Metacognitive monitoring during category learning: how success affects future behaviour
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
The purpose of this study was to see how people perceive their own learning during a category learning task, and whether their perceptions matched their performance. In two experiments, participants were asked to learn natural categories, of both high and low variability, and make category learning judgements (CLJs). Variability was manipulated by varying the number of exemplars and the number of times each exemplar was presented within each category. Experiment 1 showed that participants were generally overconfident in their knowledge of low variability families, suggesting that they considered repetition to be more useful for learning than it actually was. Also, a correct trial, for a particular category, was more likely to occur if the previous trial was correct. CLJs had the largest increase when a trial was correct following an incorrect trial and the largest decrease when an incorrect trial followed a correct trial. Experiment 2 replicated these results, but also demonstrated that global CLJ ratings showed the same bias towards repetition. These results indicate that we generally identify success as being the biggest determinant of learning, but do not always recognise cues, such as variability, that enhance learning.
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.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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