A computational account of the production effect: Still playing twenty questions with nature.
Bibliographic record
Abstract
People remember words that they read aloud better than words that they read silently, a result known as the production effect. The standing explanation for the production effect is that producing a word renders it distinctive in memory and, thus, memorable at test. By 1 key account, distinctiveness is defined in terms of sensory feedback. We formalize the sensory-feedback account using MINERVA 2, a standard model of memory. The model accommodates the basic result in recognition as well as the fact that the mixed-list production effect is larger than its pure-list counterpart, that the production effect is robust to forgetting, and that the production and generation effects have additive influences on performance. A final simulation addresses the strength-based account and suggests that it will be more difficult to distinguish a strength-based versus distinctiveness-based explanation than is typically thought. We conclude that the production effect is consistent with existing theory and discuss our analysis in relation to Alan Newell's (1973) classic criticism of psychology and call for an analysis of psychological principles instead of laboratory phenomena. (PsycINFO Database Record
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".