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Identifying suboptimalities with factorial model comparison

Published online by Cambridge University Press:  10 January 2019

Wei Ji Ma*
Affiliation:
Center for Neural Science and Department of Psychology, New York University, New York, NY 10003. weijima@nyu.edu

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
Copyright
Copyright © Cambridge University Press 2018 

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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