Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-28T15:14:09.449Z Has data issue: false hasContentIssue false
  • English
  • Français

Identifying suboptimalities with factorial model comparison

Published online by Cambridge University Press:  10 January 2019

Wei Ji Ma Open the ORCID record for Wei Ji Ma [Opens in a new window]
Wei Ji Ma*
Affiliation:
Center for Neural Science and Department of Psychology, New York University, New York, NY 10003. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Given the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e234
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

Acerbi, L., Ma, W. J. & Vijayakumar, S. (2014a) A framework for testing identifiability of Bayesian models of perception. Paper presented at Advances in Neural Information Processing Systems 27 (NIPS 2014).Google Scholar
Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6):716–23.Google Scholar
Fisher, R. A. (1926) The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain 33:503–13.Google Scholar
Keshvari, S., van den Berg, R. & Ma, W. J. (2012) Probabilistic computation in human perception under variability in encoding precision. PLoS ONE 7(6):e40216.Google Scholar
Pinto, N., Doukhan, D., DiCarlo, J. J. & Cox, D. D. (2009) A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Computational Biology 5(11):e1000579.Google Scholar
Shen, S. & Ma, W. J. (2016) A detailed comparison of optimality and simplicity in perceptual decision making. Psychological Review 123(4):452–80. Available at: http://www.ncbi.nlm.nih.gov/pubmed/27177259.Google Scholar
Shen, S. & Ma, W. J. (in press) Variable precision in visual perception. Available at: http://psycnet.apa.org/doiLanding?doi=10.1037%2Frev0000128. Psychological Review.Google Scholar
van den Berg, R., Awh, E. & Ma, W. J. (2014) Factorial comparison of working memory models. Psychological Review 121(1):124–49.Google Scholar
Vehtari, A., Gelman, A. & Gabry, J. (2017) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing 27(5):1413–32.Google Scholar
Wagenmakers, E.-J. & Farrell, S. (2004) AIC model selection using Akaike weights. Psychonomic Bulletin & Review 11(1):192–96.Google Scholar