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A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts

Nature Neuroscience volume 18, pages 1509–1517 (2015)Cite this article

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Abstract

Bayesian observer models provide a principled account of the fact that our perception of the world rarely matches physical reality. The standard explanation is that our percepts are biased toward our prior beliefs. However, reported psychophysical data suggest that this view may be simplistic. We propose a new model formulation based on efficient coding that is fully specified for any given natural stimulus distribution. The model makes two new and seemingly anti-Bayesian predictions. First, it predicts that perception is often biased away from an observer's prior beliefs. Second, it predicts that stimulus uncertainty differentially affects perceptual bias depending on whether the uncertainty is induced by internal or external noise. We found that both model predictions match reported perceptual biases in perceived visual orientation and spatial frequency, and were able to explain data that have not been explained before. The model is general and should prove applicable to other perceptual variables and tasks.

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Figure 1: Bayesian observer model constrained by efficient coding.
Figure 2: Prediction 1: Bayesian perception can be biased away from the prior peak.
Figure 3: Prediction 2: stimulus (external) and sensory (internal) noise differentially affect perceptual bias.
Figure 4: Biases in perceived orientation.
Figure 5: Relative biases in perceived orientation.
Figure 6: Biases in perceived spatial frequency.
Figure 7: Predicted biases for different loss functions.
Figure 8: Equivalent efficient neural representations for the same stimulus distribution.

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Acknowledgements

We thank V. DeGardelle, S. Kouider and J. Sackur for providing their data. We also would like to express our gratitude to E. Salinas, J. Gold and D. Swingley for providing valuable feedback on previous versions of the manuscript. The work was supported by the Office of Naval Research (grant N000141110744) and the University of Pennsylvania (including a Benjamin Franklin fellowship to X.-X.W.).

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Authors and Affiliations

  1. Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Xue-Xin Wei & Alan A Stocker

  2. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Alan A Stocker

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  1. Xue-Xin Wei
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Both authors jointly designed and performed the research, and wrote the paper.

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Correspondence to Alan A Stocker.

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Wei, XX., Stocker, A. A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts. Nat Neurosci 18, 1509–1517 (2015). https://doi.org/10.1038/nn.4105

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