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A unifying theory explains seemingly contradictory biases in perceptual estimation

Nature Neuroscience volume 27, pages 793–804 (2024)Cite this article

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Abstract

Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations.

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Fig. 1: Overall theoretical framework.
Fig. 2: Illustration of the P/P ratio rule.
Fig. 3: Bias and variability in orientation estimation.
Fig. 4: Modeling the effect of perceptual learning on motion perception.
Fig. 5: Modeling central tendency effect in numerosity perception and time interval estimation.
Fig. 6: Bias and variability in color hue estimation.
Fig. 7: General insights gained across datasets.

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Data availability

No new experimental datasets were collected in this study. The datasets used in this study were all published previously. Datasets collected by ref. 41 and ref. 37 are publicly available. Requests for other datasets should be directed to the original authors who collected the data.

Code availability

The code, including instructions for using the fitting procedure for the Bayesian modeling developed in the paper, is freely available at https://gitlab.com/m-hahn/unifying-theory-biases.

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Acknowledgements

This research uses data from a number of previously published studies. We would like to thank G.-Y. Bae, V. de Gardelle, N. Gekas, J. Solomon, R. Polania and C. Ruff for sharing their data with us, as well as the authors of several other studies for making their data publicly available. We thank A. Huttenlocher and M. Woodford for helpful discussions, along with B. Geisler, R. Goris, M. Hayhoe, N. Kriegeskorte, K. Kay, S. Gershman, G. de Hollander and L. Colgin for comments on earlier versions of this paper. X.-X.W. is supported by the startup funds provided by The University of Texas at Austin. M.H. gratefully acknowledges Saarland University for providing computing resources.

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Authors and Affiliations

  1. Saarland University, Saarbrücken, Germany

    Michael Hahn

  2. Department of Neuroscience, Department of Psychology, Center for Perceptual Systems, Center for Learning and Memory, Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, TX, USA

    Xue-Xin Wei

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  1. Michael Hahn
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M.H. and X.-X.W. conceived and designed the research. M.H. and X.-X.W. developed the theoretical framework. M.H. performed the theoretical, numerical and data analyses, with input from X.X.W. M.H. and X.-X.W. interpreted the results and wrote the paper.

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Correspondence to Michael Hahn or Xue-Xin Wei.

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Hahn, M., Wei, XX. A unifying theory explains seemingly contradictory biases in perceptual estimation. Nat Neurosci 27, 793–804 (2024). https://doi.org/10.1038/s41593-024-01574-x

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