Cognitive control capacity as a selective capacity constraint across cognitive control processes: a multimethod...

Cognitive control is characterized by severe capacity limitations, yet it remains unclear whether performance constraints across different cognitive control processes reflect independent limitations or shared capacity-related constraints. To investigate this question, we adopted an individual-differences approach and multimethod analyses to examine how cognitive control capacity (CCC), defined as the maximum rate at which the cognitive control system can successfully process task-relevant information, is associated with performance across multiple cognitive-control processes. A sample of 197 healthy adults completed a modified backward-masking majority function task (MFTM-A) that adaptively estimated each participant's exposure-time threshold at a fixed information load, from which CCC was derived (bits per second). These participants also completed a multi-component battery assessing domain-specific functions including working memory (N-back), inhibition (Go/No-Go, Stop-Signal), flexibility (task switching), proactive/reactive control (AX-CPT) and attentional functions (alerting, orienting and conflict). Results showed that CCC was significantly associated with several cognitive-control measures: higher CCC was associated with greater N-back accuracy, better AX-CPT discriminability, higher No-Go accuracy, and reduced switch costs. Network analysis indicated that proactive control and response inhibition occupied the most central positions in the network, whereas CCC showed selective connections with high-load control measures. A least absolute shrinkage and selection operator (LASSO) regression identified 3-back accuracy as the most robust unique predictor of CCC. These findings suggest that CCC reflects a selective throughput-related constraint that is closely tied to high-load working memory updating and extends to selected control processes requiring sustained and flexible information processing. Methodologically, this study illustrates how network analysis and sparsity-based modelling can be combined to clarify both the organization of cognitive-control measures and the behavioral predictors of CCC.