Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T16:06:34.176Z Has data issue: false hasContentIssue false
  • English
  • Français

Relating Bayes to cognitive mechanisms

Published online by Cambridge University Press:  25 August 2011

Mitchell Herschbach  and
William Bechtel
Mitchell Herschbach
Affiliation:
Department of Philosophy, University of California, San Diego, La Jolla, CA 92093-0119. [email protected]://mechanism.ucsd.edu/[email protected]://mechanism.ucsd.edu/~bill
William Bechtel
Affiliation:
Department of Philosophy, University of California, San Diego, La Jolla, CA 92093-0119. [email protected]://mechanism.ucsd.edu/[email protected]://mechanism.ucsd.edu/~bill
Get access Rights & Permissions [Opens in a new window]

Abstract

We support Enlightenment Bayesianism's commitment to grounding Bayesian analysis in empirical details of psychological and neural mechanisms. Recent philosophical accounts of mechanistic science illuminate some of the challenges this approach faces. In particular, mechanistic decomposition of mechanisms into their component parts and operations gives rise to a notion of levels distinct from and more challenging to accommodate than Marr's.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 34 , Issue 4 , August 2011 , pp. 202 - 203
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

Bechtel, W. (2008) Mental mechanisms. Routledge.Google Scholar
Bechtel, W. & Abrahamsen, A. (2005) Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences 36:421–41.Google Scholar
Bechtel, W. & Abrahamsen, A. (2010) Dynamic mechanistic explanation: Computational modeling of circadian rhythms as an exemplar for cognitive science. Studies in History and Philosophy of Science Part A 41:321–33.CrossRefGoogle ScholarPubMed
Bechtel, W. & Richardson, R. C. (1993/2010) Discovering complexity: Decomposition and localization as strategies in scientific research. 1993 edition, Princeton University Press; 2010 edition, MIT Press.Google Scholar
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E. & Pouget, A. (2008) Probabilistic population codes for Bayesian decision making. Neuron 60:1142–52.Google Scholar
Craver, C. F. (2007) Explaining the brain: What a science of the mind-brain could be. Oxford University Press.CrossRefGoogle Scholar
Gigerenzer, G. & Todd, P. M. (1999) Simple heuristics that make us smart. Oxford University Press.Google Scholar
Kelso, J. A. S. (1995) Dynamic patterns: The self-organization of brain and behavior. MIT Press.Google Scholar
Machamer, P., Darden, L. & Craver, C. F. (2000) Thinking about mechanisms. Philosophy of Science 67:1–25.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.Google Scholar
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