People Consistently Overweight Extreme Outcomes in Risky Choices, Even after Long Delays
Bibliographic record
Abstract
When making decisions from experience, people rely on memories of past outcomes, which can be influenced by extreme outcomes (best and worst). These memories, however, can be forgotten. In a pre-registered experiment, we evaluated risk preference with delays of up to 7 days between initial learning and later consequential decisions. Participants (N=277 total) learned to choose between safe and risky options (e.g., 25 points vs 50/50 chance of 5 or 45) both in gain and loss domains. After a delay, they made decisions without feedback. All groups were more risk seeking for gains than losses by the end of learning, contrary to the typical pattern with explicit descriptions. This effect was long-lasting and persisted across the delays. Additionally, risk aversion slightly increased for both gains and losses with any delay. These results provide novel evidence that the influence of extreme outcomes on risky decisions persists over long-term delays.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".