In order to address this question we focus on implementation strategies for visual speed perception. The observed contrast dependence of speed perception is in good agreement with a Bayesian observer model that incorporates a statistical prior that visual motion on the retina tends to be slow (Simoncelli 1993; Weiss etal. 2002). We have recently estimated the shapes of the prior on speed and the contrast-dependent likelihood using 2AFC psychophysical speed discrimination experiments (Stocker and Simoncelli, NIPS*04). There is also a wealth of physiological data available from the Medial Temporal (MT) area in monkeys, which is considered to be central for visual motion processing. Assuming that monkey neural responses provide a qualitatively reasonable approximation to their human counterparts, these data allow us to constrain neural implementations and potentially to predict neural response characteristics based on known psychophysics.

We find that all strategies based on an explicit representation of the likelihood function through the population activity of MT neurons seem implausible for implementing a Bayesian motion perception solution. In the Bayesian solution, the likelihood width needs to increase with decreasing stimulus contrast. However, the physiological literature suggests that MT tuning curves do not change with decreasing contrast. This physiological finding also imposes constraints on the implementation of the prior. It rules out strategies that propose a preferred sampling or weighting of low-speed tuned cells, because they also would require a broadening of the tuning curves to account for the perceptual shift. On the other hand, a prior that is explicitly expressed by the responses of a given population of neurons - which has been proposed in other context (Yu and Dayan, 2005) - requires stable and continuous firing rates of those neurons. This seems implausible for a perceptual prior that presumably changes slowly (because of its metabolic costs), and does not appear to be supported by any physiological evidence.

As an alternative, we suggest a different implementation that avoids the explicit representation of likelihood and prior in a population of neurons. The likelihood is implicitly represented, given that the population response of MT cells represents the unbiased measurement and assuming independent Poisson spiking statistics in which the spike rate variance is proportional to the rate. In this way, the likelihood function for a given population response does broaden with decreasing contrast yet the population response remains in complete agreement with known physiology. As for the prior, there are several possibilities of implementation. One is that the prior is imposed by a contrast response function that is non-uniform across different cells, depending on their preferred speed tuning. For example, if the firing rate of cells tuned for low speeds decreases less with decreasing contrast than those tuned for higher speeds, the population mean would shift increasingly towards slower speeds with decreasing contrast. Another possibility is that the prior is imposed by a read-out mechanism that performs a biased normalization of the responses that depends on the preferred speed tuning and the response amplitude. These are testable hypotheses, and we are currently exploring physiological data sets to evaluate their plausibility.

The present study, in combination with previously derived psychophysical and physiological data, indicates that an explicit implementation of the likelihood function and the prior distribution in the primate visual motion area MT seems unlikely, and is not necessary to perform Bayesian inference.