Dissociating appraisals of accuracy and recollection in autobiographical remembering.
Bibliographic record
Abstract
Recent studies of metamemory appraisals implicated in autobiographical remembering have established distinct roles for judgments of occurrence, recollection, and accuracy for past events. In studies involving everyday remembering, measures of recollection and accuracy correlate highly (>.85). Thus although their measures are structurally distinct, such high correspondence might suggest conceptual redundancy. This article examines whether recollection and accuracy dissociate when studying different types of autobiographical event representations. In Study 1, 278 participants described a believed memory, a nonbelieved memory, and a believed-not-remembered event and rated each on occurrence, recollection, accuracy, and related covariates. In Study 2, 876 individuals described and rated 1 of these events, as well as an event about which they were uncertain about their memory. Confirmatory structural equation modeling indicated that the measurement dissociation between occurrence, recollection and accuracy held across all types of events examined. Relative to believed memories, the relationship between recollection and belief in accuracy was meaningfully lower for the other event types. These findings support the claim that recollection and accuracy arise from distinct underlying mechanisms. (PsycINFO Database Record
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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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".