Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-20T13:39:55.756Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian computation and mechanism: Theoretical pluralism drives scientific emergence

Published online by Cambridge University Press:  25 August 2011

David K. Sewell ,
Daniel R. Little  and
Stephan Lewandowsky
David K. Sewell
Affiliation:
Department of Psychological Sciences, The University of Melbourne, Melbourne, VIC 3010, Australia. dsewell@unimelb.edu.audaniel.little@unimelb.edu.auhttp://www.psych.unimelb.edu.au/people/staff/SewellD.htmlhttp://www.psych.unimelb.edu.au/research/labs/knowlab/index.html
Daniel R. Little
Affiliation:
Department of Psychological Sciences, The University of Melbourne, Melbourne, VIC 3010, Australia. dsewell@unimelb.edu.audaniel.little@unimelb.edu.auhttp://www.psych.unimelb.edu.au/people/staff/SewellD.htmlhttp://www.psych.unimelb.edu.au/research/labs/knowlab/index.html
Stephan Lewandowsky
Affiliation:
School of Psychology, The University of Western Australia, Crawley, WA 6009, Australia. lewan@psy.uwa.edu.auhttp://www.cogsciwa.com/
Get access Rights & Permissions [Opens in a new window]

Abstract

The breadth-first search adopted by Bayesian researchers to map out the conceptual space and identify what the framework can do is beneficial for science and reflective of its collaborative and incremental nature. Theoretical pluralism among researchers facilitates refinement of models within various levels of analysis, which ultimately enables effective cross-talk between different levels of analysis.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 34 , Issue 4 , August 2011 , pp. 212 - 213
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. (1991b) The adaptive nature of human categorization. Psychological Review 98:409–29.Google Scholar
Barrett, H. C. & Kurzban, R. (2006) Modularity in cognition: Framing the debate. Psychological Review 113:628–47.Google Scholar
Dennett, D. C. (1987) The intentional stance. MIT Press.Google Scholar
Feldman, J. A. (2010) Cognitive science should be unified: Comment on Griffiths et al. and McClelland et al. Trends in Cognitive Sciences 14:341.Google Scholar
Fodor, J. A. (1983) The modularity of mind. MIT Press.CrossRefGoogle Scholar
Griffiths, T. L. & Kalish, M. L. (2007) Language evolution by iterated learning with Bayesian agents. Cognitive Science 31:441–80.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.Google Scholar
Kalish, M. L., Griffiths, T. L. & Lewandowsky, S. (2007) Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin and Review 14:288–94.Google Scholar
Kalish, M. L., Lewandowsky, S. & Kruschke, J. K. (2004) Population of linear experts: Knowledge partitioning and function learning. Psychological Review 111:1072–99.CrossRefGoogle ScholarPubMed
Kruschke, J. K. (2006) Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review 113:677–99.CrossRefGoogle ScholarPubMed
Kruschke, J. K. (2008) Bayesian approaches to associative learning: From passive to active learning. Learning and Behavior 36:210–26.Google Scholar
Kruschke, J. K. (2010) Bridging levels of analysis: Comment on McClelland et al. and Griffiths et al. Trends in Cognitive Sciences 14:344–45.CrossRefGoogle ScholarPubMed
Kuhn, T. S. (1970) The structure of scientific revolutions, 2nd edition. University of Chicago Press.Google Scholar
Lewandowsky, S., Griffiths, T. L. & Kalish, M. L. (2009) The wisdom of individuals: Exploring people's knowledge about everyday events using iterated learning. Cognitive Science 33:969–98.CrossRefGoogle ScholarPubMed
Lewandowsky, S., Kalish, M. & Ngang, S. K. (2002) Simplified learning in complex situations: Knowledge partitioning in function learning. Journal of Experimental Psychology: General 131:163–93.Google Scholar
Lewandowsky, S., Roberts, L. & Yang, L.-X. (2006) Knowledge partitioning in categorization: Boundary conditions. Memory and Cognition 34:1676–88.CrossRefGoogle ScholarPubMed
Little, D. R. & Lewandowsky, S. (2009) Beyond nonutilization: Irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance 35:530–50.Google Scholar
Marr, D. (1982/2010) Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman/MIT Press. (Original work published in 1982; 2010 reprint edition by MIT Press).Google Scholar
McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S. & Smith, L. B. (2010) Letting structure emerge: Connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences 14:348–56.Google Scholar
Navarro, D. J. (2010) Learning the context of a category. In: Advances in neural information processing systems, vol. 23, ed. Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R. & Culotta, A., pp. 1795–803. MIT Press.Google Scholar
Neisser, U. (1967) Cognitive psychology. Appleton-Century-Crofts.Google Scholar
Nosofsky, R. M. (1986) Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General 115:39–57.CrossRefGoogle ScholarPubMed
Rumelhart, D. E. & McClelland, J. L. (1985) Levels indeed! A response to Broadbent. Journal of Experimental Psychology: General 114:193–97.CrossRefGoogle 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
Schall, J. D. (2004) On building a bridge between brain and behavior. Annual Review of Psychology 55:23–50.Google Scholar
Sewell, D. K. & Lewandowsky, S. (2011) Restructuring partitioned knowledge: The role of recoordination in category learning. Cognitive Psychology 62:81–122.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
Shiffrin, R. M., Lee, M. D., Kim, W. & Wagenmakers, E. J. (2008) A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science 32:1248–84.Google Scholar
Teller, D. Y. (1984) Linking propositions. Vision Research 10:1233–46.Google Scholar
Thomas, M. S. C. & McClelland, J. L. (2008) Connectionist models of cognition. In: The Cambridge handbook of computational psychology, ed. Sun, R., pp. 23–58. Cambridge University Press.Google Scholar
Yang, L.-X. & Lewandowsky, S. (2003) Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition 29:663–79.Google Scholar
Yang, L.-X. & Lewandowsky, S. (2004) Knowledge partitioning in categorization: Constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, and Cognition 30:1045–64.Google Scholar