Bibliographic record
Abstract
The field of comparative cognition has been largely concerned with the degree to which animals have analogs of the cognitive capacities of humans (e.g., imitation, categorization), but recently attention has been directed to behavior that is judged to be biased or suboptimal.We and some of our colleagues have studied several of these and have found that pigeons too show similar paradoxical behaviors.In the present review I will discuss three of these behaviors: sunk cost, justification of effort, and unskilled gambling.Sunk cost is the tendency to decide to spend more on a losing project because of the amount already invested.Pigeons show similar effects even when there is no ambiguity about the results of continuing versus changing alternatives.Justification of effort is the added value one often gives to a reward based on the effort exerted to obtain it.Pigeons too prefer stimuli that signal outcomes that they have had to work harder to obtain.Humans engage in unskilled gambling, like lotteries and slot machines, in which the return is typically less than the investment.And pigeons show a similar tendency to choose a low-probability, high-payoff alternative (gamble) over a more optimal, high-probability, low-payoff alternative.The fact that animals such as pigeons show behavior thought to be unique to humans suggests that the basis for such behaviors is not likely to result from culture or social mechanisms and may have basic behavioral origins.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".