Neural Arbitration between Social and Individual Learning Systems

Decision making requires integrating self-gathered information with advice
from others. However, the arbitration process by which one source of
information is selected over the other has not been fully elucidated. In
this study, we formalised arbitration as the relative precision of
predictions, afforded by each learning system, using hierarchical Bayesian
modelling. 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 relative precision definition of arbitration. Functional neuroimaging
demonstrated arbitration signals that were independent of decision
confidence and involved modality-specific brain regions. Arbitrating in
favour of self-gathered information activated the dorsolateral prefrontal
cortex and the midbrain, whereas arbitrating in favour 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 behavioural and
neural levels.