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Abstract

Many studies on human and animals have provided evidence for the contribution of goal-directed and habitual valuation systems in learning and decision-making. These two systems can be modeled using model-based (MB) and model-free (MF) algorithms in Reinforcement Learning (RL) framework. Here, we study the link between the contribution of these two learning systems to behavior and meta-cognitive capabilities. Using computational modeling we showed that in a highly variable environment, where both learning strategies have chance level performances, model-free learning predicts higher confidence in decisions compared to model-based strategy. Our experimental results showed that the subjects’ meta-cognitive ability is negatively correlated with the contribution of model-free system to their behavior while having no correlation with the contribution of model-based system. Over-confidence of the model-free system justifies this counter-intuitive result. This is a new explanation for individual difference in learning style.