Development of a Within-Subject, Repeated-Measures Ego-Depletion Paradigm
Bibliographic record
Abstract
Abstract. Ego depletion is 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 12 studies (N = 754), participants reliably reported having lower energy and mood after depleting manipulations compared to after recovery manipulations. Depletion manipulations did not consistently affect behavioral self-control, although the depletion 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 takes advantage of within-subject designs across multiple sessions to avoid spillover effects between measurements.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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; 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".