Effort Expenditure Decreases Risk Aversion When Dealing With Gains but Not Losses
Bibliographic record
Abstract
Separate lines of research suggest that people tend to avoid mental effort, but also value it. Evidence for this effort paradox in the same context is scarce. We tested whether people discount effort prior to the investment of effort and value effort following its investment. In three preregistered experiments (total N = 450), participants repeatedly chose between executing a low-effort task for a small reward and a high-effort task for a larger reward. Participants then chose whether or not to gamble with their rewards. As people tend to become more risk averse as subjective value increases, we reasoned participants would be less likely to gamble with rewards the harder they had to work for them. In Studies 1 and 2, we framed the experiment in terms of gaining rewards. In Study 3, we framed the experiment in terms of losing rewards. In all three studies, effort was discounted prospectively, meaning people demanded higher rewards to invest more effort. Contrary to our predictions, we found that people were more likely to gamble with the rewards the more effort it required to obtain them, but only when the rewards were framed in terms of gains (Studies 1 and 2). Collectively, these results suggest that any potential effort paradox is unlikely to occur when people are aware of the association between investing effort and gaining rewards. Our results also imply a novel hypothesis, namely that the aversive feeling accompanying effort might motivate people to engage in risky behavior.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".