Learning Signals Within an Iterated Social Exchange

描述(由申请者提供)：信任和互惠是关键的社会信号，能够促进同种人之间的合作。这一建议建立在初步工作的基础上，该研究发现，表示合作的行为，如交换中的慷慨，会在相同的纹状体结构中引发活动，这些纹状体结构对初级奖励做出反应。目前的工作将探索计算和神经机制，通过这些机制，社会学习在重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一社会困境，并用于指导在迭代交换期间获得的功能磁共振成像数据的分析。研究的具体目的将是在10轮信任交换的背景下，确定背侧纹状体的活动是否反映了“预测”或“预测错误”信号--这是开发涉及正常和异常社会学习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.