Neural correlates of auditory distraction revealed in theta-band EEG
Bibliographic record
Abstract
Selective attention involves the exclusion of irrelevant information in order to optimize perception of a single source of sensory input; failure to do so often results in the familiar phenomenon of distraction. The term 'distraction' broadly refers to a perceptual phenomenon. In the present study we attempted to find the electrophysiological correlates of distraction using an auditory discrimination task. EEG and event-related potential responses to identical stimuli were compared under two levels of distraction (continuous broad-band noise or continuous speech). Relative to broad-band noise, the presence of a continuous speech signal in the unattended ear impaired task performance and also attenuated the N1 peak evoked by nontarget stimuli in the attended ear. As the magnitude of a peak in the event-related potential waveform can be modulated by differences in intertrial power but also by differences in the stability of EEG phase across trials, we sought to characterize the effect of distraction on intertrial power and intertrial phase locking around the latency of the N1. The presence of continuous speech resulted in a prominent reduction of theta EEG band intertrial phase locking around the latency of the N1. This suggests that distraction may act not only to disrupt a sensory gain mechanism but also to disrupt the temporal fidelity with which the brain responds to stimulus events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".