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Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants ( r = 0.53 ± 0.08 ). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants ( r = 0.87 ± 0.04 ). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions. [ABSTRACT FROM AUTHOR]

Interruption generally has a negative effect on performance by affecting working memory (WM). However, the neural mechanism of interruption has yet to be understood clearly, and previous studies have largely ignored the role of fatigue state. To address these issues, the present study explores the behavioral and electrophysiological effects of interruption on WM performance using electroencephalography (EEG) data. The moderating effect of fatigue is also explored. The participants performed spatial 2-back tasks with math task interruption, suspension interruption, and non-interruption under different fatigue states. The results show that interruption led to increased alpha activity and P300 amplitude, indicating inhibitory control to interference from irrelevant information. Analysis of P200 amplitude revealed that interruption affected attentional reallocation when resuming the primary task. Increased theta power indicated an increased demand for information maintenance during the interruption. A speeding-up effect was discovered after interruption; however, fatigue impaired cognitive ability and further exacerbated the negative effects of interruption on WM and behavioral performance. These findings contribute to a better understanding of cognitive activity during the interruption and of the interaction with fatigue, and provide further support for the theory of memory for goals (MFG). [ABSTRACT FROM AUTHOR]