Investigating the Interaction Between Semantic Knowledge and Motor Engagement in the Recognition of Unfamiliar Text
Bibliographic record
Abstract
Humans excel at recognizing and remembering familiar text, such as well-known product names or brands in their native language, but this ability diminishes with unfamiliar text, such as words in a foreign language. One strategy to improve recognition of unfamiliar texts is repetitive writing, which engages sensorimotor networks and strengthens the spatial cognitive networks involved in word recognition. Previous studies demonstrating the effect of writing on word recognition typically use either native speakers or novice learners. In this thesis, we extended this research by including a group that we define as recognizers. Specifically, we were interested in Muslim Quran reciters who can read and write Arabic with little understanding of the language. Studying the influence of writing on recognition memory in recognizers could offer unique insights into the networks engaged by partial language familiarity compared to native speakers and novices. Objective: This study aimed to determine the effect of semantic knowledge on recognition memory across different levels of sensorimotor engagement. During the encoding phase, participants were shown Arabic words, followed by a pre-test where they had to identify the previously shown words. Participants then underwent an acquisition phase where they wrote the encoded words in four writing conditions: active handwriting with full visual feedback (A), active handwriting without visual feedback of their writing (e.g., active no-ink or ANI), active handwriting without visual feedback of the environment (ANVE), and observational writing (OW). Following the acquisition phase, participants performed a post-test that was the same as the pre-test. The main dependent variables were recognition accuracy and response time. Results: Overall, participants were more accurate in the post-test compared to the pre-test, and native speakers were more accurate and responded faster than both recognizers and novices. Furthermore, native speakers were more accurate than novices and recognizers in the A, ANI, and ANVE conditions. Conclusion: The study demonstrated that semantic knowledge significantly enhances recognition memory, with native speakers benefiting most from sensorimotor and visual encoding strategies. Novices and recognizers benefit from active sensorimotor engagement, highlighting a possible benefit of encoding via writing for individuals with less semantic knowledge.
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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".