Directed exploration in the Iowa Gambling Task: model-free and model-based analyses in a large dataset of young and old healthy participants

Abstract

The Iowa Gambling Task (IGT) is one of the most common paradigms used to assess decision-making and executive functioning in neurological and psychiatric disorders. Several reinforcement-learning (RL) models were recently proposed to refine the qualitative and quantitative inferences that can be made about these processes based on IGT data. Yet, these models do not account for the complex exploratory patterns which characterize participants behavior in the task. Using a dataset of more than 500 subjects, we demonstrate the existence of such patterns and we describe a new computational architecture (Explore-Exploit) disentangling exploitation, random exploration and directed exploration in this large population of participants. The EE architecture provided a better fit to the choice data on multiple metrics. Parameter recovery and simulation analyses confirmed the superiority of the EE scheme over alternative schemes. Furthermore, using the EE model, we were able to replicate the reduction in directed exploration across lifespan, as previously reported in other paradigms. Finally, we provide a user-friendly toolbox enabling researchers to easily fit computational models on the IGT data, hence promoting reanalysis of the numerous datasets acquired in various populations of patients.