Forrin, N. D., & MacLeod, C. M. (2016). Auditory presentation at test does not diminish the production effect in recognition.
Bibliographic record
Abstract
Canadian Journal of Experimental Psychology, 70, 116-124. Abstract: Three experiments investigated whether auditory information at test would undermine the relational distinctiveness of vocal production at study, diminishing the production effect. In Experiment 1, with visual presentation during study, the production effect was equivalently large regardless of whether participants read each test word out loud prior to making their recognition decision. In Experiment 2, incorporating auditory presentation during study, the production effect was unaltered by whether recognition test words were presented visually or auditorily. In Experiment 3, the authors manipulated whether presentation was visual or auditory both at study and at test. Once again, presentation modality at test did not affect the size of the production effect, although the effect was significantly smaller when words were presented auditorily at study. These experiments demonstrate that production at the time of study stands out as distinct above and beyond auditory information. Moreover, this distinct aloud information need not “stand out” against a background of silent unstudied words on a recognition test. Consistent with the distinctiveness account, encoding via production enhances later recognition consistently, regardless of study or test modality.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".