A Process-Oriented Analysis of Speech and Silent Intervals in Responses to Serial Naming Tasks
Bibliographic record
Abstract
In this study we present a framework for conceptualizing and analyzing responses to serial naming (RAN) tasks, in which participants sequentially name a set of stimuli that are simultaneously presented in an array format. Our aim is to better understand how these tasks are processed and why they are associated with reading skills, particularly word reading fluency. We analyzed responses by 298 Greek children in Grades 1, 3, and 5 to serial and discrete naming of digits, dice, objects, number words, and words. We measured the durations of silent and speech intervals per item in each task and grade, and tested predictions about their relations based on a hypothesis of two overlapping processing stages. We found that articulation times were longer in the serial tasks, further modulated by task demands. Total times were faster for serial than for discrete tasks, and the differences between the two (termed serial advantage) were increasingly associated with the duration of speech intervals, consistent with efficient scheduling. Serial naming rate approached or exceeded the limits imposed by processing time (operationalized as discrete onset latency), consistent with increasing processing overlap. These patterns were primarily observed for digits, number words, and—to some extent—dice, after Grade 1. Object naming seemed to pose different cognitive demands, stably across grades. Word reading exhibited the greatest differences between grades, consistent with rapid development of automaticity. We interpret this pattern of findings within a cascaded processing framework, in which performance is determined by the efficiency of cognitive scheduling of successive operations, constrained by susceptibility to interference from adjacent items. We propose that reading fluency is predicted by serial naming because it is also largely governed by the same scheduling constraints.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".