Metacognition in animals: how do we know that they know?
Bibliographic record
Abstract
Research on animal metacognition has typically used choice discriminations whose difficulty can be varied. Animals are given some opportunity to escape the discrimination task by emitting a so-called uncertain response. The usual claim is that an animal possesses metacognition if (a) the probability of picking the uncertain response increases with task difficulty, and (b) animals are more accurate on "free-choice" trials -i.e., trials where the uncertain response was available but was not chosen-than on "forced-choice" trials, where the uncertain response is unavailable. We describe a simple behavioral economic model (BEM), based on familiar learning principles, and thus lacking any metacognition construct, which is able to meet both criteria in most of these tasks. We conclude that rather than designing ever more complex experiments to identify "metacognition," a necessarily ill-defined concept, knowledge might better be advanced not by further refining behavioral criteria for the concept, but by the development and testing of theoretical models for the clever behavior that many animals show in these experiments.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".