Signal detection theory, uncertainty, and Poisson-like population codes

The juxtaposition of established signal detection theory models of perception and more recent claims about the encoding of uncertainty in perception is a rich source of confusion Are the latter simply a rehash of the former? Here we make an attempt to distinguish precisely between optimal and probabilistic computation In optimal computation the observer minimizes the expected cost under a posterior probability distribution In probabilistic computation the observer uses higher moments of the likelihood function of the stimulus on a trial-by trial basis Computation can be optimal without being probabilistic and vice versa Most signal detection theory models describe optimal computation Behavioral data only provide evidence for a neural representation of uncertainty if they are best described by a model of probabilistic computation We argue that single-neuron activity sometimes suffices for optimal computation but never for probabilistic computation A population code is needed instead Not every population code is equally suitable because nuisance parameters have to be marginalized out This problem is solved by Poisson-like but not by Gaussian variability Finally we build a dictionary between signal detection theory quantities and Poisson like population quantities (C) 2010 Elsevier Ltd All rights reserved