Learning Signals Within an Iterated Social Exchange

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DESCRIPTION (provided by applicant): Trust and reciprocity are critical social signals enabling the development of cooperation among conspecifics. This proposal builds on preliminary work that has found that behaviors signaling cooperation, such as generosity in an exchange, elicit activity in the same striatal structures that are responsive to primary rewards. The current work will explore the computational and neural mechanisms through which social learning occurs within iterated exchanges of trust. First, a prediction error model of reinforcement learning will be adapted for this social dilemma and used to guide the analysis of functional magnetic resonance imaging data acquired during an iterated exchange. The specific aim will be to identify whether activity in the dorsal striatum reflects 'predictions' or 'prediction error' signals within the context of a 10-round exchange of trust -- a critical distinction for developing a computational account of the neural mechanisms involved in normative and aberrant social learning. Second, this work will be devoted to the development of novel analytic methods, including cross-brain independent component analysis, for use in identifying neural representations of shared social models.