The Effect of Response Effort on Risk-Sensitive Decision Making in Pigeons (Columbia livia)
Bibliographic record
Abstract
Foraging decisions are central to an organism’s survival in the unpredictable natural world. Recently, decision-making theories related to foraging have shifted focus from classic optimal rate maximization to ones that recognize risk as a prime factor that governs choice. For example, animals have been shown to adopt either risk-prone or risk-averse strategies, depending on their preference for variable or constant alternatives. The effects of variability in time or amount of reward have been consistently validated in animal models, establishing two main factors that affect choice behaviour. In the current study, we investigated response effort as a potential third factor that could produce risk sensitivity. Using operant conditioning, pigeons were trained to perform a colour discrimination task under two conditions. In the first condition, pigeons were exposed to stimuli associated with variable or constant response effort. In the second condition, pigeons experienced a time-matched delay before reinforcement to control for the concurrent effects of time. Preference for variable stimuli was measured in each condition using a probe trials method. We found that pigeons’ preference for a variable alternative in the response effort condition differed significantly from the time-matched control, such that time factors alone produced stronger risk-prone preferences over effort. These findings provide further evidence of the importance of assessing ecologically valid behaviour when using experimental methods. While incorporating foraging effort diminishes the effect of risk sensitivity, this may be more representative of the risk associated with delay that animals encounter in their natural environment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".