下向和生成计算独特地解释了社交大脑中的神经反应
bioRxiv : the preprint server for biology
|February 27, 2026
概括
社会大脑区域通过自下而上的感知和自上而下的推断来处理交互,挑战以前的等级模型. 图形神经网络和反向规划模型都解释了神经活动,表明了时间而不是空间的专业化.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 从视觉输入的社会评价参与上突 (STS) 和 temporoparietal 结 (TPJ).
- 一个普遍的假设表明一个空间层次结构:后部STS (pSTS) 为自下而上的社会感知和TPJ为自上而下的心态化.
- 这种计算神经映射缺乏经验测试,原因是社会处理的计算模型缺陷.
研究的目的:
- 为了正式测试大脑中社会处理的计算神经映射.
- 将图形神经网络 (GNN) 和反向规划模型与社会交互感知过程中的神经反应进行比较.
- 调查pSTS和TPJ在社会感知和思维中的作用.
主要方法:
- 开发了一个基于关系视觉信息的社交互动识别GNN模型.
- 通过反转代理目标和物理世界模型,开发了一种用于社会互动识别的生成反向规划模型.
- 从观看动画形状视频的参与者收集了fMRI数据,这些动画形状描绘了社会互动,并将神经反应与两种计算模型进行了比较.
主要成果:
- 无论是GNN模型还是反向规划模型,都解释了pSTS和TPJ的神经反应,即使在控制其他模型的差异时也是如此.
- 探索性分析表明,在这些区域内,从早期的感知处理到以后的更高阶推理的时间转移.
- 这表明社会大脑区域的时间层次结构而不是严格的空间层次结构.
结论:
- 社会感知和心智化的区域执行了自下而上的和推理计算的组合,可能是在不同的时间尺度上.
- 挑战了社会大脑中严格的分工假设.
- 提供了一个反向规划模型的第一个神经比较,并证明了认知模型对社会场景的fMRI反应的预测能力.
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