Developmental changes in phonological and semantic competition during spoken word recognition
Bibliographic record
Abstract
Spoken word-recognition is a foundational skill in child language development; the organization and activation of phonological and semantic competitors develops which in turn impacts word recognition. One of the factors impacting spoken word recognition is the ability to resolve lexical competition. The current study first examines how the time-course of lexical activation changes through development, and second, whether children attend more to targets when competitors are visually present or not. To date, 25 children between ages 3–7 who began acquiring French from birth participated in a visual world eye-tracking paradigm. Children were presented with 4 images, each representing a different French word. Images were either presented with both a phonological (e.g., baleine) and semantic (e.g., cerise) competitor of the audio stimuli (e.g., banana) or the audio stimuli had no relation to the competitor images (e.g., épée). Primary analysis reveals that older children look more to the target than younger children [F(2,22) = 3.8, p =.04] and that the presence of competitors does not impact target recognition [F(1,18) = 0.03, p =.87]. Such findings imply that children improve in their abilities to efficiently recognize words with age but are not affected by the visual presence of lexical competitors.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".