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The Iowa gambling task (IGT) has been used in numerous studies, often to examine decision-making performance in different clinical populations. Reinforcement learning (RL) models such as the expectancy valence (EV) model have often been used to characterize choice behavior in this work, and accordingly, parameter differences from these models have been used to examine differences in decision-making processes between different populations. These RL models assume a strategy whereby participants incrementally update the expected rewards for each option and probabilistically select options with higher expected rewards. Here we show that a formal model that assumes a win-stay/lose-shift (WSLS) strategy--which is sensitive only to the outcome of the previous choice--provides the best fit to IGT data from about half of our sample of healthy young adults, and that a prospect valence learning (PVL) model that utilizes a decay reinforcement learning rule provides the best fit to the other half of the data. Further analyses suggested that the better fits of the WSLS model to many participants' data were not due to an enhanced ability of the WSLS model to mimic the RL strategy assumed by the PVL and EV models. These results suggest that WSLS is a common strategy in the IGT and that both heuristic-based and RL-based models should be used to inform decision-making behavior in the IGT and similar choice tasks.

Heterogeneity of strategy use in the Iowa gambling task: A comparison of win-stay/lose-shift and reinforcement learning models