How do we judge our confidence? Differential effects of meta‐memory feedback on eyewitness accuracy and confidence
Bibliographic record
Abstract
Summary According to the cue–belief model, we assess confidence in our memories using self‐credibility cues that reflect beliefs about our memory faculties. We tested the influence of meta‐memory feedback on self‐credibility cues in the context of eyewitness testimony, when feedback was provided prior to “testifying” via a memory questionnaire (Experiment 1) and after an initial memory questionnaire but before participants had to retake it (Experiment 2). Participants received feedback (good score, bad score, or none) on a fictitious scale purported to predict eyewitness memory ability. Those given good score feedback before testifying were more confident (but no more accurate) than those given bad score feedback. Feedback also affected confidence (good increased and bad decreased) and accuracy (good increased) after testifying but only on leading questions. These differential effects of meta‐memory feedback on confidence for normal and leading questions are not explained by the cue–belief model. Implications for our confidence judgments are discussed.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".