Cognitive effort for self, strangers, and charities
Bibliographic record
Abstract
Effort is aversive and often avoided, even when earning benefits for oneself. Yet, people sometimes work hard for others. How do people decide who is worth their effort? Prior work shows people avoid physical effort for strangers relative to themselves, but invest more physical effort for charity. Here, we find that people avoid cognitive effort for others relative to themselves, even when the cause is a personally meaningful charity. In two studies, participants repeatedly decided whether to invest cognitive effort to gain financial rewards for themselves and others. In Study 1, participants (N = 51; 150 choices) were less willing to invest cognitive effort for a charity than themselves. In Study 2, participants (N = 47; 225 choices) were more willing to work cognitively for a charity than an intragroup stranger, but again preferred cognitive exertion that benefited themselves. Computational modeling suggests that, unlike prior physical effort findings, cognitive effort discounted the subjective value of rewards linearly. Exploratory machine learning analyses suggest that people who represented others more similarly to themselves were more willing to invest effort on their behalf, opening up new avenues 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".