Delineating Discrete Generalizable States from Intraindividual Time Series: Towards a Science of Moments

The proposed talk will present a novel approach for uncovering generalizable states via unsupervised clustering techniques applied to time-series data. In two studies, we demonstrate the consistency and validity of the identified classes using diverse methods. This approach can generate a novel “Science of Moments” and identify temporally-embedded psychological constructs.