Effects of learning experience on forgetting rates of item and associative memories
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
Are associative memories forgotten more quickly than item memories, and does the level of original learning differentially influence forgetting rates? In this study, we addressed these questions by having participants learn single words and word pairs once (Experiment 1), three times (Experiment 2), and six times (Experiment 3) in a massed learning (ML) or a distributed learning (DL) mode. Then they were tested for item and associative recognition separately after four retention intervals: 10 min, 1 d, 1 wk, and 1 mo. The contribution of recollection and familiarity processes were assessed by participants' remember/know judgments. The results showed that for both item and associative memories, across different degrees of learning, recollection decreased significantly and was the main source of forgetting over time, whereas familiarity remained relatively stable over time. Learning multiple times led to slower forgetting at shorter intervals, depending on recollection and familiarity processes. Compared with massed learning, distributed learning (six times) especially benefited associative memory by increasing recollection, leading to slower forgetting at longer intervals. This study highlighted the importance of process contribution and learning experiences in modulating the forgetting rates of item and associative memories. We interpret these results within the framework of a dual factor representational model of forgetting (as noted in a previous study) in which recollection is more prone to decay over time than familiarity.
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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 | 0.015 |
| 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 | 0.000 |
| 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 it