Primacy (and recency) effects in delayed recognition of items from instances of repeated events
Bibliographic record
Abstract
In repeated-event paradigms where participants are asked to recall details of a sequence of similar instances they viewed/experienced previously, more accurate details are typically recalled from the first and final instances (i.e., long-term primacy and recency effects). Participants likely encode distinct attributes of details of the boundary instances that subsequently facilitate source monitoring. To date, most repeated event research has measured memory performance via free-/cued-recall paradigms; we examined delayed memory for repeated events using the recognition paradigm. In two preregistered experiments, participants viewed four videos, and after a delay completed a recognition task. In Experiment 1 (N = 168, between-subjects), participants decided whether an item was old (i.e., presented in any video) or new, or whether an item was presented in video 1/2/3/4 or was new. In Experiment 2 (N = 160, within-subjects), the old/new decision was followed by an instance attribution decision. Old items were recognized faster in the old/new task compared to the instance-attribution task. In the instance-attribution task, items from the boundary instances were accurately attributed faster compared to items from the middle instances. We found further evidence for primacy (and recency) effects in measures of confidence, memory judgments, recognition accuracy and discriminability, and confidence-accuracy calibration.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".