In DC Kersten and W Richards (eds.), Perception as Bayesian Inference (pp. 451-455). Cambridge: Cambridge University Press. 1996.
[Commentary on Knill DC, Kersten D, and Mamassian P (1996) "Implications of a Bayesian Formulation of Visual Information for Processing for Psychophysics". In Perception as Bayesian Inference, DC Knill and W Richards, eds.]
As the authors admit, most (if not all) perceptual processes are ill-suited to a "strong" Bayesian approach based on a single consistent model of the world. Instead, they argue for a "weak" variant that assumes Bayesian inference to be carried out in modules of more limited scope. But how weak is "weak"? Are such approaches suitable for only a few relatively low-level tasks, or can they be applied more generally? Could a weak Bayesian approach, for example, explain how we would recognize the return of Elvis Presley?
It is argued here that a weak Bayesian approach is suitable only for a task that avoids ill-defined structures and resource-limited processes, and has well-defined priors that are relatively invariant, at least under some sets of conditions. But many perceptual problems (such as recognizing the return of Elvis Presley) are not of this type, and Bayesian analyses of such tasks therefore cannot succeed.