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Sensory adaptation within a Bayesian framework for perception

A A Stocker and E P Simoncelli

Presented at:
Neural Information Processing Systems (NIPS*05)

Published in Adv. Neural Information Processing Systems (NIPS*05), vol.18 pp. 1291--1298, May 2006.
© MIT Press, Cambridge, MA.

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    • 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.
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