Mind wandering on command
Bibliographic record
Abstract
= 336) examined whether participants can systematically adjust levels of mind wandering on command. Participants performed four blocks of the metronome response task (MRT) in which they pressed a spacebar in sync with a steady audio tone. Levels of spontaneous and deliberate mind wandering were measured using intermittent thought probes. Performance was indexed with MRT response time variability and omission errors. Each block started with instructions to mind wander either 20, 40, 60, or 80% of the time. Analysis was primarily conducted using linear mixed effects models. We found that mind wandering (spontaneous and deliberate), response time variability, and omission errors increased progressively with instructions to mind wander more and that these instruction-related changes were larger for deliberate than spontaneous mind wandering (Experiments 1-3). This pattern held regardless of whether participants' eyes were open or shut (Experiment 2). Relative to a control group receiving no commands to mind wander, instructing people to mind wander 60 or 80% of the time led to more deliberate mind wandering, and strikingly, asking people to mind wander 20% of the time led to less spontaneous mind wandering (Experiment 3). Our results suggest that individuals can titrate mind wandering experiences to roughly match instructed levels indicating that mind wandering can be manipulated through simple instructions. However, other features of the data suggest that such titration is effortful and may come with a cost to performance.
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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 | 0.001 | 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) | 0.020 | 0.002 |
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".