Living near the edge: How extreme outcomes and their neighbours drive risky choice
Bibliographic record
Abstract
Extreme stimuli are often more salient in perception and memory than moderate stimuli. In risky choice, when people learn the odds and outcomes from experience, the extreme outcomes (best and worst) also stand out. This additional salience leads to more risk-seeking for relative gains than for relative losses—the opposite of what people do when queried in terms of explicit probabilities. Previous research has suggested that this pattern arises because the most extreme experienced outcomes are more prominent in memory. An important open question, however, is what makes these extreme outcomes more prominent? Here we assess whether extreme outcomes stand out because they fall at the edges of the experienced outcome distributions or because they are distinct from other outcomes. Across four experiments, proximity to the edge determined what was treated as extreme: Outcomes at or near the edge of the outcome distribution were both better remembered and more heavily weighted in choice. This prominence did not depend on two metrics of distinctiveness: lower frequency or distance from other outcomes. This finding adds to evidence from other domains that the values at the edges of a distribution have a special role.
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.
How this classification was reachedexpand
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".