Bibliographic record
Abstract
Mind wandering, or “daydreaming,” is a shift in the contents of a thought away from a task and/or event in the external environment, to self-generated thoughts and feelings. This research seeks to test the reliability of eye tracking as an objective of measure mind wandering using the Wandering Eye Paradigm, as well as examine the relationships between mind wandering and individual characteristics. Fifty participants will be recruited for two appointments a day apart, on each day on each day completing two eye tracking sessions following a moving target. In this task, participants will be instructed to press the space bar if they feel they are mind wandering, and then answer three questions about their episode content. Questionnaires measuring mind wandering, procrastination, mindfulness, creativity and personality (in particular conscientiousness) will be completed between eye tracking sessions. By comparing the eye tracking data in the period prior to the spacebar press we can determine quantifiable indicators of the onset and duration of mind wandering episodes by analyzing gaze location in relation to the target location. It has been hypothesized that severity of task performance failures (losing track of the target) should correlate with the “depth” of the mind wandering episode content. Additionally, we expect the frequency of mind wandering episodes to correlate with individual characteristics, and that these measures will be consistent across trials. This research would provide a novel objective way to identify and measure mind wandering, and would help further advance the understanding of its behavioral and subjective dimensions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".