A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion.

ABSTRACT: Classical decision models assume that the parameters giving rise to choice behavior are stable, yet emerging research suggests these parameters may fluctuate over time. Such fluctuations, observed in neural activity and behavioral strategies, have significant implications for understanding decision-making processes. However, empirical studies on fluctuating human decision-making strategies have been limited due to the extensive data requirements for estimating these fluctuations. Here, we introduce hMFC (Hierarchical Model for Fluctuations in Criterion), a Bayesian framework designed to estimate slow fluctuations in the decision criterion from limited data. We first showcase the importance of considering fluctuations in decision criterion: incorrectly assuming a stable criterion gives rise

SUBMITTER: Vloeberghs R 

PROVIDER: S-EPMC12367131 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature