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This paper addresses two main challenges facing systems neuroscience today:understanding the nature and function of a) cortical feedback between sensoryareas and b) correlated variability. Starting from the old idea of perceptionas probabilistic inference, we show how to use knowledge of the psychophysicaltask to make easily testable predictions for the impact that feedback signalshave on early sensory representations. Applying our framework to thewell-studied two-alternative forced choice task paradigm, we can explainmultiple empirical findings that have been hard to account for by thetraditional feedforward model of sensory processing, including thetask-dependence of neural response correlations, and the diverging time coursesof choice probabilities and psychophysical kernels. Our model makes a number ofnew predictions and, importantly, characterizes a component of correlatedvariability that represents task-related information rather thanperformance-degrading noise. It also demonstrates a normative way to integratesensory and cognitive components into physiologically testable mathematicalmodels of perceptual decision-making.

The implications of perception as probabilistic inference for correlated neural variability during behavior