Comparative inspiration: From puzzles with pigeons to novel discoveries with humans in risky choice
Bibliographic record
Abstract
Both humans and non-human animals regularly encounter decisions involving risk and uncertainty. This paper provides an overview of our research program examining risky decisions in which the odds and outcomes are learned through experience in people and pigeons. We summarize the results of 15 experiments across 8 publications, with a total of over 1300 participants. We highlight 4 key findings from this research: (1) people choose differently when the odds and outcomes are learned through experience compared to when they are described; (2) when making decisions from experience, people overweight values at or near the ends of the distribution of experienced values (i.e., the best and the worst, termed the “extreme-outcome rule”), which leads to more risk seeking for relative gains than for relative losses; (3) people show biases in self-reported memory whereby they are more likely to report an extreme outcome than an equally-often experienced non-extreme outcome, and they judge these extreme outcomes as having occurred more often; and (4) under certain circumstances pigeons show similar patterns of risky choice as humans, but the underlying processes may not be identical. This line of research has stimulated other research in the field of judgement and decision making, illustrating how investigations from a comparative perspective can lead in surprising directions.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".