Investigating the motor strategies involved in handwriting and recall of unfamiliar text
Bibliographic record
Abstract
Repetitive writing is an effective strategy for enhancing recognition memory for unfamiliar texts, possibly because the involvement of sensorimotor networks during writing strengthens the spatial cognitive networks utilised for word recognition. However, the relative contribution of visual feedback and motor processes to the recognition benefit remains unclear. In this study, we examine whether the nature of the visual information presented during writing influences later recognition of unfamiliar texts. In an initial encoding phase, participants encoded a list of 8 words in an unfamiliar text (i.e., Arabic). Participants then performed a pre-test where they attempted to identify each previously encoded word in a three-alternative-forced choice task. Participants then underwent an acquisition phase where they wrote a portion of the encoded words (trained words) in four counterbalanced conditions: observational writing (OW), then active handwriting either with full visual feedback (A), without visual feedback of their writing (e.g., active no-ink or ANI), or without visual feedback of the environment (ANVE). To determine the effects of this training, participants performed a post-test that was identical to the pre-test. Recognition accuracy (# correct responses) was computed for both the pre- and post-tests. Accuracy differences between conditions were assessed using a four (condition) by two (pre/post) repeated measures ANOVA. Recognition accuracy increased from the pre-test to the post-test after training in all conditions for trained, but not untrained words. These results provide evidence that both active writing and passive observation improved recall of unfamiliar text.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".