Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-29T03:08:17.215Z Has data issue: false hasContentIssue false
  • English
  • Français

Although optimal models are useful, optimality claims are not that common

Published online by Cambridge University Press:  10 January 2019

Claire Chambers  and
Konrad Paul Kording Open the ORCID record for Konrad Paul Kording [Opens in a new window]
Claire Chambers
Affiliation:
Department of Bioengineering and Department of Neuroscience, University of Pennsylvania, PA 19104. clairenc@seas.upenn.edukording@seas.upenn.eduhttp://kordinglab.com/
Konrad Paul Kording
Affiliation:
Department of Bioengineering and Department of Neuroscience, University of Pennsylvania, PA 19104. clairenc@seas.upenn.edukording@seas.upenn.eduhttp://kordinglab.com/
Get access Rights & Permissions [Opens in a new window]

Abstract

Rahnev & Denison (R&D) argue that human behavior is often described as “optimal,” despite many previous findings of suboptimality. We address how the literature handles these concepts and discuss our own findings on suboptimality. Although we agree that the field should embrace the “systematic weirdness of human behavior” (sect. 6, para. 1), this does not detract from the value of the Bayesian approach.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e228
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., Vijayakumar, S. & Wolpert, D. M. (2014b) On the origins of suboptimality in human probabilistic inference. PLoS Computational Biology 10(6):e1003661. Available at: https://doi.org/10.1371/journal.pcbi.1003661.Google Scholar
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E. & Pouget, A. (2012) Not noisy, just wrong: The role of suboptimal inference in behavioral variability. Neuron 74(1):30–39. Available at: https://doi.org/10.1016/j.neuron.2012.03.016.Google Scholar
Chambers, C., Fernandes, H. & Kording, K. (2017a) Policies or knowledge: Priors differ between perceptual and sensorimotor tasks. bioRxiv 132829. Available at: https://doi.org/10.1101/132829.Google Scholar
Chambers, C., Sokhey, T., Gaebler-Spira, D. & Kording, K. (2017b) The development of Bayesian integration in sensorimotor estimation. bioRxiv 136267. Available at: https://doi.org/10.1101/136267.Google Scholar
Gigerenzer, G. & Gaissmaier, W. (2011) Heuristic decision making. Annual Review of Psychology 62:451–82. Available at: https://doi.org/10.1146/annurev-psych-120709-145346.Google Scholar
Griffiths, T. L., Chater, N., Norris, D. & Pouget, A. (2012) How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis (2012) Psychological Bulletin 138(3):415–22. Available at: https://doi.org/10.1037/a0026884.Google Scholar
Kahneman, D., Slovic, P. & Tversky, A. (1982a) Judgment under uncertainty. Science 185(4157):1124–31. Available at: https://doi.org/10.1093/oxfordhb/9780195376746.013.0038.Google Scholar
Kersten, D., Mamassian, P. & Yuille, A. (2004) Object perception as Bayesian inference. Annual Review of Psychology 55:271–304.Google Scholar
Körding, K. P. & Wolpert, D. M. (2006) Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences 10(7):319–26. Available at: https://doi.org/10.1016/j.tics.2006.05.003.Google Scholar
Maloney, L. T. & Mamassian, P. (2009) Bayesian decision theory as a model of human visual perception: Testing Bayesian transfer. Visual Neuroscience 26(1):147–55. Available at: https://doi.org/10.1017/S0952523808080905.Google Scholar
Ramachandran, V. (1990) Interactions between motion, depth, color and form: The utilitarian theory of perception. In: Vision: Coding and efficiency, ed. Blakemore, C., pp. 346–60. Cambridge University Press.Google Scholar
Summerfield, C. & Tsetsos, K. (2015) Do humans make good decisions? Trends in Cognitive Sciences 19(1):27–34. Available at: https://doi.org/10.1007/s11103-011-9767-z.Google Scholar
Vilares, I. & Kording, K. (2011) Bayesian models: The structure of the world, uncertainty, behaviour, and then brain. Annals of the New York Academy of Sciences 1224(1):22–39. Available at: https://doi.org/10.1111/j.1749-6632.2011.05965.x.Google Scholar