A computational account of the production effect: Still playing twenty questions with nature.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it