### Sensory Adaptation within a Bayesian Framework for Perception

**Alan A Stocker** and

Eero P Simoncelli
NIPS Advances in Neural Information Processing Systems 18, Vancouver Canada,
December 2005, MIT Press, p. 1291-1298

We extend a previously developed Bayesian framework for perception to account for sensory
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 adapting the operational
range of the measurement stage to the input range. We show that this changes the likelihood
function in such a way that the Bayesian estimator model can account for reported perceptual
behavior. In particular, we compare the model's predictions to human motion discrimination data
and demonstrate that the model accounts for the commonly observed perceptual adaptation effects of
repulsion and enhanced discriminability.

reprint (pdf)

#### related publications:

- the first paper that describes how to formulate a bayesian observer model of (speed) perception so
that it can account for the
*complete* psychometric function of a subject performing a typical 2AFC
experiment: NN 2006
(see also NIPS 2004)