超越所说的:神经计算是参考通信中的实用推理的基础
Shanshan Zhen1, Mario Martinez-Saito2, Rongjun Yu3
1Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China.
NeuroImage
|January 12, 2025
概括
听众 听众 听众 听众
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 务实推理对于沟通至关重要,它使人们能够理解文字之外的意义.
- 实用推理和个体变化背后的神经计算机制在很大程度上是未知的.
研究的目的:
- 用参考游戏研究实用推理的神经计算基础.
- 为了确定个人层面或人口层面的模型是否更好地预测实用推理表现.
- 在实用推理中识别与贝叶斯信念更新相关的大脑区域和功能连接模式.
主要方法:
- 使用功能磁共振成像 (fMRI) 的参考游戏范式.
- 利用基于模型的fMRI将大脑活动与贝叶斯推理计算相关联.
- 评估了高阶推理能力的个体差异.
主要成果:
- 在预测听众表现方面,个体级务实推理模型显著超过了人口级模型.
- 贝叶斯后部概率与腹中前额叶皮层 (vmPFC) 和腹上条纹体中的活动正相关.
- 观测到的负相关性是背中PFC,前面岛屿 (AI) 和下额头 (IFG);更高阶的推理与IFG/AI激活和vmPFC-AI连接相关.
结论:
- 这项研究提供了一个初步的神经计算模型,用于大脑在实用推理过程中如何表示贝叶斯的信念推理.
- 高阶推理中的个体差异通过特定的神经通路影响实用推理,特别是涉及vmPFC和AI.
- 这些发现突出了实用推理能力中异质性的神经基础.
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