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EEG Decoding of Poor Sustained Attention Beyond Failures of Selective Attention and Task Maintenance
Poster Session F - Tuesday, March 10, 2026, 8:00 – 10:00 am PDT, Fairview/Kitsilano Ballroom
Matthieu Chidharom1 (), Henry Jones1, Edward Vogel1, Monica Rosenberg1; 1University of Chicago
Distraction is a universal aspect of human cognition, yet its neural origins remain unclear. Traditional models of sustained attention attribute distraction to lapses in cognitive control and task-set maintenance, whereas recent data-driven perspectives suggest it may arise from failures in selecting task-relevant information. This study investigated the neural dynamics underlying distraction and tested whether its EEG signature represents a distinct pattern or overlaps with other attentional failures. Twenty adults performed a sustained attention go/no-go task (3,200 trials) requiring focus on either numbers or letters while EEG activity was recorded. Distraction was analyzed both at the trial level (no-go errors) and across broader attentional states defined by reaction-time variability (“in-the-zone” vs. “out-of-the-zone”). In both cases, distraction correlated with reduced event-related potentials, particularly a diminished parietal P3 component, and with weakened large-scale neural coordination revealed by inter-electrode correlation analyses. A machine-learning classifier trained on EEG data successfully decoded attentional state with about 80% accuracy, even after controlling for reaction time, indicating that the signal reflects more than behavioral performance. Additional decoding and representational similarity analyses confirmed that this EEG pattern was distinct from neural markers of selective attention (stimulus-side selection) and task-set control (letter vs. number). Overall, the results identify a consistent electrophysiological signature of distraction that generalizes across measures and tasks, offering new insight into the neural mechanisms that underlie momentary lapses of attention.
Topic Area: ATTENTION: Other
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March 7 – 10, 2026