### Constraining a Bayesian model of orientation perception with efficient coding

**Xue-Xin Wei** and

**Alan A Stocker**
CoSyNe Computational and Systems Neuroscience, Salt Lake City UT,
February 28 - March 03 2013

Poster presentation.

A common challenge for Bayesian models of perceptual behavior is the fact that the two fundamental components of a Bayesian model,
the prior distribution and the likelihood function, are formally unconstrained. Here we argue that a neural system that emulates Bayesian
inference naturally imposes constraints by the way sensory information is represented in populations of neurons. More specifically, we show
how an efficient coding principle can constrain both the likelihood and the prior based on the underlying stimulus distribution. We apply
this idea to the perception of visual orientation and formulate an encoding-decoding model based on a prior distribution that reflects the
statistics of visual orientations in natural scenes. At the behavioral level, our model predicts that perceived orientations are biased away
from the cardinal orientations where the prior distribution peaks. Such biases are seemingly at odds with the traditional Bayesian view that
the prior always biases a percept towards the prior peaks. Yet they are in perfect agreement with recent studies that report perceptual
biases toward the oblique orientations. Our model also correctly predicts the reported relative biases toward the cardinal orientation when
comparing the perceived orientation of a stimulus with low versus a stimulus with high external noise. The model is able to account for both
types of biases because it assumes an efficient neural representation that typically generates asymmetric likelihoods with heavier tails
away from the prior peaks. At the neurophysiological level, the proposed efficient coding principle predicts neural tuning characteristics
that match many aspects of the known orientation tuning properties of neurons in primary visual cortex. Our results suggest that efficient
coding provides a promising constraint for Bayesian models of perceptual inference, and might explain perceptual behaviors that are
otherwise difficult to reconcile with traditional Bayesian approaches.