Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

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 representa...

Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates.

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-di...

Decision Making Using Probabilistic Inference Methods

The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental nonnative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to ...

Neural Stochastic Dynamics of Perceptual Decision Making

Recent studies have identified some brain regions involved in the decision process for simple perceptual tasks, and have provided insight into how sensory information is evaluated before the commitment to a motor response. To understand the dynamical principles of the decision process, and how they emerge from the interaction of interconnected neurons, computational models have been proposed ba...

Neural Mechanisms of Perceptual Decision Making