A multi-brain mechanism for observational threat learning

Abstract

Abstract Survival and adaptation in environments require swift and efficacious learning about what is dangerous. Across species, much of such threat learning is acquired socially, for example, through the observation of others’ (“demonstrators’”) defensive behaviors. However, the specific mechanisms responsible for the integration of information flowing between demonstrators and observers remain largely unknown. We addressed this dearth of knowledge by sequentially performing magnetoencephalography (MEG) imaging in demonstrator-observer dyads: a set of stimuli were first shown to a demonstrator whose defensive responses were filmed and later presented to an observer, with neuronal activity recorded from both individuals. Observers exhibited successful learning, as revealed by physiological responses and computational modeling. Sensor- and source-level results consistently demonstrated brain-to-brain coupling (BtBC) within demonstrator-observer dyads. Strikingly, BtBC in the fronto-limbic circuit (including insula, ventromedial and dorsolateral prefrontal cortex) predicted ensuing learning outcomes (i.e., conditioned responses). A machine learning algorithm revealed that the predictive power of BtBC on learning was magnified when a threat was imminent to the demonstrator. BtBC depended on how observers perceived their social status relative to the demonstrator, and was likely to be driven by shared attention and emotion, as bolstered by dyadic pupillary coupling. Taken together, our study describes a multi-brain mechanism for social threat learning, involving BtBC, which reflects social relationships and predicts adaptive learned behaviors.