Manipulating basic characteristics of the Rapid Automatized Naming task in search for its most reliable connections to reading performance
Bibliographic record
Abstract
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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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".