Orangutans (Pongo abelii) “play the odds”: Information-seeking strategies in relation to cost, risk, and benefit.
Bibliographic record
Abstract
Recent research has examined whether animals possess metacognition, or the ability to monitor their knowledge states. However, the extent to which animals actively control their knowledge states is still not well delineated. Although organisms might be capable of seeking information when it is lacking, it does not mean that it is always adaptive to do so. In the present set of experiments, we examined the flexibility of this behavior in captive orangutans (Pongo abelii; two adults and one juvenile) in a foraging task, by varying the necessity of information-seeking, the cost associated with it, the likelihood of error, and the value of the reward. In Experiment 1, subjects searched for information most often when it was "cheapest" energetically. In Experiment 2, subjects searched for information most often when the odds of making an error were the greatest. In Experiment 3, subjects searched for information more when the reward was doubled in value. In Experiment 4, adult subjects adapted to risk/benefit trade-offs in their searching behavior. In every experiment, subjects sought information more often when they needed it than when they already knew the solution to the problem. Therefore, the current research suggests that information-seeking behavior in orangutans shows a sophisticated level of flexibility, comparable to that seen in human children, as they appear to "play the odds" when making the decision to seek information or not.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".