Meta-analysis of aging effects in mind wandering: Methodological and sociodemographic factors.
Bibliographic record
Abstract
Our attention frequently shifts from ongoing tasks to internal content such that we find ourselves mind wandering (MW). Recent research has revealed that increasing age in adulthood is associated with a decrease in the frequency of MW, but the factors that contribute to this age-related effect are unclear. Thus, in the present study we conducted a meta-analysis of age differences in MW that aimed to analyze (a) the size and consistency of the age-related decrease in MW frequency, and (b) the moderating impact of methodological and sociodemographic factors. We analyzed studies comparing MW frequency in healthy younger and older groups and found a large effect in older adults. The age-related decrease in MW was more pronounced for probe compared to self-caught procedures, when task-related interfering thoughts were measured separately, when visual masks were presented, as the proportion of targets increased, and as older participants and fewer women were included in older adult groups. In discussing the theoretical and practical implications of these findings, we highlight the role of motivation and response options and provide recommendations for future research. These included emphasizing the need for open-ended methods in order to avoid bias due to MW instructions. (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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".