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Neural arbitration between social and individual learning systems

Article by Christoph Mathys et al published in eLife

2020.09.10 | Anne-Mette Pedersen

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Abstract

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Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.

   

Article

 

Diaconescu A., Stecy M., Kasper L., Burke C., Nagy Z., Mathys C. and Tobler P. (2020). Neural arbitration between social and individual learning systems. eLife, Computational And System Biology

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Christoph Mathys, Associate Professor
School of Culture and Society
Interacting Minds Centre

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