A Remember/Know Examination of Free-recall Reveals Dissociative Roles of Item- and Context-Information over Time
Bibliographic record
Abstract
It is well-established that the ability to freely recall information is driven by the extent to which the context at encoding is reinstated at retrieval. Still, when asked to judge the subjective quality of one's memories giving Remember/Know (R/K) judgments, people tend to classify a substantial proportion of recalls as being devoid of context. We suggest that R- and K-recalls differ with regard to their reliance on context- and item-information, with R-recalls driven primarily by contextual-information (e.g., associations evoked by the study-items) and K-recalls driven primarily by information pertaining to the items (e.g., semantic information). Memory was tested both immediately after study and in a final free-recall test conducted ~20 minutes after encoding-a timescale which is akin to real-life events. In line with our predictions, as compared to K-recalls, R-recalls show stronger contextual effects, but similarly strong item-related effects over these timescales. Furthermore, drawing on theories regarding the forgetting of item- and contextual information, we hypothesized and found that R- and K-recalls are differentially affected by the passage of time. Our findings provide several converging pieces of evidence for differential roles of item and contextual information in driving recall and thus highlight the need to extend longstanding theories of free-recall to account for cases in which recall relies less on context.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".