Everyday attention and lecture retention: the effects of time, fidgeting, and mind wandering
Bibliographic record
Abstract
We have all had our thoughts wander from the immediate task at hand. The emerging embodied cognition literature emphasizes the role that the body plays in human thought, and raises the possibility that changes in attentional focus may be associated with changes in body behavior. Recent research has found that when individuals view a lecture, mind wandering increases as a function of time. In the present study we asked whether this decline in attention during lecture viewing was associated with fidgeting. Participants were filmed while they watched a 40-min lecture video, and at regular 5-min intervals provided ratings of their attentiveness. Following the lecture, participant's memory for the material was assessed. Fidgeting behavior was coded from video recordings of each session. Results indicated that attention to, and retention of, lecture material declined as a function of time on task. Critically, and as predicted, fidgeting also increased with time on task. We also found that the relation between fidgeting and retention was significant even when the role of attention was factored into the equation, suggesting that fidgeting makes a unique contribution to retention of lecture material over and above that contributed by an individual's attention. We propose a novel non-attentional stress-based account of fidgeting and how this impacts retention for lecture material over and above changes in levels in mind wandering vis-a-vis changes in attention.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".