Retrieval priming in product verification: Evidence from retrieval-induced forgetting
Bibliographic record
Abstract
The conditions under which multiplication verification (3 × 6 = 12, true or false?) involves product retrieval and comparison or familiarity-based recognition judgements has not been clearly established. In two experiments examining verification of single-digit multiplication problems, we used Retrieval-Induced Forgetting (RIF), a signature of retrieval use, as an index of product retrieval in multiplication verification. In Experiment 1, 72 adults practiced multiplication either in a production format or in a verification format and then were tested on corresponding addition and control problems. The results showed RIF (i.e., slower answer production for addition problems whose multiplication counterparts had been practiced) in both the production-practice and the verification-practice groups, but RIF was stronger following true than false verification. Experiment 2 tested verification with related-false and unrelated-false products. Related-false equations produced longer RTs than unrelated false equations. Practice of true, related-false and unrelated-false multiplication equations all produced RIF of the addition counterparts but, overall, related-false multiplication equations produced relatively weak RIF. The results indicated that product retrieval mediates multiplication verification even when false answers are weak associative lures and suggest that a retrieve-and-compare process is the default strategy when false answers are at least plausible. We conclude that the presented answer in verification equations act as retrieval-priming stimuli with true equations priming correct answer retrieval and related-false answers interfering with correct answer retrieval.
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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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".