Development of a Within-Subject, Repeated-Measures Ego Depletion Paradigm: Inconsistent Results and Future Recommendations
Bibliographic record
Abstract
The ego-depletion effect is currently under scrutiny for low replicability, possibly reflecting the limited statistical power available in between-subject designs. In response, we created a within-subject, repeated-measures ego-depletion paradigm that repeatedly alternated depletion and recovery manipulations. Each manipulation was followed by measuring subjective fatigue, mood, and self-control performance. Across twelve studies (N = 754), participants reliably reported having less energy and lower mood after depleting manipulations compared to after recovery manipulations. Depletion manipulations did not consistently affect behavioral self-control, although the small effect was meta-analytically significant (d = .045). Furthermore, unintended fatigue and practice effects occurred over the course of the paradigm, systematically interfering with the intended depletion effects. We recommend that depletion research take advantage of within-subject designs without using repeated-measures.
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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 | 0.361 | 0.462 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".