Reward context determines risky choice in pigeons and humans
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
Whereas humans are risk averse for monetary gains, other animals can be risk seeking for food rewards, especially when faced with variable delays or under significant deprivation. A key difference between these findings is that humans are often explicitly told about the risky options, whereas non-human animals must learn about them from their own experience. We tested pigeons (Columba livia) and humans in formally identical choice tasks where all outcomes were learned from experience. Both species were more risk seeking for larger rewards than for smaller ones. The data suggest that the largest and smallest rewards experienced are overweighted in risky choice. This observed bias towards extreme outcomes represents a key step towards a consilience of these two disparate literatures, identifying common features that drive risky choice across phyla.
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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 | 0.002 | 0.002 |
| 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) | 0.000 | 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