Going AWOL in the Brain: Mind Wandering Reduces Cortical Analysis of External Events
Bibliographic record
Abstract
Converging evidence from neuroscience suggests that our attention to the outside world waxes and wanes over time. We examined whether these periods of "mind wandering" are associated with reduced cortical analysis of the external environment. Participants performed a sustained attention to response task in which they responded to frequent "nontargets" (digits 0-9) and withheld responses for infrequent "targets" (the letter X). Mind wandering was defined both behaviorally, indicated by a failure to withhold a response to a target, and subjectively, via self-report at a thought probe. The P300 event-related potential component for nontargets was reduced prior to both the behavioral and subjective reports of mind wandering, relative to periods of being "on-task." Regression analysis of P300 amplitude revealed significant common variance between behavioral and subjective markers of mind wandering, suggesting that both markers reflect a common underlying mental state. Finally, control analysis revealed that the effect of mind wandering on the P300 could not be ascribed to changes in motor activity nor was it associated with general arousal. Our data suggest that when trying to engage attention in a sustained manner, the mind will naturally ebb and flow in the depth of cognitive analysis it applies to events in the external environment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".