Timezone: »

A Probabilistic Model of Social Decision Making based on Reward Maximization
Koosha Khalvati · Seongmin A. Park · Jean-Claude Dreher · Rajesh PN Rao

Mon Dec 05 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #42

A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We employ the framework of partially observable Markov decision processes (POMDPs) to model human decision making in a social context, focusing specifically on the volunteer's dilemma in a version of the classic Public Goods Game. We show that the POMDP model explains both the behavior of subjects as well as neural activity recorded using fMRI during the game. The decisions of subjects can be modeled across all trials using two interpretable parameters. Furthermore, the expected reward predicted by the model for each subject was correlated with the activation of brain areas related to reward expectation in social interactions. Our results suggest a probabilistic basis for human social decision making within the framework of expected reward maximization.

Author Information

Koosha Khalvati (University of Washington)
Seongmin A. Park (Cognitive Neuroscience Center)
Jean-Claude Dreher (Centre de Neurosciences Cognitives)
Rajesh PN Rao (University of Washington)

More from the Same Authors