Developmental Trends of Visual Processing of Letters and Objects Using Naming Speed Tasks
Bibliographic record
Abstract
Studying the typical development of reading is key to understanding the precise deficits that underlie reading disabilities. An important correlate of efficient reading is the speed of naming arrays of simple stimuli such as letters and pictures. In this cross-sectional study, we examined developmental changes in visual processing that occurs during letter and object naming from childhood to early adulthood in terms of behavioral task efficiency, associated articulation and eye movement parameters, and the coordination between them, as measured by eye-voice span in both the spatial and temporal domains. We used naming speed (NS) tasks, in which participants were required to name sets of letters or simple objects as quickly and as accurately as possible. Single stimulus manipulations were made to these tasks to make the stimuli either more visually and/or phonologically similar to one another in order to examine how these manipulations affected task performance and the coordination between speech and eye movements. Across development there was an increased efficiency in speech and eye movement performance and their coordination in both the spatial and temporal domains. Furthermore, manipulations to the phonological and visual similarity of specific letter and object stimuli revealed that orthographic processing played a greater role than phonological processing in performance, with the contribution of phonological processing diminishing across development. This comprehensive typical developmental trajectory provides a benchmark for clinical populations to elucidate the nature of the cognitive dysfunction underlying reading difficulties.
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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 | 0.001 | 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".