Humans face many complex decision-making and learning situations in which the computation of optimal solutions challenges – or even surpasses – cognitive capacities. Therefore, humans often resort to heuristic solutions. Formal models that adequately capture the neuro-cognitive mechanisms of the trade-offs between optimal and heuristic solutions are lacking. Here, I focus on two pertinent scenarios:
First, I will present a series of partly published studies that show how humans combine optimal and heuristic solutions to maximize rewards in multistep decision scenarios. Results obtained from behavioral modelling and functional neuroimaging suggest a role of the medial prefrontal cortex in the computation of the employed policies and of the uncertainty associated with relying on these policies.
Second, I will describe unpublished experiments that outline how humans get to know other persons by updating the estimations of these persons’ character traits. The best-fitting models combine principles derived from reinforcement learning algorithms with participants’ world knowledge about the distributions and interrelations of different character traits. Two functional neuroimaging datasets show that these interrelations between character traits are represented in the medial prefrontal cortex.
Taken together, the to-be-presented projects aim at providing neuro-computational accounts of the trade-offs in complex decision-making and learning processes.