Multiple demonstrations of metacognition in nonhumans: Converging evidence or multiple mechanisms?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Metacognition allows one to monitor and adaptively control cognitive processes. Reports from the last 15 years show that when given the opportunity, nonhuman animals selectively avoid taking difficult tests of memory or perception, collect more information if needed before taking tests, or "gamble" more food reward on correct than on incorrect responses in tests of memory and perception. I review representative examples from this literature, considering the sufficiency of four classes of mechanism to account for the metacognitive performance observed. This analysis suggests that many of the demonstrations of metacognition in nonhumans can be explained in terms of associative learning or other mechanisms that do not require invoking introspection or access to private mental states. Consideration of these accounts may prompt greater appreciation of the diversity of metacognitive phenomena and may inform theoretical positions about the nature of the mental representations underlying metacognition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it