Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-09T07:26:29.291Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian statistics to test Bayes optimality

Published online by Cambridge University Press:  10 January 2019

Brandon M. Turner Open the ORCID record for Brandon M. Turner [Opens in a new window] ,
James L. McClelland  and
Jerome Busemeyer
Brandon M. Turner
Affiliation:
Psychology Department, The Ohio State University, Columbus, OH 43210. [email protected]://turner-mbcn.com/
James L. McClelland
Affiliation:
Psychology Department, Stanford University, Stanford, CA 94305. [email protected]://stanford.edu/~jlmcc/
Jerome Busemeyer
Affiliation:
Psychology Department, Indiana University, Bloomington, IN 47405. [email protected]://mypage.iu.edu/~jbusemey/home.html
Get access Rights & Permissions [Opens in a new window]

Abstract

We agree with the authors that putting forward specific models and examining their agreement with experimental data are the best approach for understanding the nature of decision making. Although the authors only consider the likelihood function, prior, cost function, and decision rule (LPCD) framework, other choices are available. Bayesian statistics can be used to estimate essential parameters and assess the degree of optimality.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e246
Copyright
Copyright © Cambridge University Press 2018 

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. (1991) The adaptive nature of human categorization. Psychological Review 98:409–29.Google Scholar
Hotaling, J. M., Cohen, A. L., Shiffrin, R. M. & Busemeyer, J. R. (2015) The dilution effect and information integration in perceptual decision making. PLoS ONE 10(9):e0138481. Available at: https://doi.org/10.1371/journal.pone.0138481.Google Scholar
McClelland, J. L. (2013) Integrating probabilistic models of perception and interactive neural networks: A historical and tutorial review. Frontiers in Psychology 4:503.Google Scholar
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 4:1144–67.Google Scholar
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.Google Scholar
Turner, B. M. (under review) Toward a common representational framework for adaptation.Google Scholar
Turner, B. M., Gao, J., Koenig, S., Palfy, D. & McClelland, J. L. (2017) The dynamics of multimodal integration: The averaging diffusion model. Psychonomic Bulletin and Review 24:1819–43.Google Scholar
Turner, B. M. & Van Zandt, T. (2014) Hierarchical approximate Bayesian computation. Psychometrika 79:185–209.Google Scholar
Turner, B. M., Van Zandt, T. & Brown, S. (2011) A dynamic, stimulus-driven model of signal detection. Psychological Review 118:583–613.Google Scholar