Recognition, remember-know, and confidence judgments: no evidence of cross-contamination here!
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
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.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it