A Bayesian Attractor Model for Perceptual Decision Making

Sebastian Bitzer, Jelle Bruineberg and Stefan J Kiebel

PLOS Computational Biology, 2015, vol. 11, issue 8, 1-35

Abstract: Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.Author Summary: How do we decide whether a traffic light signals stop or go? Perceptual decision making research investigates how the brain can make these simple but fundamentally important decisions. Current consensus states that the brain solves this task simply by accumulating sensory information over time to make a decision once enough information has been collected. However, there are important, open questions on how exactly this accumulation mechanism operates. For example, recent experimental evidence suggests that the sensory processing receives feedback about the ongoing decision making while standard models typically do not assume such feedback. It is also an open question how people compute their confidence about their decisions. Furthermore, current decision making models usually consider only a single decision and stop modelling once this decision has been made. However, in our natural environment, people change their decisions, for example when a traffic light changes from green to red. Here, we show that one can explain these three aspects of decision making by combining two already existing modelling techniques. This resulting new model can be used to derive novel testable predictions of how the brain makes perceptual decisions.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004442

DOI: 10.1371/journal.pcbi.1004442

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