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.
View on arXiv@article{lee2025_2408.07237,
title={ Neural embedding of beliefs reveals the role of relative dissonance in human decision-making },
author={ Byunghwee Lee and Rachith Aiyappa and Yong-Yeol Ahn and Haewoon Kwak and Jisun An },
journal={arXiv preprint arXiv:2408.07237},
year={ 2025 }
}