Enhancing memory using enactment: does meaning matter in action production?
Bibliographic record
Abstract
Enactment is an encoding strategy in which performing an action related to a target item enhances memory for that word, relative to verbal encoding. Precisely how this motor activity aids recall is unclear. We examined whether the action created during encoding needed to be semantically relevant to the to-be-remembered word, to enhance memory. In Experiment 1, participants were asked to either (a) enact, (b) perform unrelated motoric gestures, or (c) read forty-five action verbs. On a subsequent free-recall test, memory for enacted words was significantly higher relative to words read, or encoded with unrelated gestures. In Experiment 2, to reduce the ambiguity associated with initiating an unrelated gesture, participants were instructed to write target words in the air. Results were similar to Experiment 1. In Experiment 3, we replicated the results of Experiment 2 using video conferencing to record the onset time of action initiation for enacted, unrelated gesture, and read trials. Results showed that planning of meaningful actions may also contribute to the memory performance as evidenced by a longer onset time to initiate an action on enactment relative to gesturing and reading trials. These findings suggest that planning and executing meaningful actions drive the enactment benefit.
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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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".