Do people avoid mental effort after facing a highly demanding task?
Bibliographic record
Abstract
Ego depletion effects are usually examined in a sequential task paradigm in which exerting mental effort in a first task is thought to affect performance on a subsequent self-control task. A so-called ego depletion effect is observed if performance on the second task is impaired for the high demand relative to the low demand group. The present studies take a different approach. Instead of measuring performance in the second task that is equally difficult for all participants, the present studies investigated effects of effortful exertion on the choice to willingly exert effort on a subsequent task. Three pre-registered studies investigated if participants select less effort demanding math problems for upcoming tasks compared to a control group after exerting mental effort in an initial task. Results were mixed. Study 1 (N = 86) revealed no significant effect of mental effort exertion on mean choice difficulty. In Study 2 (N = 269), the expected effect emerged in an exploratory analysis when controlling for math self-assessment, which was robustly associated with the choice measure. Study 3 (N = 330) descriptively, albeit non-significantly replicated this result. An internal random-effects meta-analysis revealed a small overall effect of g = 0.18 when accounting for math self-assessment, albeit with large heterogeneity. Exploratory analyses point to the importance of the subjective experience of mental effort in effort-selection paradigms. We discuss the implications of the small overall effect size for future research and the possibility to examine effort choice in everyday life.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".