Electrophysiological markers of mind wandering: A systematic review
Bibliographic record
Abstract
The ability to mentally wander away from the external environment is a remarkable feature of the human mind. Although recent years have witnessed a surge of interest in examining mind wandering using EEG, there is no comprehensive review that summarizes and accounts for the variable findings. Accordingly, we conducted a systematic review that synthesizes evidence from EEG studies that examined the electrophysiological measures of mind wandering. Our search yielded 42 studies that met eligibility criteria. The reviewed literature converges on a reduction in the amplitude of canonical ERP components (i.e., P1, N1 and P3) as the most reliable markers of mind wandering. Spectral findings were less robust, but point towards greater activity in lower frequency bands, (i.e., delta, theta, and alpha), as well as a decrease in beta band activity, during mind wandering compared to on-task states. The variability in these findings appears to be modulated by the task context. To integrate these findings, we propose an electrophysiological account of mind wandering that explains how the brain supports this inner experience. Conclusions drawn from this work will inform future endeavours in basic science to map out electrophysiological patterns underlying mind wandering and in translational science using EEG to predict the occurrence of this phenomenon.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".