Eye Movements and Articulations During a Letter Naming Speed Task
Bibliographic record
Abstract
Naming speed (NS) refers to how quickly and accurately participants name a set of familiar stimuli (e.g., letters). NS is an established predictor of reading ability, but controversy remains over why it is related to reading. We used three techniques (stimulus manipulations to emphasize phonological and/or visual aspects, decomposition of NS times into pause and articulation components, and analysis of eye movements during task performance) with three groups of participants (children with dyslexia, ages 9-10; chronological-age [CA] controls, ages 9-10; reading-level [RL] controls, ages 6-7) to examine NS and the NS-reading relationship. Results indicated (a) for all groups, increasing visual similarity of the letters decreased letter naming efficiency and increased naming errors, saccades, regressions (rapid eye movements back to letters already fixated), pause times, and fixation durations; (b) children with dyslexia performed like RL controls and were less efficient, had longer articulation times, pause times, fixation durations, and made more errors and regressions than CA controls; and (c) pause time and fixation duration were the most powerful predictors of reading. We conclude that NS is related to reading via fixation durations and pause times: Longer fixation durations and pause times reflect the greater amount of time needed to acquire visual/orthographic information from stimuli and prepare the correct response.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".