Surprise Acts as a Reducer of Outcome Value in Human Reinforcement Learning.

Surprise occurs because of differences between a decision outcome and its predicted outcome (prediction error), regardless of whether the error is positive or negative. It has recently been postulated that surprise affects the reward value of the action outcome; studies have indicated that increasing surprise as an absolute value of prediction error decreases the value of the outcome. However, how surprise affects the value of the outcome and subsequent decision making is unclear. We suggest that, on the assumption that surprise decreases the outcome value, agents will increase their risk-averse choices when an outcome is often surprising. Here, we propose the surprise-sensitive utility model, a reinforcement learning model that states that surprise decreases the outcome value, to explain how surprise affects subsequent decision making. To investigate the properties of the proposed model, we compare the model with previous reinforcement learning models on two probabilistic learning tasks by simulations. As a result, the proposed model explains the risk-averse choices like the previous models, and the risk-averse choices increase as the surprise-based modulation parameter of outcome value increases. We also performed statistical model selection by using two experimental datasets with different tasks. The proposed model fits these datasets better than the other models with the same number of free parameters, indicating that the model can better capture the trial-by-trial dynamics of choice behavior.