Reconciling neurocognitive and behavioral impulsivities through ecological assessment and multivariate modelling of cognitive control dynamics

Impulsivity is a core dimension of ADHD and a transdiagnostic vulnerability factor for a wide range of adverse psychiatric and somatic outcomes, that could be mitigated through more effective screening of at-risk individuals. However, laboratory-based measures of impulsivity show weak convergence across paradigms and limited prediction of real-world behavior, constraining their utility. We tested whether combining repeated ecological assessment with computational modeling of response-time (RT) dynamics improves measurement of impulsivity and its cross-paradigm validity. Sixty participants, including adolescents with ADHD, individuals with 22q11.2 deletion syndrome, and healthy controls, completed a total of 1347 smartphone-based Balloon-Analogue-Risk-Task (D-BART) assessments repeatedly in daily life, alongside a single-session Conners CPT-3. RT was modeled using linear mixed-effects models as a function of objective risk and subjective uncertainty, with random effects capturing between- and within-person variability. Dynamic RT parameters were integrated with conventional performance metrics and related to CPT-3 variables using partial least squares analysis. External validity was evaluated against parent-rated behavioral symptoms. RT increased with both risk and uncertainty, consistent with adaptive modulation of speed-accuracy trade-offs. These effects varied substantially across individuals and repeated assessments. Dynamic RT parameters differentiated clinical from control participants, whereas traditional aggregate metrics did not. A PLS latent component linked D-BART and CPT-3 patterns and was associated with real-world hyperactivity/impulsivity, whereas CPT-3-derived scores alone were not. Experimental manipulation of ecological sampling density directly impacted D-BART predictive accuracy. These findings show that ecological repetition combined with parsimonious RT-dynamics modeling enhances construct validity, cross-paradigm convergence, and behavioral relevance of impulsivity measures, providing a scalable framework for capturing dynamic cognitive-control processes.