The influence of motor reinstatement and drawing quality on remembering
Bibliographic record
Abstract
Creating drawings of information can provide elaborative, pictorial, and motor cues that facilitate later retrieval from memory, but the contribution of motor information remains unclear. To test this, we had participants encode words via drawing using a robotic manipulandum. 10 mins (E1) or 1-2 days (E2) later, they completed a visual recognition task while the robotic manipulandum guided their arm through predetermined motor paths. Unbeknownst to participants, the paths were either congruent (motor reinstatement) or incongruent (interference) with their drawing of the current target word. Response time was consistently fastest with motor reinstatement, indicating that reactivating an encoded motor path made visual recognition more efficient regardless of the delay between encoding and reinstatement. However, while E1 revealed that passive reinstatement improved recognition accuracy, this benefit disappeared with the longer delay in E2. Drawings were submitted to a pretrained neural network (NN), and E1 revealed that higher-quality drawings (i.e. those more easily identified by the NN) were better recognized, regardless of reinstatement condition, but again, this pattern disappeared with the longer delay. The collective findings demonstrate that passive motor reinstatement reliably increases the speed of memory retrieval, but that any influence on accuracy is transient. Using principal components analysis (PCA) we identified two components in NN features that were associated with better memory, and these were consistent across experiments, indicating that there were predictable features that lead to more memorable drawings. Ongoing work will determine whether changes in the level of motor engagement, quality and timing of reinstatement change the observed benefit to memory. Together, these results emphasize the influence of the quality and type of encoded features as well as the timing of reinstatement in influencing memory performance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".