Integration of representations is key to the enactment benefit: Insights from individuals with stroke lesions
Bibliographic record
Abstract
Previous research has suggested that performing an action during encoding, related to the meaning of a target word (known as ‘enactment’), benefits later memory retrieval relative to when the word is simply read. It has been suggested that enactment confers this memory benefit by promoting the formation of a multimodal memory trace through the integration of verbal and motoric representations, facilitated by the parietal lobe. More recent work has proposed that cognitive planning preceding the execution of enactment, via engagement of frontal lobe-based processes, is most critical for the memory benefit. Here, evidence for these two accounts was assessed by comparing memory in healthy controls relative to individuals with lesions to parietal or frontal brain areas. Frontal stroke participants and controls both showed significant enactment effects: Recall was better for words enacted at encoding relative to those that were silently read. In contrast, participants with parietal lesions did not show the effect. Results suggest that the integration of multimodal representations by parietal lobe-based processes is a critical step necessary to evoke the benefit of enactment on memory performance. • Enacting a word at encoding enhances memory more than reading it. • We compared enactment benefits in healthy controls (HCs) and stroke patients. • Enacted words were recalled better by HCs and frontal patients, not parietal. • We suggest that multimodal integration via the parietal lobe underlies enactment.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".