Speed of Cognitive Processing Within a Test of Executive Functions and Information Integration
Bibliographic record
Abstract
Rapid Automatic Naming (RAN) has been widely recognized as a reliable predictor of reading proficiency. Although RAN represents the speed of cognitive processing, there are few studies that have addressed RAN as a cognitive process in its own right Furthermore, RAN performance of ELL (English Language Learners) has been less frequently investigated. We have two parts to this study. First, we examine the factor structure of an enlarged composite measure of speed measure by adding four additional tests comprising color naming, and two number naming tasks to the traditional RAN of digit and letter naming. In the second part, we determine the association of Speed with broad cognitive processes comprising Executive Functions, and Information processing. Participants were students in English medium schools in India. They were divided into two age groups (8–14 and 15–20) for statistical analyses of six Speed measures Results show a strong unitary speed factor in the 8 to 14 age group. In contrast, in the 15 to 20 group RAN tests comprising digits and letters showed a very small loading on the same factor. Addressing the second objective, which is the impact of speed on various cognitive tasks, the results show that response speed has a minimal influence on Nonverbal Configurations (simultaneous) tasks, and tasks of executive functions comprising Working Memory, and Visual-Spatial Processing. These tests will enable us to isolate specific cognitive deficiencies from response speed. In a re-examination of the relation between Reading & RAN-type tests, we could suggest that serial articulation is the common and essential feature that binds rapid naming tasks and reading fluency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".