Learning and Decision Making in Human During a Game of Matching Pennies

To gain insights into the neural basis of such adaptive decision-making processes, we investigated the nature of learning process in humans playing a competitive game with binary choices, using a matching pennies game. As in reinforcement learning, the subject’s choice during a competitive game was biased by its choice and reward history, as well as by the strategies of its opponent. Furthermore, we recorded event-related brain potentials (ERPs) while subjects played the game to evaluate how neural responses to outcomes related to subsequent decision-making. Analyses of ERP data focused on the feedback-related negativity (FRN), an outcome-locked potential thought to reflect a neural prediction error signal. Consistent with predictions of the reinforcement learning model, we found that the magnitude of ERPs after losing to the computer opponent predicted whether subjects would change decision behavior on the subsequent trial. These findings provide novel evidence that humans engage a reinforcement learning process to adjust representations of competing decision options. But in our experiment, it didn’t show significant difference of ERP or FRN results with the cumulative complex of the game, which indicates that the subject’s decision wasn’t systematically influenced by the cumulative effects of reward history in our experiment