Recognition, remember-know, and confidence judgments: no evidence of cross-contamination here!
Bibliographic record
Abstract
We report three experiments designed to reveal the mechanisms that underlie subjective experiences of recognition by examining effects of how those experiences are measured. Prior research has explored the potential influences of collecting metacognitive measures on memory performance. Building on this work, here we systematically evaluated whether cross-measure contamination occurs when remember-know (RK) and/or confidence (C) judgments are made after old/new recognition decisions. In Experiment 1, making either RK or C judgments did not significantly influence recognition relative to a standard no-judgment condition. In Experiment 2, making RK judgments in addition to C judgments did not significantly affect recognition or confidence. In Experiment 3, making C judgments in addition to RK judgments did not significantly affect recognition or patterns of RK responses. Cross-contamination was not apparent regardless of whether items were studied using a shallow or deep levels-of-processing task - a manipulation that yielded robust effects on recognition, RK judgments, and C. Our results indicate that under some conditions, participants can independently evaluate their recognition, subjective recognition experience, and confidence. Though contamination across measures of metamemory and memory is always possible, it may not be inevitable. This has implications for the mechanisms that underlie subjective experiences that accompany recognition judgments.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".