The power of nothing: Risk preference in pigeons, but not people, is driven primarily by avoidance of zero outcomes.
Bibliographic record
Abstract
) often show similar choice patterns. When the reward probabilities of risky choices are learned through experience, preferences in both species seem to be disproportionately influenced by the extreme (highest and lowest) outcomes 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. In a series of studies, we systematically examine how this overweighting of extreme outcomes in risky choice generalizes across 2 evolutionary distant species: pigeons and humans. Both species showed risky choices consistent with an overweighting of extreme outcomes when the low-value risky option could yield an outcome of 0. When all outcome values were increased such that none of the options could lead to 0, people but not pigeons still overweighted the extremes. Unlike people, pigeons no longer avoided a low-value risky option when it yielded a nonzero food outcome. These results suggest that, despite some similarities, different mechanisms underlie risky choice in pigeons and people. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".