Experimental and simulation-based efforts to advance understanding of cognitive, motor, and neural predictors of dual-task performance

Abstract

Dual tasks are characterized by the splitting of attention between tasks and are common in everyday life as well as sports scenarios. These scenarios reflect real-world challenges for populations such as those with injuries, older adults, or athletes. While attention control's role in multitasking is well studied, its relevance in physically demanding dual tasks remains unclear. Alongside attention control ability, neural activation could give insights into the ability to divide attention during these cognitive-motor dual tasks. Understanding what allows some individuals to multitask better than others could inform training, rehabilitation, and performance optimization. Attention control (AC) was assessed through a short test battery and dorsolateral prefrontal cortex (DLPFC) activation was measured during this task. Cognitive-motor multitasking abilities were tested through a challenging dual-task. High DLPFC activation and performance in AC testing correlated with fewer dual-task deficits. The low correlation between AC scores and DLPFC activation shows that they each account for some of the variability in dual-task ability. This suggests that AC performance and neural engagement may be independently important in predicting dual-task function. fNIRS measurements in dynamic settings often shows motion-related artifacts that can obscure neural signals if not addressed. Short-separation channels have been validated as a regression method to remove physiological artifacts such as those related to heartbeat, blood pressure changes, and respiration rate in the scalp, skull, and cerebrospinal fluid. Though effective in stationary settings, their role in reducing motion artifacts from fluid inertia remains unclear. Thus, the ability to reduce motion artifacts through short-separation channel regression remains a critical gap in knowledge. Failure to address this fundamental unknown of fNIRS signal quality limits our confidence in neural activity measurements under dynamic conditions, such as dual tasks. In this study, motion artifacts were simulated at varying frequencies and neural activation strengths. In settings with low contrast-to-noise (i.e. neural signal obscured by motion artifact), short-separation channels showed improved accuracy in identifying true neural signals. Overall, the findings provide new insights into the relationship between attention control, neural activation, and performance in physically challenging dual-task scenarios and expand on the use of short-separation channels during dynamic movements.