Two case studies of very long-term retention
Bibliographic record
Abstract
Here, we present two case studies of extremely long-term retention. In the first, Richard C. Atkinson (RCA) had learned word sequences during experiments for his dissertation. Sixty-seven years later, RCA relearned the same words either in the original order or in a scrambled order. RCA reported no conscious awareness that the words were those used in the dissertation, but his relearning was considerably better for the words in the original order. In the second case study, Denis Cousineau had searched displays of objects for the presence of a target. The targets and foils had been novel at the beginning of training, and his search rate improved markedly over about 70 sessions. After 22 years, retraining showed retention of much of this gain in rate of search, and the rate was markedly faster than search for new objects with the same structure as the trained set. We consider interpretations of these case studies for our understanding of long-term retention.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".