Dynamics and geometry of emotion and cognition: an interpretable model of individual human behavior

Abstract

Affective flexibility-the capacity to flexibly process emotional information-has been assessed with affective task switching, in which response times consistently reveal larger behavioral costs when switching to the affective task. These traditional metrics of task behavior, however, discard the sub-trial dynamics that produce them and are vulnerable to trial-sampling noise. Subject-specific generative models were independently fitted (22 adults) with response-time sequences. The learned dynamics were visualized as continuous trajectories in a labeled embedding that was shared across person-specific models, yielding a suite of interpretable geometric metrics that captured how sub-trial behavior is jointly shaped by experimental conditions and between-subject variability. Switch-repeat onset distance tracked individual differences in switch costs; early- and late-epoch tortuosity provided convergence with classical sub-process models; and angular alignment between trajectories supported a task set flexibility account of asymmetric switch costs. Overall, our approach demonstrates the utility of latent geometry for deriving mechanistically informative measures of individual task behavior.