Restless mind, restless body.
Bibliographic record
Abstract
In the present work, we investigate the hypothesis that failures of task-related executive control that occur during episodes of mind wandering are associated with an increase in extraneous movements (fidgeting). In 2 studies, we assessed mind wandering using thought probes while participants performed the metronome response task (MRT), which required them to synchronize button presses with tones. Participants performed this task while sitting on a Wii Balance Board providing us with an index of fidgeting. Results of Study 1 demonstrate that relative to on-task periods, mind wandering is indeed accompanied by increases in fidgeting, as well as increased response variability in the MRT. In Study 2, we observed that only deep mind wandering was associated with increases in fidgeting, whereas task-related response variability increased even during mild mind wandering. We interpret these findings in the context of current theories of mind wandering and suggest that (a) mind wandering is associated with costs not only to primary-task performance but also to secondary-task goals (e.g., controlling extraneous movements) and (b) these costs may depend on the degree to which task-related executive control processes are disengaged during mind wandering (i.e., depth of mind wandering).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".