 If we plot the activity of all MT neurons responding to a specific motion stimulus, one hypothesis is that the population response codes the likelihood, \(\pP(r\mid s)\), namely the conditional probability that one has the observed MT response r given the stimulus s. A Bayesian approach to neural population codes then understands neural processing to involve computation of the posterior probability, \(\pP(s\mid r)\) from the likelihood and prior knowledge of the probability of the stimulus \(\pP(s)\) in accordance with Bayes Theorem (this is normalized so that the probabilities sum to one). The details of Bayesian computation need not concern us since our main concern is with the possibility of neural content as probabilistic, something that seems counterintuitive relative to the approach illustrated by Georgopoulos and colleagues.