Kang Hyeran, Kim Kun Il, Kim Jinhee, Kim Hackjin
Laboratory of Social and Decision Neuroscience, Department of Psychology, Korea University, Seoul, Republic of Korea.
Soc Cogn Affect Neurosci. 2025 May 14;20(1). doi: 10.1093/scan/nsaf051.
In human society, successful social interactions often hinge upon the ability to accurately estimate other's perspectives, a skill that necessitates integrating contextual cues. This study investigates the neural mechanism involved in this capacity through a preference estimation task. In this task, participants were presented with the target's face and asked to predict their preference for a given item. Preference estimation accuracy was assessed by calculating the percentage of correct guesses, where participants' responses matched the target's preferences on a 4-point Likert scale. Our research demonstrates that, based on inter-subject representational similarity analysis (IS-RSA), the multi-voxel patterns in the medial prefrontal cortex (mPFC) and the anterior insula (AI) predict individual differences in preference estimation accuracy. Specifically, the varying behavioral tendencies among participants in inferring others' preferences were mirrored in the multivariate neural representations within these regions, both of which are known for their involvement in individual differences in interoception and context-dependent interpretation of ambiguous facial emotion. These findings suggest that mPFC and AI play pivotal roles in accurately estimating others' preferences based on minimal information and provide insights that transcend the limitations of traditional univariate approaches by employing multivariate pattern analysis.
在人类社会中，成功的社交互动往往取决于准确估计他人观点的能力，而这一技能需要整合情境线索。本研究通过一项偏好估计任务来探究参与这一能力的神经机制。在该任务中，向参与者展示目标人物的面部，并要求他们预测目标人物对给定物品的偏好。通过计算正确猜测的百分比来评估偏好估计的准确性，参与者的回答需与目标人物在4点李克特量表上的偏好相匹配。我们的研究表明，基于个体间表征相似性分析（IS-RSA），内侧前额叶皮层（mPFC）和前脑岛（AI）中的多体素模式可预测偏好估计准确性的个体差异。具体而言，参与者在推断他人偏好时不同的行为倾向反映在这些区域内的多变量神经表征中，这两个区域都因参与个体间内感受差异以及对面部模糊情绪的情境依赖性解读而闻名。这些发现表明，mPFC和AI在基于最少信息准确估计他人偏好方面发挥着关键作用，并通过采用多变量模式分析提供了超越传统单变量方法局限性的见解。
