Using ecological momentary assessments to evaluate extant measures of mind wandering.
Bibliographic record
Abstract
Mind wandering is a commonly experienced phenomenon. Although self-report measures are available to assess these attentional lapses, examination of their correspondence with the reported frequency of these episodes in daily life is warranted. Using ecological momentary assessments (EMAs), the present study aimed to validate 3 mind-wandering measures: the Mind-Wandering Questionnaire (MWQ) and the Mind Wandering-Spontaneous (MW-S) and Mind Wandering-Deliberate (MW-D) measures in university students (N = 100). Participants completed a series of questionnaires in an in-lab session. Using time-based EMA sampling, participants received 6 prompts via text message daily for 7 days. Each prompt asked students to report if their current thoughts were about something other than what they were doing using a scale ranging from completely on-task to completely on unrelated concerns. Self-report data collected via EMA indicated that reporting more mind-wandering episodes was associated with higher MWQ and MW-S scores but was not significantly correlated with the MW-D score. Results highlight the utility of EMA in validating measures designed to capture mind-wandering episodes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".