A preliminary investigation into the neural basis of the production effect.
Bibliographic record
Abstract
Items that are produced (e.g., read aloud) during encoding typically are better remembered than items that are not produced (e.g., read silently). This "production effect" has been explained by distinctiveness: Produced items have more distinct features than nonproduced items, leading to enhanced retrieval. The goal of the current study was to use electroencephalography (EEG) to examine the neural basis of the production effect. During study, participants were presented with words that they were required to read silently, read aloud, or sing while EEG data were recorded. Subsequent memory performance was tested using a yes/no recognition test. Analysis focused on the event-related brain potentials (ERPs) evoked by the encoding instruction cue for each instruction condition. Our data revealed enhanced memory performance for produced items and a greater P300 ERP amplitude for instructions to sing or read aloud compared with instructions to read silently. Our results demonstrate that the amplitude of the P300 is modulated by at least 1 aspect of production, vocalization (singing/reading aloud relative to reading silently), and are consistent with the distinctiveness account of the production effect. The ERP methodology is a viable tool for investigating the production effect. (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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".