Going Through the Motions: The Influence of Motor Reinstatement on Recognition Memory
Bibliographic record
Abstract
Engaging the motor system is a potent tool for improving memory, both on its own and as a component of other effective mnemonics (e.g., enactment, writing, drawing). Drawing in particular likely benefits the encoding of new memories by incorporating motor information, but also via the integration ofelaborative and pictorial information, all of which provide cues that can be used to facilitate memory retrieval. Here, we attempt to isolate the distinct influence of motor information by testingwhether passively reinstating movements during retrieval that were previously produced while encoding via drawing will boost memory performance. In an initial encoding phase, participants drew words by guiding the handle of a robotic manipulandumand pushing a button to draw. In the retrieval phase, the manipulandum instead guided the participant’s hand, chasing an on-screen bullseye. A single word appeared on-screen mid-trial, which was sometimes (half the time) one they had drawn (i.e. a ‘target'). Participants pressed a button to indicate whether they had drawn the word. During each trial, the handle moved along coordinates governed by one of three conditions: motor reinstatement (MR; the path of a drawing of the target word), motor interference (MI; a different word), or random (R; a random path). Results indicated that memory accuracy washigher and response time faster when the movements aligned with the target (MR) compared to when they did not (MI; R). This suggests that the motor system can influence memory through the reinstatement of prior movements, opening the door for future therapeutic applications.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".