Action in auctions: neural and computational mechanisms of bidding behavior

Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behavior plays a major role in social competition. Yet how humans learn to bid efficiently remains an open question. We used model-based neuroimaging to investigate the neural mechanisms of bidding behavior under different types of competition. Twenty-seven subjects (nine male) played a prototypical bidding game: a double action, with three \"market\" types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is \"nudged\" depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behavior best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action-space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.