Neurocomputational mechanisms involved in adaptation to fluctuating intentions of others

Rémi Philippe, Rémi Janet, Koosha Khalvati, Rajesh P. N. Rao, Daeyeol Lee and Jean-Claude Dreher ()
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Rémi Philippe: UMR5229, Neuroeconomics, reward, and decision making laboratory
Rémi Janet: UMR5229, Neuroeconomics, reward, and decision making laboratory
Koosha Khalvati: University of Washington
Rajesh P. N. Rao: University of Washington
Daeyeol Lee: Johns Hopkins University
Jean-Claude Dreher: UMR5229, Neuroeconomics, reward, and decision making laboratory

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Humans frequently interact with agents whose intentions can fluctuate between competition and cooperation over time. It is unclear how the brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Here, we use model-based fMRI and a task in which participants thought they were playing with another player. In fact, they played with an algorithm that alternated without signaling between cooperative and competitive strategies. We show that a neurocomputational mechanism with arbitration between competitive and cooperative experts outperforms other learning models in predicting choice behavior. At the brain level, the fMRI results show that the ventral striatum and ventromedial prefrontal cortex track the difference of reliability between these experts. When attributing competitive intentions, we find increased coupling between these regions and a network that distinguishes prediction errors related to competition and cooperation. These findings provide a neurocomputational account of how the brain arbitrates dynamically between cooperative and competitive intentions when making adaptive social decisions.

Date: 2024
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DOI: 10.1038/s41467-024-47491-2

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