Robin Vloeberghs

This study shows that trial-to-trial fluctuations in decision criterion can explain sequential effects in decision making, challenging the assumption that these effects are caused by updates to decision parameters.

The paper introduces an autocorrelated Drift Diffusion Model to better account for temporally clustered errors in decision-making tasks, showing it outperforms traditional models.

The paper shows that trial-by-trial changes in decision criteria affect confidence in human decision-making across multiple datasets and measures.

This paper introduces hMFC, a Bayesian model to estimate slow decision criterion fluctuations from limited data, showing it improves accuracy over stable criterion assumptions.