Distinctiveness and interference in free recall: A test with the production effect.
Bibliographic record
Abstract
It is well established that when some words within a list are read aloud, or produced, and others are read silently, produced items are better recalled. According to the revised feature model (RFM), this benefit stems from additional features and enhanced distinctiveness. Because the model assigns forgetting to similarity-based retroactive interference, produced items should be better recalled when followed by a silent item than by another produced item. However, this mechanism had never been directly tested. In addition, Forrin et al. (2019) suggested a memory cost for silent items in mixed lists. Based on their production anticipation hypothesis, this cost derived from social anxiety and performance anticipation emerging within the experimental setting. Here, we tested these two competing accounts in four experiments in which 240 participants completed an immediate or delayed free recall task with 10-word lists. Produced and silent items were presented in two blocks of various lengths, and the occurrence of produced items within the list was predictable. We also manipulated the presence of the experimenter in the room during the task, so that the experimenter was present for Experiments 1A and 2A but absent for Experiments 1B and 2B. Overall, results offer full support for the RFM but very limited support for the production anticipation hypothesis. The main trends in the data were also modeled by the RFM. In sum, with the production effect, we provide the first comprehensive test of the retroactive interference mechanism described within the RFM and further inform our understanding of memory. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".