Neural Arbitration Between Social and Individual Learning Systems
RESEARCH ARTICLE Neural arbitration between social and individual learning systems Andreea Oliviana Diaconescu1,2,3,4†*, Madeline Stecy1,2,5†, Lars Kasper1,2,6, Christopher J Burke2, Zoltan Nagy2, Christoph Mathys1,7,8, Philippe N Tobler2 1Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland; 2Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland; 3University of Basel, Department of Psychiatry (UPK), Basel, Switzerland; 4Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada; 5Rutgers Robert Wood Johnson Medical School, New Brunswick, United States; 6Institute for Biomedical Engineering, MRI Technology Group, ETH Zu¨ rich & University of Zurich, Zurich, Switzerland; 7Interacting Minds Centre, Aarhus University, Aarhus, Denmark; 8Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy Abstract 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.
[Show full text]