Quantifying metacognitive thresholds using signal-detection theory
Bibliographic record
Abstract
Abstract How sure are we about what we know? Confidence, measured via self-report, is often interpreted as a subjective probabilistic estimate on having made a correct judgement. The neurocognitive mechanisms underlying the construction of confidence and the information incorporated into these judgements are of increasing interest. Investigating these mechanisms requires principled and practically applicable measures of confidence and metacognition. Unfortunately, current measures of confidence are subject to distortions from decision biases and task performance. Motivated by a recent signal-detection theoretic behavioural measure of metacognitive sensitivity, known as meta- ď , here we present a quantitative behavioural measure of confidence that is invariant to decision bias and task performance. This measure, which we call m - distance , captures in a principled way the propensity to report decisions with high (or low) confidence. Computational simulations demonstrate the robustness of m - distance to decision bias and task performance, as well as its behaviour under conditions of high and low metacognitive sensitivity and under dual-channel and hierarchical models of metacognition. The introduction of the m - distance measure will enhance systematic quantitative studies of the behavioural expression and neurocognitive basis of subjective confidence.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".