Abstract

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

中文翻译：

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社会认知和行为的计算建模——强化学习入门

摘要

社会神经科学旨在描述支撑社会认知和行为的神经系统。在过去的十年中，研究人员开始将计算模型与神经成像相结合，将社会计算与大脑联系起来。受强化学习理论方法的启发，强化学习理论描述了结果的意外性如何驱动决策，已经开发了亲社会学习、观察学习、心智化和印象形成的神经基础的解释。在这里，我们为希望在研究中使用这些模型的研究人员提供介绍。我们考虑与其实施相关的理论和实践问题，重点关注该领域的具体例子。