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Research ArticleResearch Article: Methods/New Tools, Novel Tools and Methods

A General Framework for Inferring Bayesian Ideal Observer Models from Psychophysical Data

Tyler S. Manning, Benjamin N. Naecker, Iona R. McLean, Bas Rokers, Jonathan W. Pillow and Emily A. Cooper
eNeuro 31 October 2022, 10 (1) ENEURO.0144-22.2022; https://doi.org/10.1523/ENEURO.0144-22.2022
Tyler S. Manning
1Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, Berkeley, CA 94720
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Benjamin N. Naecker
2Psychology, University of Texas at Austin, Austin, TX 78712
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Iona R. McLean
1Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, Berkeley, CA 94720
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Bas Rokers
3Psychology, New York University–Abu Dhabi, Abu Dhabi, United Arab Emirates
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Jonathan W. Pillow
4Princeton Neuroscience Institute, Department of Psychology, Princeton University, Princeton, NJ 08540
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Emily A. Cooper
5Herbert Wertheim School of Optometry and Vision Science, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720
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Abstract

A central question in neuroscience is how sensory inputs are transformed into percepts. At this point, it is clear that this process is strongly influenced by prior knowledge of the sensory environment. Bayesian ideal observer models provide a useful link between data and theory that can help researchers evaluate how prior knowledge is represented and integrated with incoming sensory information. However, the statistical prior employed by a Bayesian observer cannot be measured directly, and must instead be inferred from behavioral measurements. Here, we review the general problem of inferring priors from psychophysical data, and the simple solution that follows from assuming a prior that is a Gaussian probability distribution. As our understanding of sensory processing advances, however, there is an increasing need for methods to flexibly recover the shape of Bayesian priors that are not well approximated by elementary functions. To address this issue, we describe a novel approach that applies to arbitrary prior shapes, which we parameterize using mixtures of Gaussian distributions. After incorporating a simple approximation, this method produces an analytical solution for psychophysical quantities that can be numerically optimized to recover the shapes of Bayesian priors. This approach offers advantages in flexibility, while still providing an analytical framework for many scenarios. We provide a MATLAB toolbox implementing key computations described herein.

  • ideal observer models
  • perception
  • Bayesian inference

Footnotes

  • The authors declare no competing financial interests.

  • This work was supported by National Institute of Health Grants F32 EY03232 and T32 EY007043 (to T.S.M.); the National Science Foundation Award 2041726 (to E.A.C.); the Aspire Grant VRI20-10 (to B.R.); and the McKnight Scholar’s Award, the Simons Collaboration on the Global Brain (SCGB) Grant AWD543027, and the National Institutes of Health BRAIN Initiative Grant R01EB026946 (to J.W.P.).

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

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A General Framework for Inferring Bayesian Ideal Observer Models from Psychophysical Data
Tyler S. Manning, Benjamin N. Naecker, Iona R. McLean, Bas Rokers, Jonathan W. Pillow, Emily A. Cooper
eNeuro 31 October 2022, 10 (1) ENEURO.0144-22.2022; DOI: 10.1523/ENEURO.0144-22.2022

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A General Framework for Inferring Bayesian Ideal Observer Models from Psychophysical Data
Tyler S. Manning, Benjamin N. Naecker, Iona R. McLean, Bas Rokers, Jonathan W. Pillow, Emily A. Cooper
eNeuro 31 October 2022, 10 (1) ENEURO.0144-22.2022; DOI: 10.1523/ENEURO.0144-22.2022
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Keywords

  • ideal observer models
  • perception
  • Bayesian inference

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