Sequential exploration in the Iowa gambling task: Validation of a new computational model in a large dataset of young and old healthy participants

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 sequential exploration in the IGT and we describe a new computational architecture disentangling exploitation, random exploration and sequential exploration in this large population of participants. The new Value plus Sequential Exploration (VSE) architecture provided a better fit than previous models. Parameter recovery, model recovery and simulation analyses confirmed the superiority of the VSE scheme. Furthermore, using the VSE model, we confirmed the existence of a significant reduction in directed exploration across lifespan in the IGT, as previously reported with other paradigms. Finally, we provide a user-friendly toolbox enabling researchers to easily and flexibly fit computational models on the IGT data, hence promoting reanalysis of the numerous datasets acquired in various populations of patients and contributing to the development of computational psychiatry. Author summary The ability to perform decisions and learn from their outcomes is a fundamental function of the central nervous system. In order to maintain their homeostasis and maximize their biological fitness, organisms must maximize rewards and minimize punishments. Yet, pure exploitation often leads to suboptimal solutions. In order to discover the best course of action, organisms must also explore their environment, especially when this environment is complex or volatile. Here, we dissected exploratory strategies in one of the most classic decision-making paradigms of cognitive neuroscience. First, we found that humans tend to sample sequentially the space of possible actions. Second, we developed a new mathematical model better able to predict trial-by-trial choices, by articulating this sequential exploration mechanism with random exploration and exploitation. Third, we showed that sequential exploration reduces across lifespan, a result which might be explained by specific neuroanatomical or neurochemical changes associated with normal aging. Together, these findings may contribute to a better understanding of exploratory behaviors and a better assessment of their disruption in a wide range of neuropsychiatric conditions.