The pupillometric production effect: Evidence for enhanced processing preceding, during, and following production
Bibliographic record
Abstract
The production effect refers to superior memory performance for words read aloud than for those read silently. This finding has usually been attributed to the incorporation of distinctive sensorimotor information into the memory record of items read aloud, facilitating their successful retrieval during the memory test. Less research has explored other cognitive or motivational differences between the aloud and silent conditions. Here we used pupillometry to explore the time course of attention allocated during aloud, silent, and control (say "check") study trials. Across four experiments, instructions were presented either concurrently with or preceding the word. To permit evaluation of preparatory processing independent of a verbal response, we explored the case where responses had to be withheld until a "Go" signal appeared. In addition to the typical behavioral production effect in memory, each experiment also revealed a pupillometric production effect (greater pupil dilation for aloud than for silent words) that-while separable from the act of speaking itself-was correlated with the size of the memory benefit. Critically, this pupillometric-behavioral correlation did not occur for control (say "check") trials. We interpret these findings as support for an initial attention-focusing effect that comes from preparing for and executing vocalization during both aloud and control trials, followed by a phase of distinctive processing of target word features that is unique to aloud trials.
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How this classification was reachedexpand
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".