Cooperative update of beliefs and state-transition functions in human reinforcement learning

It is widely known that reinforcement learning systems in the brain contribute to learning via interactions with the environment. These systems are capable of solving multidimensional problems, in which some dimensions are relevant to a reward, while others are not. To solve these problems, computational models use Bayesian learning, a strategy supported by behavioral and neural evidence in human. Bayesian learning takes into account beliefs, which represent a learner’s confidence in a particular dimension being relevant to the reward. Beliefs are given as a posterior probability of the state-transition (reward) function that maps the optimal actions to the states in each dimension. However, when it comes to implementing this learning strategy, the order in which beliefs and state-transition functions update remains unclear. The present study investigates this update order using a trial-by-trial analysis of human behavior and electroencephalography signals during a task in which learners have to identify the reward-relevant dimension. Our behavioral and neural results reveal a cooperative update—within 300 ms after the outcome feedback, the state-transition functions are updated, followed by the beliefs for each dimension.