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

Kevin Berlemont (1) , Jean-Remy Martin (2) , Jérôme Sackur (3) , Jean-Pierre Nadal (4, 1)

1 Biophysique et Neuroscience Théoriques
2 IJN - Institut Jean-Nicod
3 LSCP - Laboratoire de sciences cognitives et psycholinguistique
4 CAMS - Centre d'Analyse et de Mathématique sociales

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, as well as sequential effects. Our results suggest that a metacognitive process such as confidence in one's decision is linked to the intrinsically nonlinear dynamics of the decision-making neural network.

Mots clés: Decision; Dynamical systems; Network models; Nonlinear phenomena; Decision

Domaines: Neurosciences; Physique [physics]; Neurosciences [q-bio.NC]; Sciences cognitives

Kevin Berlemont, Jean-Remy Martin, Jérôme Sackur, Jean-Pierre Nadal. Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions. Scientific Reports, 2020, 10, pp.7940. ⟨10.1038/s41598-020-63582-8⟩. ⟨hal-02138028v3⟩