Spontaneous Thought Dynamics as a Signature of Positive and Negative Affectivity

Spontaneous thought unfolds as a dynamic stream that reflects continuously changing internal states and is closely tied to individual differences in mental health. However, quantifying thetemporal structure of spontaneous thought remains challenging due to limited behavioral paradigms and analytic tools that preserve its continuous, multidimensional nature. Here, weintroduced Density Map-Based Predictive Modeling (DMPM), a framework that characterizes spontaneous thought dynamics elicited by the Free Association Semantic Task (FAST) withinvalence–self-relevance–time (VST) space. DMPM summarizes each individual’s thought stream using rating and vector density maps that capture both where thoughts concentrate and how theytransition over time. Across multiple independent datasets (total N = 392), DMPM yields robust, generalizable predictions of positive affectivity (PA) and negative affectivity (NA). Feature analysis indicated that affectivity is reflected not only in the content of thoughts, but also in transition dynamics, with self-relevance as a key organizing dimension. Furthermore, higher DMPM-predicted positive affectivity was associated with lower salivary C-reactive protein (CRP), suggesting a link between thought dynamics and inflammation. Together, these findings establish a scalable, interpretable approach for quantifying spontaneous thought dynamics and relating them to transdiagnostic affective traits and potential physiological correlates.