Manipulating basic characteristics of the Rapid Automatized Naming task in search for its most reliable connections to reading performance
Notice bibliographique
Résumé
Introduction. Connections between Rapid Automatized Naming (RAN) task performance and reading are well documented. Primary empirical studies and meta-analyses established and described associations between specific RAN subtasks and reading outcomes. The cognitive nature of these associations, however, remains largely underexplored. This study attempts to address the issue by explicitly manipulating some critical characteristics of the RAN task (stimuli types, combinations, and familiarity) and conditions of its administration (attention demand) in search for factors that affect RAN performance and underlie its connections to reading competencies. Method. Ten modified RAN subtasks were created by manipulating type and familiarity of the stimuli, size of the stimuli source set, and demand to attention (cognitive controlled processing), involved in RAN performance. Measures of ballistic and efficiency-based automaticity, attention control, and reading rate were collected and analyzed using, ANOVA – with respect to performance on modified RAN subtasks, and correlational and multiple regression analyses – to address interrelations among major independent variables and their connections to reading rate. Results. The study found differential sensitivity of the RAN performance to the explored experimental manipulations. Specifically, significant main effects on naming speed were observed for stimuli type, stimuli familiarity and attention demand. RAN performance on most of the modified subtasks (seven out of ten) was significantly correlated with the measure of attention control, whereas only one correlation between RAN and measures of automaticity was statistically significant. Findings of multiple regression analyses confirmed this pattern of results. Attention factor explained substantially larger portion of variance in performance on modified RAN than both indices of automaticity combined. Reading rate was significantly correlated with bigram-based RAN (supposedly reflecting practice), and its correlations with other modified subtasks were higher for the elevated attention demand conditions, in one case exceeding significance level. Discussion. Understanding the cognitive nature of RAN is important for informing instructional practice of what reading skills might require special attention. This study explored specific conditions to which RAN performance may be especially sensitive. Modified RAN subtasks were markedly influenced by experimental manipulations, especially with regard to attention demand, indicating that attention, more than automaticity, could be a factor underlying naming speed as a predictor of reading.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».