Preparing to produce (without production) is sufficient to elicit a behavioral and pupillometric production effect
Bibliographic record
Abstract
The production effect refers to the finding that words read aloud are remembered better than words read silently. Historically, this phenomenon has been explained with reference to distinctive features encoded at study (e.g., auditory and motor elements) being retrieved at test to discriminate between studied and unstudied items, with emphasis placed on features stemming from the act of production itself. Across two experiments, we demonstrate that even anticipation of reading a word aloud is sufficient to improve its memory over silent items. Using a recent variant of the production paradigm involving pupillometry, participants were instructed to withhold their response until a “Go” signal appeared. On “catch” trials this signal never occurred. Despite having not produced the word on a catch trial, participants nonetheless demonstrated both a behavioral (Experiments 1 and 2) and a pupillary (Experiment 2) production effect, although both were of lesser magnitude than on trials requiring actual production. For “Go” trials, the behavioral production effect was evident for both recollection and familiarity; for “catch” trials, the effect was evident only for recollection. These results support recent claims that motivational or attentional factors play a role in the emergence of the production effect, connecting this effect to a broader framework of action-oriented memory enhancement.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".