A Modeling Pipeline for Inferring Hidden Rhythms in Behavioral Data: Application to the Death Implicit Association...

Behavioral analyses is often focused on differences in average response time (RT) or accuracy across task conditions, such as conflict factors embedded in a cognitive flexibility task. While average RT can reveal personal traits or variations in cognitive processing across populations, it often fails to capture the rich temporal dynamics and behavioral patterns that might be present in task data. In this study, we present a case where average RT does not effectively distinguish between participant conditions, whereas behavioral patterns across task trials provide meaningful discriminative power. These temporal patterns carry information about individuals in a population that cannot be captured by average RT or accuracy alone. Specifically, we show that in a modified version of the Death Implicit Association Test (DIAT), known as the Brief Death Implicit Association Test (B-DIAT), the average RT per condition, i.e., conflict vs non-conflict conditions, does not effectively capture individual traits. Meanwhile, the temporal patterns extracted from RT reveal unique characteristics of behavior, particularly those associated with suicidal ideation, derived from suicidal index (SI) scores. In this research, we develop a modeling pipeline that facilitates the inference of temporal and dynamical patterns from behavioral data. The proposed pipeline can be applied to RT data across various cognitive tasks to probe the underlying encoding mechanisms, shaping behavioral deviations in a population.