title: The neural basis of following advice.
abstract: Learning by following explicit advice is fundamental for human cultural evolution, yet the neurobiology of adaptive social learning is largely unknown. Here, we used simulations to analyze the adaptive value of social learning mechanisms, computational modeling of behavioral data to describe cognitive mechanisms involved in social learning, and model-based functional magnetic resonance imaging (fMRI) to identify the neurobiological basis of following advice. One-time advice received before learning had a sustained influence on people's learning processes. This was best explained by social learning mechanisms implementing a more positive evaluation of the outcomes from recommended options. Computer simulations showed that this "outcome-bonus" accumulates more rewards than an alternative mechanism implementing higher initial reward expectation for recommended options. fMRI results revealed a neural outcome-bonus signal in the septal area and the left caudate. This neural signal coded rewards in the absence of advice, and crucially, it signaled greater positive rewards for positive and negative feedback after recommended rather than after non-recommended choices. Hence, our results indicate that following advice is intrinsically rewarding. A positive correlation between the model's outcome-bonus parameter and amygdala activity after positive feedback directly relates the computational model to brain activity. These results advance the understanding of social learning by providing a neurobiological account for adaptive learning from advice.
authors.affiliation: Max Planck Institute for Human Development, Berlin, Germany. g.p.biele@psykologi.uio.no
authors.forename: Guido
authors.initials: G
authors.lastname: Biele
authors.forename: Jörg
authors.initials: J
authors.lastname: Rieskamp
authors.forename: Lea K
authors.initials: LK
authors.lastname: Krugel
authors.forename: Hauke R
authors.initials: HR
authors.lastname: Heekeren
mesh_terms: D001921:Brain / Q000502:physiology*; D002755:Choice Behavior; D003198:Computer Simulation; D003657:Decision Making; D005246:Feedback; D006801:Humans; D007858:Learning; D008279:Magnetic Resonance Imaging; D012201:Reward