Behavioral parameters obtained from cognitive control tasks have been linked to electrophysiological markers. Yet, most previous research has investigated only a few specific behavioral parameters at a time. An integrated approach with simultaneous consideration of multiple aspects of behavior may better elucidate the development and function of cognitive control. Here, we aimed to identify shared patterns between cognitive control behavior and electrophysiological markers using stop-signal task data and EEG recordings from an adolescent sample ( n = 193, aged 11–25 years). We extracted behavioral variables covering various aspects of RT, accuracy, inhibition, and decision-making processes, as well as amplitude and latency of the ERPs N1, N2, and P3. To identify shared patterns between the two sets of variables, we employed a principal component analysis and a canonical correlation analysis. First, we replicated previously reported associations between various cognitive control behavioral parameters. Next, results from the canonical correlation analysis showed that overall good task performance was associated with fast and strong neural processing. Furthermore, the canonical correlation was affected by age, indicating that the association varies depending on age. The present study suggests that although distributional and computational methods can be applied to extract specific behavioral parameters, they might not capture specific patterns of cognitive control or electrophysiological brain activity in adolescents.

Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision-making intended to capture processes jointly giving rise to RT distributions and choice data in n -alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of SSMs. In addition, recent work has motivated the combination of SSMs with reinforcement learning models, which had historically been considered in separate literatures. Here, we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user-extensible) wide variety of SSMs and how they can be combined with reinforcement learning models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.