The long-term recency effect in recognition memory
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
Three classes of theories explain the recency effect: the modal model, single-store models, and the composite view, which integrates the two positions. None could explain the absence of a long-term recency effect in recognition memory in previous studies. We suggest that prior work did not obtain a recency effect because testing used a multiple-probe rather than a single-probe recognition procedure. Here we tested memory using a single-probe recognition procedure. Experimental conditions included an immediate test, a delayed test after a filled interval, and a continuous-distractor paradigm in which the same filled delay preceded the first word and followed every study word. The long-term recency effect in continuous-distractor recognition was equivalent to the recency effect in immediate recognition. Its absence in the delayed recognition condition demonstrated that it was not attributed to the use of a putative short-term memory store. Single-store models and the composite view can account for this novel finding.1 1The study was funded by an NSERC grant CFC 205055 Fund 454119 to Morris Moscovitch and by the Israel Science Foundation Grant 894-01 to Yonatan Goshen-Gottstein. The authors thank Morris Moscovitch for his support, J. B. Caplan and F. I. M. Craik for their encouragement, helpful discussions, and comments on earlier drafts of this manuscript, and M. Ziegler for help in programming.
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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 | 0.001 | 0.001 |
| 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