Technical Program

Paper Detail

Paper: PS-1A.10
Session: Poster Session 1A
Location: H Lichthof
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Anxiety Impedes Adaptive Social Learning Under Uncertainty
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Amrita Lamba, Michael Frank, Oriel FeldmanHall, Brown University, United States
Abstract: Very little is known about how individuals learn under uncertainty in social contexts. Given that social information is especially noisy and ambiguous, we propose that humans are particularly tuned to social uncertainty, which may be exacerbated in those who are uncertainty-sensitive. For example, anxious individuals generally report lower tolerance of uncertainty, which may be further heightened in social contexts. We employed a Bayesian-RL model in a dynamic Trust Game and matched slot machine task to probe reward learning dynamics across social and nonsocial domains. We find healthy subjects are particularly good at learning under negative social uncertainty (e.g., potential monetary losses imposed by others), as this buffers an individual from being exploited, which results in swiftly learning when to stop investing in an exploitative social partner. In contrast, anxious subjects showed equivalent sensitivity for monetary gains across both social and nonsocial contexts and thus sub-optimally overinvested in others. In addition, those with anxiety had difficulty in adjusting their learning rates as the task dynamics shifted. Our results suggest that humans are particularly tuned to negative social uncertainty, which likely facilitates adaptive social learning.