| Paper: | PS-2B.62 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Bayesian nonparametric models characterize social sensitivity in a competitive dynamic game | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1303-0 | ||
| Authors: | Kelsey McDonald, Scott Huettel, John Pearson, Duke University, United States | ||
| Abstract: | Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices. Yet, most real-world social behaviors involve dynamic, coevolving decisions by interacting agents, which poses challenges for creating tractable models of behavior. We have previously shown that it is possible to quantify the instantaneous dynamic coupling in strategic human game play when paired against both human and artificial opponents. Here, we apply this coupling model to human neuroimaging data. We observe that the rTPJ and dmPFC exhibit increased activation when playing against a human opponent compared to a computer opponent, both immediately before and after game play. Moreover, a network of regions frequently associated with social cognition, including the dlPFC and dmPFC, was found to correlate with player coupling metrics derived from our model for both human and computer opponents. These findings suggest that prefrontal cortex may play a role in tracking the relationship between oneself and other dynamic agents, regardless of whether those agents are perceived to be human. | ||