Variability can carry meaningful information beyond average values. In medicine, for example, heart rate variability provides insights that mean heart rate cannot. In cognitive psychology, however, individual differences in variability—particularly in parameters from cognitive process models such as the diffusion decision model (DDM)—have received relatively little attention. A major reason is that trial numbers feasible in individual differences research make DDM variability parameters difficult to estimate reliably. Recent advances at the intersection of Bayesian statistics and machine learning now make it possible to recover these parameters with greater precision. In this work, I draw on hierarchical Bayesian modeling to stabilize individual parameter estimates under low trial counts, and I employ a recently developed superstatistical framework to capture fine-grained, trial-by-trial fluctuations in cognitive processing. Across several existing datasets, I examine whether individual differences in the dynamics of DDM parameters—drift rates, non-decision times, and boundary separations—relate to individual differences in cognitive abilities. The superstatistical approach allows not only the estimation of within-person variability, but also explicit modeling of the temporal structure governing these fluctuations. A nuanced pattern of relationships emerges: variability and dynamic features of DDM parameters provide information that is not captured by mean parameters alone. These findings highlight the promise of using both hierarchical Bayesian estimation and superstatistical modeling to deepen our understanding of cognitive processes and their individual differences.