What Is Ego Depletion? Toward a Mechanistic Revision of the Resource Model of Self-Control
Bibliographic record
Abstract
According to the resource model of self-control, overriding one's predominant response tendencies consumes and temporarily depletes a limited inner resource. Over 100 experiments have lent support to this model of ego depletion by observing that acts of self-control at Time 1 reduce performance on subsequent, seemingly unrelated self-control tasks at Time 2. The time is now ripe, therefore, not only to broaden the scope of the model but to start gaining a precise, mechanistic account of it. Accordingly, in the current article, the authors probe the particular cognitive, affective, and motivational mechanics of self-control and its depletion, asking, "What is ego depletion?" This study proposes a process model of depletion, suggesting that exerting self-control at Time 1 causes temporary shifts in both motivation and attention that undermine self-control at Time 2. The article highlights evidence in support of this model but also highlights where evidence is lacking, thus providing a blueprint for future research. Though the process model of depletion may sacrifice the elegance of the resource metaphor, it paints a more precise picture of ego depletion and suggests several nuanced predictions for future research.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".