Investigating the Interaction Between Semantic Knowledge and Motor Engagement in the Recognition of Unfamiliar Text
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».