Joiner Jessica, Piva Matthew, Turrin Courtney, Chang Steve W C
1Department of Psychology, Yale University, New Haven, CT 06511 USA.
2Department of Neuroscience, Yale School of Medicine, New Haven, CT 06520 USA.
NPJ Sci Learn. 2017 Jun 16;2:8. doi: 10.1038/s41539-017-0009-2. eCollection 2017.
Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.
了解世界对于生存和成功至关重要。在群居动物中,了解其他个体是在社会环境中生存的必要组成部分,最终有助于提高进化适应性。长期以来,人类和非人类动物如何表征他人的内在状态和经历一直是发展心理学传统领域,以及最近涉及自我与他人的学习和决策研究中备受关注的主题。在这篇综述中,我们探讨心理学如何将表征他人的过程概念化,以及神经科学如何发现强化学习信号的相关因素,以便从表征自我与他人的奖励相关信息的角度来探索社会学习的神经机制。特别是,我们讨论了跨多个脑结构的自我参照和他人参照类型的奖励预测误差,这些误差有效地允许强化学习算法介导社会学习。大脑中基于预测的计算原则在自我参照和他人参照信息之间可能具有显著的保守性。