The long-term recency effect in recognition memory
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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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".