Institut des Sciences Cognitives-Marc Jeannerod, UMR5229, Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard Lyon 1, Bron 69675, France
J Neurosci. 2024 Jan 31;44(5):e0250232023. doi: 10.1523/JNEUROSCI.0250-23.2023.
Understanding social interaction requires processing social agents and their relationships. The latest results show that much of this process is visually solved: visual areas can represent multiple people encoding emergent information about their interaction that is not explained by the response to the individuals alone. A neural signature of this process is an increased response in visual areas, to face-to-face (seemingly interacting) people, relative to people presented as unrelated (back-to-back). This effect highlighted a network of visual areas for representing relational information. Using functional MRI, we measured the brain activity of healthy female and male humans (= 42), in response to images of two faces or two (head-blurred) bodies, facing toward or away from each other. Taking the effect as a signature of relation perception, we found that relations between faces and between bodies were coded in distinct areas, mirroring the categorical representation of faces and bodies in the visual cortex. Additional analyses suggest the existence of a third network encoding relations between (nonsocial) objects. Finally, a separate occipitotemporal network showed the generalization of relational information across body, face, and nonsocial object dyads (multivariate pattern classification analysis), revealing shared properties of relations across categories. In sum, beyond single entities, the visual cortex encodes the relations that bind multiple entities into relationships; it does so in a category-selective fashion, thus respecting a general organizing principle of representation in high-level vision. Visual areas encoding visual relational information can reveal the processing of emergent properties of social (and nonsocial) interaction, which trigger inferential processes.
理解社会互动需要处理社会主体及其关系。最新的研究结果表明，这一过程的很大一部分是通过视觉来解决的：视觉区域可以表示多个人，他们对自己互动的新兴信息进行编码，而这些信息不能仅通过对个体的反应来解释。这一过程的神经特征是，与呈现为不相关（背对背）的个体相比，视觉区域对面对面（似乎在互动）的个体的反应增强。这种效应突出了一个用于表示关系信息的视觉区域网络。 使用功能磁共振成像，我们测量了健康女性和男性的大脑活动（= 42 人），以响应两个面孔或两个（头部模糊）身体的图像，这些图像彼此朝向或背向。将效应作为关系感知的特征，我们发现面孔之间和身体之间的关系是在不同的区域编码的，反映了视觉皮层中面孔和身体的分类表示。进一步的分析表明，存在第三个网络编码（非社会性）物体之间的关系。最后，一个单独的枕颞网络显示了关系信息在身体、面孔和非社会对象对之间的泛化（多元模式分类分析），揭示了跨类别关系的共同属性。总之，除了单个实体之外，视觉皮层还编码将多个实体绑定在一起形成关系的关系；它以类别选择性的方式这样做，从而尊重了高级视觉中表示的一般组织原则。编码视觉关系信息的视觉区域可以揭示社会（和非社会）互动的新兴属性的处理，这些属性会引发推理过程。
