The Predictive Effects of Resting-State and Task-Related Prefrontal and Vagal Activity on Cognitive Performances

Performance efficiency in cognitive tasks is a combination of effectiveness, that is, accuracy, and cognitive effort. Resting-state and task-related autonomic and cortical activity, together with psychological variables, may represent effective predictors of performance efficiency. This study aimed to investigate the impact of these variables in the prediction of performance during a set of cognitive tasks in a sample of young adults. The 76 participants (age: 23.96 +/- 2.69 years; 51.3% females) who volunteered for this study completed several psychological questionnaires and performed a set of attention and executive functions tasks. Resting-state and task-related prefrontal and autonomic activity were collected through a Time-Domain and a Continuous Wave 2-channel Functional Near-Infrared Spectroscopy (fNIRS) and a portable Electrocardiogram (ECG) monitoring system, respectively. A set of Machine Learning (ML) approaches were employed to (i) predict the performance of each cognitive task, while minimizing and quantifying the prediction error, and to (ii) quantitatively evaluate the predictors that most affected the cognitive outcome. Results showed that perfectionistic traits, as well as both resting-state and task-related autonomic and cortical activity, predicted performance for most of the tasks, partially supporting previous evidence. Our results add to the knowledge of psycho-physiological determinants of performance efficiency in cognitive tasks and provide preliminary evidence on the role of ML approaches in detecting important predictors in cognitive neuroscience.