title: Perceptual Decision-Making as Probabilistic Inference by Neural Sampling.
abstract: We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a 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 new predictions and characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making.
authors.affiliation: Brain & Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA. Electronic address: ralf.haefner@gmail.com.
authors.forename: Ralf M
authors.initials: RM
authors.lastname: Haefner
authors.affiliation: Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University, Waltham, MA 02454, USA.
authors.forename: Pietro
authors.initials: P
authors.lastname: Berkes
authors.affiliation: Department of Cognitive Science, Central European University, Budapest 1051, Hungary.
authors.forename: József
authors.initials: J
authors.lastname: Fiser
mesh_terms: D002540:Cerebral Cortex / Q000502:physiology; D002755:Choice Behavior / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D010465:Perception / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D011984:Sensory Receptor Cells / Q000502:physiology*