Massimo Silvetti

The paper shows that space is a late compensatory mechanism for time representation when non-spatial timekeeping fails, using EEG and computational models.

This paper proposes a meta-Reinforcement Learning framework to unify theories of anterior cingulate cortex function in cognitive control and surprise monitoring, showing it explains neuroimaging data better than previous models.

This study examines how subclinical psychotic symptoms affect delay and effort discounting using behavioral, computational, and electrophysiological methods, finding greater delay discounting and altered dopamine-related effort willingness in high PS participants.

This paper introduces a Reinforcement Meta-Learning framework to model information gathering trade-offs and shows it aligns with neural and behavioral findings on information demand.

This paper introduces a computational model of the dorsal anterior cingulate cortex (dACC) and midbrain circuitry that explains how dACC functions as a reinforcement meta-learner.