The power of nothing: Risk preference in pigeons, but not people, is driven primarily by avoidance of zero outcomes
Bibliographic record
Abstract
When making risky decisions, people and pigeons often show similar choice patterns. When people learn the reward probabilities through repeated exposure to the outcomes, their preference is disproportionately influenced by the extreme (highest and lowest) outcomes occurring in the decision context. Overweighting of these extremes increases preference for risky alternatives that lead to the highest outcome and decreases preference for risky alternatives that lead to the lowest outcome, termed the extreme-outcome rule. This rule predicts greater risk seeking for choices between safe and risky high-value outcomes than for choices between safe and risky low-value outcomes, when both choices occur in the same context. In a series of studies, we examine how this extreme-outcome rule generalizes within and across two evolutionary distant species: pigeons (Columba livia) and humans (Homo sapiens). Both species showed risky choices consistent with the extreme-outcome rule when a low-value risky option could yield an outcome of zero. When all outcome values were increased such that none of the options could lead to zero, people but not pigeons were still consistent with the extreme-outcome rule. Unlike people, pigeons no longer avoided a low-value risky option when it yielded a non-zero food outcome. These results suggest that, despite some similarities, different mechanisms underlie risky choice in pigeons and people.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".