Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-28T00:52:14.576Z Has data issue: false hasContentIssue false
  • English
  • Français

Credo for optimality

Published online by Cambridge University Press:  10 January 2019

Alan A. Stocker Open the ORCID record for Alan A. Stocker [Opens in a new window]
Alan A. Stocker*
Affiliation:
Department of Psychology, University of Pennsylvania, Philadelphia PA 19104. [email protected]://www.sas.upenn.edu/~astocker
Get access Rights & Permissions [Opens in a new window]

Abstract

Optimal or suboptimal, Rahnev & Denison (R&D) rightly argue that this ill-defined distinction is not useful when comparing models of perceptual decision making. However, what they miss is how valuable the focus on optimality has been in deriving these models in the first place. Rather than prematurely abandon the optimality assumption, we should refine this successful normative hypothesis with additional constraints that capture specific limitations of (sensory) information processing in the brain.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e244
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

Barlow, H. B. (1961) Possible principles underlying the transformation of sensory messages. In: Sensory communication, ed. Rosenblith, W. A., pp. 217–34. MIT Press.Google Scholar
de Gardelle, V, Kouider, S. & Sackur, J. (2010) An oblique illusion modulated by visibility: Non-monotonic sensory integration in orientation processing. Journal of Vision 10(10):6.Google Scholar
Ernst, M. O. & Banks, M. S. (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415(6870):429–33. Available at: http://dx.doi.org/10.1038/415429a.Google Scholar
Helmholtz, H. (1867) Handbuch der Physiologischen Optik. Allg. Enzyklopadie der Physik, 9 Bd. Voss.Google Scholar
Jaynes, E. (1957/2003) Probability theory: The logic of science. (Original lectures published 1957). Available at: http://www.med.mcgill.ca/epidemiology/hanley/bios601/GaussianModel/JaynesProbabilityTheory.pdf. Cambridge University Press.Google Scholar
Knill, D. C. & Richards, W., eds. (1996) Perception as Bayesian inference. Cambridge University Press.Google Scholar
Körding, K. P. & Wolpert, D. M. (2004) Bayesian integration in sensorimotor learning. Nature 427(6971):244–47. Available at: http://dx.doi.org/10.1038/nature02169.Google Scholar
Stocker, A. A. & Simoncelli, E. P. (2006a) Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience 9(4):578–85. Available at: http://dx.doi.org/10.1038/nn1669.Google Scholar
Tomassini, A., Morgan, M. J. & Solomon, J. A. (2010) Orientation uncertainty reduces perceived obliquity. Vision Research 50:541–47.Google Scholar
Wei, X.-X. & Stocker, A. A. (2015) A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience 18:1509–17. Available at: http://dx.doi.org/10.1038/nn.4105.Google Scholar
Wei, X. X. & Stocker, A. A. (2017) Lawful relation between perceptual bias and discriminability. Proceedings of the National Academy of Sciences of the United States of America 114(38):10244–49.Google Scholar