From ego depletion to self-control fatigue: A review of criticisms along with new perspectives for the investigation and replication of a multicomponent phenomenon
Bibliographic record
Abstract
The replication crisis in psychology has led to question popular phenomena such as ego depletion, which has been criticized after studies failed to replicate. Here, we describe limitations in the literature that contributed to these failures and suggest how they may be addressed. At the theoretical level, the literature focuses on two out of at least eight identified auxiliary hypotheses. Thus, the majority of the hypotheses related to the three core assumptions of the ego-depletion theory have been overlooked, thereby preventing the rejection of the theory as a whole. At the experimental level, we argue that the low replicability of ego-depletion studies could be explained by the absence of a comprehensive, integrative, and falsifiable definition of self-control, which is central to the concept of ego depletion; by an unclear or absent distinction between ego depletion and mental fatigue, two phenomena that rely on different processes; and by the low validity of the tasks used to induce ego depletion. Finally, we make conceptual and methodological suggestions for a more rigorous investigation of ego depletion, discuss the necessity to take into account its dynamic and multicomponent nature, and suggest using the term self-control fatigue instead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".