Topic: Two-step Markov Decision Task

Reinforcement Learning algorithms allow agents to learn the probabilistic structure of the environment. These structures, termed belief-states, predict future outcomes and guide choice policies to maximize rewards. The ability to utilize new evidence to update belief-states is vital for adaptive behaviors, a process often impaired in schizophrenia contributing to psychosis, cognitive deficits, and avolition.A critical challenge persists in determining the underlying neuronal mechanisms governing belief-state updating and its impact on learning and reasoning. To address this, we train wildtype-