Neurocomputational mechanisms engaged in detecting cooperative and competitive intentions of others

Abstract

Humans frequently interact with other agents whose intentions can fluctuate over time between competitive and cooperative strategies. How does the brain decide whether the others’ intentions are to cooperate or compete when the nature of the interactions is not explicitly signaled? We used model-based fMRI and a task in which participants thought they were playing with another player. In fact, this agent was an algorithm alternating without signaling between cooperative and competitive strategies. A neurocomputational mechanism underlying arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. The ventral striatum and ventromedial prefrontal cortex tracked the reliability of this arbitration process. When attributing competitive intentions, these regions increased their coupling with a network that distinguish prediction error related to competition versus cooperation. These findings provide a neurocomputational account of how the brain dynamically arbitrates between cooperative and competitive intentions when making adaptive social decisions.

Heterogeneity in strategy use during arbitration between experiential and observational learning

“…In EL, people dynamically arbitrate between model-free and model-based learning 20,21 . In OL, recent evidence suggests a similar arbitration mechanism between imitation -the tendency to repeat other people's choicesand emulationthe tendency to infer their goals 18 , as well as between cooperative and competitive learning during strategic interactions 22 . Yet, whether and how people may engage dynamic arbitration processes across domainsthat is, between EL and OLremains unclear.…”