Cooper Jeffrey C, Dunne Simon, Furey Teresa, O'Doherty John P
Department of Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA, Trinity College Institute of Neuroscience and.
Trinity College Institute of Neuroscience and.
Cereb Cortex. 2014 Sep;24(9):2502-11. doi: 10.1093/cercor/bht102. Epub 2013 Apr 18.
Romantic interest or rejection can be powerful incentives not merely for their emotional impact, but for their potential to transform, in a single interaction, what we think we know about another person--or ourselves. Little is known, though, about how the brain computes expectations for, and learns from, real-world romantic signals. In a novel "speed-dating" paradigm, we had participants meet potential romantic partners in a series of 5-min "dates," and decide whether they would be interested in seeing each partner again. Afterward, participants were scanned with functional magnetic resonance imaging while they were told, for the first time, whether that partner was interested in them or rejected them. Expressions of interest and rejection activated regions previously associated with "mentalizing," including the posterior superior temporal sulcus (pSTS) and rostromedial prefrontal cortex (RMPFC); while pSTS responded to differences from the participant's own decision, RMPFC responded to prediction errors from a reinforcement-learning model of personal desirability. Responses in affective regions were also highly sensitive to participants' expectations. Far from being inscrutable, then, responses to romantic expressions seem to involve a quantitative learning process, rooted in distinct sources of expectations, and encoded in neural networks that process both affective value and social beliefs.
浪漫的兴趣或拒绝不仅因其情感影响,还因其在单次互动中改变我们对他人或自己认知的潜力,而成为强大的诱因。然而,关于大脑如何计算对现实世界浪漫信号的期望并从中学习,我们知之甚少。在一种新颖的“速配”范式中,我们让参与者在一系列5分钟的“约会”中与潜在的浪漫伴侣见面,并决定是否有兴趣再次见到每个伴侣。之后,参与者接受功能磁共振成像扫描,同时他们首次被告知该伴侣是否对他们感兴趣或拒绝了他们。兴趣和拒绝的表达激活了先前与“心理化”相关的区域,包括后颞上沟(pSTS)和喙内侧前额叶皮层(RMPFC);虽然pSTS对与参与者自己决定的差异做出反应,但RMPFC对来自个人吸引力强化学习模型的预测误差做出反应。情感区域的反应对参与者的期望也高度敏感。因此,对浪漫表达的反应远非难以理解,似乎涉及一个定量学习过程,植根于不同的期望来源,并编码在处理情感价值和社会信念的神经网络中。