Adaptation is a fundamental phenomenon in sensory perception that seems to occur at all processing levels and modalities. A variety of computational principles have been suggested as explanations for adaptation. Many of these are based on the concept of maximizing the sensory information an observer can obtain about a stimulus despite limited sensory resources, which is similar to the concept of redundancy reduction and efficient representation. More mechanistically, adaptation can be interpreted as the attempt of the sensory system to adjusts its limited dynamic range such that it is maximally informative with respect to the statistics of the stimulus. Perceptually, adaptation seems to have two fundamental effects. First, subsequent stimuli are repelled by the adaptor stimulus, i.e. the perceived values of the stimulus variable that is subject to the perception task are more distant to the adaptor value after adaptation. Second, adaptation leeds ot an increase in the observer's discrimination ability around the adaptor value, whereas it decreases further away from the adaptor.

If a Bayesian framework is to provide a valid computational explanation of perceptual processes, then it needs to account for the behavior of a perceptual system, regardless of its adaptation state. So far, it has not been shown how adaptation could be in agreement with a Bayesian framework of perception.

We extend our previously developed Bayesian framework for perception [1] to account for adaptation. We first note that the perceptual effects of adaptation seems inconsistent with an adjustment of the internally represented prior distribution. Instead, we postulate that adaptation increases the signal-to-noise ratio of the measurements by re-allocating the limited resources of the measurement stage to the input range. We show that this re-allocation changes the likelihood function in such a way that the Bayesian estimator model accounts for reported perceptual behavior. In particular, we compare the model's predictions to human motion direction discrimination data and demonstrate that the model well predicts the characteristics of the observed adaption effects of repulsion and changes in discrimination threshold. The proposed model shows for the first time how adaptation can be incorporated into a Bayesian framework of perception. It represents a link between bottom-up processing guided by efficient coding and a top-down Bayesian estimation process.

[1] A.A. Stocker and E.P. Simoncelli. "Constraining a Bayesian Model of Human Visual Speed Perception". In: Advances in Neural Information Processing and Systems NIPS 2004, vol.17, p.1361-1368