Lee Byunghwee, Aiyappa Rachith, Ahn Yong-Yeol, Kwak Haewoon, An Jisun
Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
Nat Hum Behav. 2025 Jun 4. doi: 10.1038/s41562-025-02228-z.
Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and has relied heavily on surveys. Here we propose a method to study the nuanced interplay between thousands of beliefs by leveraging online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model. This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance on the basis of the distance between existing and new beliefs. This study demonstrates how large language models, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation.
信念构成了人类认知和决策的基础，引导着我们的行为和社会关系。一个囊括信念及其相互关系的模型对于理解它们对我们行为的影响至关重要。然而，关于信念相互作用的研究往往局限于与特定问题相关的信念，并且严重依赖于调查。在此，我们提出一种方法，通过利用在线用户辩论数据并将信念映射到使用微调后的大语言模型构建的神经嵌入空间，来研究数千种信念之间细微的相互作用。这个信念空间捕捉了社会问题中不同信念的相互联系和两极分化。我们的研究结果表明，这个信念空间中的位置能够预测个体的新信念，并根据现有信念与新信念之间的距离来估计认知失调。这项研究展示了大语言模型与人类信念的集体在线记录相结合，如何能够洞察支配人类信念形成的基本原则。
