The implications of perception as probabilistic inference for correlated
neural variability during behavior
release_h2sr4btjozf6thos73ixv6najm
by
Ralf M. Haefner, Pietro Berkes, József Fiser
2015
Abstract
This paper addresses two main challenges facing systems neuroscience today:
understanding the nature and function of a) cortical feedback between sensory
areas and b) correlated variability. Starting from the old idea of perception
as probabilistic inference, we show how to use knowledge of the psychophysical
task to make easily testable predictions for the impact that feedback signals
have on early sensory representations. Applying our framework to the
well-studied two-alternative forced choice task paradigm, we can explain
multiple empirical findings that have been hard to account for by the
traditional feedforward model of sensory processing, including the
task-dependence of neural response correlations, and the diverging time courses
of choice probabilities and psychophysical kernels. Our model makes a number of
new predictions and, importantly, characterizes a component of correlated
variability that represents task-related information rather than
performance-degrading noise. It also demonstrates a normative way to integrate
sensory and cognitive components into physiologically testable mathematical
models of perceptual decision-making.
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