Prior Expectations in Visual Speed Perception Predict Encoding Characteristics of Neurons in Area MT

Bayesian inference provides an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate because of its flexibility and the fact that suitable constraints on the structure of the sensory uncertainty have been missing. Here, we demonstrate that a Bayesian observer model constrained by efficient coding not only well explains human visual speed perception but also provides an accurate quantitative account of the tuning characteristics of neurons known for representing visual speed. Specifically, we found that the population coding accuracy for visual speed in area MT (neural prior) is precisely predicted by the power-law, slow-speed prior extracted from fitting the Bayesian observer model to psychophysical data (behavioral prior) to the point that the two priors are indistinguishable in a cross-validation model comparison. Our results demonstrate a quantitative validation of the Bayesian observer model constrained by efficient coding at both the behavioral and neural levels.

Automated summary

Bayesian inference provides an elegant theoretical framework for understanding visual speed perception, but its validation has been challenging due to the lack of constraints on sensory uncertainty. In this study, we demonstrate that a Bayesian observer model constrained by efficient coding can accurately explain human visual speed perception and predict the tuning characteristics of neurons representing visual speed.