Spontaneous Thought Dynamics as a Signature of Positive and Negative Affectivity

Spontaneous thought, characterized by its dynamic nature, emerges from the ever-evolving internal states of individuals. These dynamics potentially reflect unique individual differences and multiple aspects of mental health. However, the quantitative assessment of these thought patterns has been a challenge, due to a lack of appropriate behavioral tasks and analysis methods. To bridge this gap, we introduced a web-based, free association task, designed to capture and analyze the dynamics of spontaneous thought. Utilizing a novel approach termed Density Map-Based Predictive Modeling, we developed predictive models of positive and negative affectivity based on trial-by-trial spontaneous thought dynamics. These models demonstrated significant and robust prediction accuracy across multiple training and independent datasets (total N = 392). Furthermore, our findings revealed significant correlations between model outputs and inflammatory marker responses, underscoring the significant influences of spontaneous thought dynamics on inflammatory physiology. This study suggests that the dynamics of spontaneous thought can effectively serve as cognitive and affective signatures, offering deeper insights into individual psychological profiles.