Learning and decision making in monkeys during a rock–paper–scissors game

Game theory provides a solution to the problem of finding a set of optimal decision-making strategies in a group. However, people seldom play such optimal strategies and adjust their strategies based on their experience. Accordingly, many theories postulate a set of variables related to the probabilities of choosing various strategies and describe how such variables are dynamically updated. In reinforcement learning, these value functions are updated based on the outcome of the player's choice, whereas belief learning allows the value functions of all available choices to be updated according to the choices of other players. We investigated the nature of learning process in monkeys playing a competitive game with ternary choices, using a rock–paper–scissors game. During the baseline condition in which the computer selected its targets randomly, each animal displayed biases towards some targets. When the computer exploited the pattern of animal's choice sequence but not its reward history, the animal's choice was still systematically biased by the previous choice of the computer. This bias was reduced when the computer exploited both the choice and reward histories of the animal. Compared to simple models of reinforcement learning or belief learning, these adaptive processes were better described by a model that incorporated the features of both models. These results suggest that stochastic decision-making strategies in primates during social interactions might be adjusted according to both actual and hypothetical payoffs.