大脑对社会知识的广泛表征

Daniel Alcalá-López, Ning Mei, Pedro Margolles, David Soto
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引用次数: 0

摘要

了解人脑如何映射社会概念化的不同维度仍然是一个尚未解决的关键问题。我们进行了一项功能性核磁共振成像研究,让参与者接触人格特质的音频定义,并要求他们模拟与这些概念相关的体验。一半的概念是情感性的(如同情心),另一半是非情感性的(如聪明)。与此相对应,一半的概念非常讨人喜欢(如真诚),另一半则不受欢迎(如骗子)。从行为上看,我们发现社会可取性维度比情感维度更能反映被试的主观评价。FMRI 解码结果表明,社会可取性和情感都可以通过分布式脑区的局部活动模式进行解码,这些脑区包括颞上部、额下部、楔前区以及后/前扣带回和腹内侧前额叶皮层默认模式网络的关键节点。对社会可取性的解码准确性优于对情感的解码准确性。表征相似性分析进一步表明,深度语言模型可显著预测颞叶上部和前部双侧区域与概念相关的大脑活动。研究结果表明,社交知识的大脑表征涉及支持多模态社交体验模拟的默认模型网络系统,并进一步依赖于与语言相关的预处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain-wide representation of social knowledge.

Understanding how the human brain maps different dimensions of social conceptualizations remains a key unresolved issue. We performed a functional magnetic resonance imaging (MRI) study in which participants were exposed to audio definitions of personality traits and asked to simulate experiences associated with the concepts. Half of the concepts were affective (e.g. empathetic), and the other half were non-affective (e.g. intelligent). Orthogonally, half of the concepts were highly likable (e.g. sincere) and half were socially undesirable (e.g. liar). Behaviourally, we observed that the dimension of social desirability reflected the participant's subjective ratings better than affect. FMRI decoding results showed that both social desirability and affect could be decoded in local patterns of activity through distributed brain regions including the superior temporal, inferior frontal, precuneus and key nodes of the default mode network in posterior/anterior cingulate and ventromedial prefrontal cortex. Decoding accuracy was better for social desirability than affect. A representational similarity analysis further demonstrated that a deep language model significantly predicted brain activity associated with the concepts in bilateral regions of superior and anterior temporal lobes. The results demonstrate a brain-wide representation of social knowledge, involving default model network systems that support the multimodal simulation of social experience, with a further reliance on language-related preprocessing.

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