Do Human Reinforcement Learning Models Account for Key Experimental Choice Patterns in the Iowa Gambling Task

The Iowa Gambling Task (IGT) is widely used to study risky decision-making and learning from rewards and punishments. Although numerous cognitive models have been developed using reinforcement learning frameworks to investigate the processes underlying the IGT, no single model has consistently been identified as superior, largely due to the overlooked importance of model flexibility in capturing choice patterns. This study examines whether human reinforcement learning models adequately capture key experimental choice patterns observed in IGT data.Using simulation and parameter space partitioning methods, we explore the parameter space of two recently introduced models—Outcome-Representation Learning and Value plus Sequential Exploration—alongside four traditional models. We investigate central choice patterns and fit all models by analyzing a comprehensive data pool (N = 1,428) comprising 45 behavioral datasets from both healthy and clinical populations. Applying Akaike and Bayesian information criteria, we find that the Value plus Sequential Exploration model consistently outperforms others across nearly all datasets. These findings suggest that the search for a suitable IGT model may have reached its conclusion, emphasizing the importance of aligning a model’s parameter space with experimentally observed choice patterns for achieving high accuracy in cognitive modeling.