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 relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). 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 based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human decision-making.

翻译：信念构成人类认知与决策的基础，引导着我们的行为和社会联系。一个能够囊括信念及其相互关系的模型对于理解信念如何影响行为至关重要。然而，以往关于信念相互作用的研究往往局限于特定议题相关的信念，并且严重依赖问卷调查。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（LLM）构建的神经嵌入空间中，来研究数千种信念之间微妙的相互作用。该信念空间捕捉了跨越社会议题的多样化信念之间的相互关联性和极化程度。我们的研究结果表明，个体在此信念空间中的位置能够预测其新信念，并能根据现有信念与新信念之间的距离来估计认知失调。本研究展示了LLM与人类信念的集体在线记录相结合，如何能够为揭示支配人类决策的基本原理提供洞见。