Risky choice and memory for effort: Hard work stands out.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When deciding between different courses of action, both the potential outcomes and the costs of making a choice should be considered. These costs include the cognitive and physical effort of the different options. In many decision contexts, the outcome of the choice is guaranteed but the amount of effort required to achieve that outcome is unknown. Here, we studied choices between options that varied in the riskiness of the effort (number of responses) required. People made repeated choices between pairs of options that required them to click different numbers of sequentially presented response circles. Easy-effort options led to small numbers of response circles, whereas hard-effort options led to larger numbers of response circles. For both easy and hard-effort options, fixed options led to a consistent effort, whereas risky options led to variable effort that, with a 50/50 chance, required either more effort or less effort than the fixed option. Participants who showed a preference for easier over harder options were more risk averse for decisions involving hard options than for decisions involving easy options. On subsequent memory tests, people most readily recalled the hardest outcome, and they overestimated its frequency of occurrence. Memory for the effort associated with each risky option strongly correlated with individual risky preferences for both easy-effort and hard-effort choices. These results suggest a relationship between memory biases and risky choice for effort similar to that found in risky choice for reward. With effort, the hardest work seems to particularly stand out.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it