Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions.

ABSTRACT: Electrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment. Here we show that an attractor neural network model quantitatively reproduces, for each participant, the relations between accuracy, response times and confidence. We show that the attractor neural n

SUBMITTER: Berlemont K 

PROVIDER: S-EPMC7224191 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature