Bayesian statistics has been a successful and principled model framework for explaining human behavior in perceptual and cognitive inference tasks. However, in many cases it has been difficult to convincingly justify the choices of the model parameters (i.e. the likelihood functions and prior beliefs) needed to explain the data. Stocker will demonstrate how they used the efficient coding hypothesis to derive a new and better constrained formulation of the Bayesian observer model. The new model makes a set of rather surprising and counter-intuitive predictions that, however, are supported both by neural and psychophysical data. Stocker will discuss the general implications of the new framework for our understanding of perceptual inference.

1. X.-X. Wei and A.A. Stocker. A Bayesian observer model constrained by efficient coding can explain "anti-Bayesian" percepts. Nature Neuroscience, 2015.
2. X.-X. Wei and A.A. Stocker. Lawful relation between perceptual bias and discriminability. Proc. of National Academy of Sciences, U.S.A., 2017.  

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