pp. 345–57. Erlbaum.Google Scholar
Yuille, A. & Kersten, D. (2006) Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences 10:301–308.CrossRefGoogle ScholarPubMed