Brain signal complexity tracks mind-wandering and visual perceptual learning
Bibliographic record
Abstract
Often characterized as thoughts unrelated to the ongoing task, mind-wandering occupies up to 50% of our waking hours and impacts neural and behavioural functioning. Until now, research has focused on the impact of mind-wandering on immediate task performance, but its relationship to neural states that support learning-related gains over time remains unclear. Previous research examining brain signal complexity during task performance showed that periods of mind-wandering were associated with higher signal complexity compared with on-task states, reflecting increased neural flexibility. The primary aim of this study was to investigate whether higher signal complexity associated with mind-wandering may represent a flexible neural state conducive to longer-term learning. Twenty-six adults underwent electroencephalography recording while performing a visual texture discrimination task before and after a training period, with their attention state probed throughout the task. Task performance improved and N1 and P3 event-related potential (ERP) amplitudes were modulated significantly following training (p's < 0.01). Moreover, greater pre- and post-training mind-wandering, better post-training performance, and larger ERP amplitudes were all associated with higher signal complexity (p's < 0.05). Overall, these results suggest that greater engagement in mind-wandering is linked to a high-flexibility brain state that supports longer-term learning in low-level perceptual tasks.
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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 | 0.001 |
| 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".