Risky Choice and Memory for Effort: Hard Work Stands Out
Bibliographic record
Abstract
When deciding between different courses of action, both the potential outcomes and the costs of making a choice should be considered. To date, most studies of risk-sensitive choice have focused on the probability of different reward amounts. Here we studied choice 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 (low effort) options led to small numbers of response circles, whereas hard (high effort) options led to larger numbers of response circles. For both easy and hard options, safe options led to a fixed effort, whereas risky options led to variable effort that, with a 50/50 chance, required more or less effort. Participants who showed a preference for easier over harder options (63% in Experiment 1 and 93% in Experiment 2) were risk averse overall. Participants 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. Strikingly, memory for the effort associated with each risky option strongly correlated with risky choices for both easy-effort and hard-effort choices, suggesting that the memory may determine choices based on risky effort.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".