Perceptual decision-making as probabilistic inference by neural sampling
Year: 2014
Type: preprint
Abstract: We present a new framework that combines the idea of perception as probabilistic inference with the recently proposed neural sampling hypothesis to make directly testable neurophysiological and psychophysical predictions. Applying our framework to the well-studied two-alternative forced choice task paradigm, we functionally explain multiple empirical ndings, including task-dependent correlations between neuronal responses, between responses and behavior, and between stimulus and behavior, that have been hard to account for by the traditional feedforward model of sensory processing. The top-down connections required by probabilistic inference in our hierarchical model allow the correlated sensory neural responses to reect task-relevant knowledge without invoking the concept of attention. Our model makes a number of new predictions and characterizes correlated variability as reecting 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. Almost 150 years ago, Helmholtz proposed that visual perception is an inference process 1 . He suggested that at any point in time the brain combines its prior knowledge about the external world with the incoming sensory information to compute the most likely explanation for its inputs. Substantial evidence from both psychophysics 2 and recent physiological
Source: arXiv (Cornell University)
Authors: Ralf M. Haefner, Pietro Berkes, József Fiser
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2950623343