Learning Signals Within an Iterated Social Exchange

描述（由申请人提供）：信任和互惠是关键的社会信号，使同种之间的合作发展。这项提议建立在初步工作的基础上，这些工作发现，传递合作信号的行为，如交换中的慷慨，会引发对初级奖励做出反应的相同纹状体结构的活动。目前的工作将探索计算和神经机制，通过这种机制，社会学习在反复的信任交换中发生。首先，强化学习的预测误差模型将适用于这种社会困境，并用于指导在迭代交换过程中获取的功能磁共振成像数据的分析。具体的目标是确定背侧纹状体的活动是否反映了10轮信任交换的背景下的“预测”或“预测错误”信号-这是开发规范和异常社会学习中所涉及的神经机制的计算帐户的关键区别。其次，这项工作将致力于开发新的分析方法，包括跨脑独立成分分析，用于识别共享的社会模型的神经表征。

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.