Revealing Individual Differences in the Iowa Gambling Task

The Iowa Gambling Task (IGT) is a well–studied experimental paradigm known to simulate both intact and impaired real-world decision making in choice tasks that involve uncertain payoffs. Prior work has used computational reinforcement learning models to successfully reproduce a range of task phenomena. In this prior work a set of models were fit to individual decision making data, the best-fit models were selected based on group-averaged metrics, and then theoretical conclusions were drawn based on group-averaged parameters. In the present work we investigate the performance of this class of reinforcement learning models in fitting individual data. This class of learning models has provided a useful starting point for characterizing decision making performance in the aggregate. However, we demonstrate that no one best-fit model aptly captures individual differences and our results caution against using aggregate parameters from best fit models to characterize decision making across populations as has been done in prior work