September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Psychophysically estimated low-speed prior expectations match the encoding characteristics of neurons in area MT
Author Affiliations & Notes
  • Ling-Qi Zhang
    University of Pennsylvania
  • Alan Stocker
    University of Pennsylvania
  • Footnotes
    Acknowledgements  This work was supported by the University of Pennsylvania.
Journal of Vision September 2021, Vol.21, 2012. doi:https://doi.org/10.1167/jov.21.9.2012
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      Ling-Qi Zhang, Alan Stocker; Psychophysically estimated low-speed prior expectations match the encoding characteristics of neurons in area MT. Journal of Vision 2021;21(9):2012. doi: https://doi.org/10.1167/jov.21.9.2012.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Bayesian inference provides an elegant normative framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the prior beliefs and the likelihood functions have been missing. Here we use assumptions of efficient coding to develop a better constrained Bayesian observer model (Wei & Stocker 2015). In the new model, the stimulus distribution links and jointly constrains both the likelihood function and the prior belief. We fit the model to existing psychophysical speed discrimination data, representing discrimination measurements over a broad range of speeds and stimulus uncertainties. Cross-validation confirms that the model fits the data as well as parametric approximations (Weibull fits) of each psychometric curve. The extracted prior beliefs are closely following a power-law function with an exponent of approximately -1 and are much more consistent across subjects than when extracted with a previous model (Stocker & Simoncelli 2006). Furthermore, the efficient coding assumption of the model also makes the specific prediction that for a power-law prior distribution the neural encoding space should be logarithmic. We tested this prediction by analyzing the speed encoding characteristics of a large population of MT neurons (Nover et al. 2005). We show that the prior predicted by the encoding characteristics (neural prior) very closely matches the low-speed power-law prior extracted from the behavioral data (behavioral prior). Our results demonstrate that a Bayesian observer model constrained by efficient coding not only accurately accounts for the behavioral characteristics of visual speed perception, but also provides a normative account of the logarithm encoding of MT neurons (which gives rise to Weber’s law) as the efficient neural representation of a power-law distributed perceptual variable.

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