Bibliographic record
Abstract
Animals often engage in representationally guided goal-directed behaviors. These behaviors are thus also subjected to representational uncertainty (e.g. timing uncertainty during waiting), which has been previously shown to adaptively guide behaviors normatively. These observations raise the question of whether non-human animals can track the direction and magnitude of their timing errors (i.e. temporal error monitoring). Only a few studies have investigated this question without addressing the key components of temporal error monitoring (e.g. due to differential reinforcement of metacognitive judgments and primary task representation). We conducted the critical test of temporal error monitoring in mice by developing a novel behavioral task that involved temporal production that exponentially favored temporal accuracy and minimized the contribution of sensorimotor noise. The response rate for an upcoming probabilistic reward following the timing performance was used as a proxy for confidence. We found that mice exhibited high reward expectancy after accurate and low reward expectancy after inaccurate timing performance. The reward expectancy decreased as a function of deviations from the target interval for the short and long reproductions; pointing to the symmetrical sensitivity of metacognition to shorter/longer than target responses. These findings suggest a complete temporal error monitoring ability for mice with human-like metacognitive features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".