Auditory and visual naming tests for children
Bibliographic record
Abstract
Assessment of naming in children has been hampered by the use of tests that were developed, either to assess naming in adults or to assess related verbal functions in children. We developed comparable visual naming test (VNT) and auditory description naming (ANT) specifically for children. We collected normative data, not only for accuracy, typically the sole performance measure, but also for response time (RT) and reliance on phonemic cuing. The normative sample consisted of 200 typically developing children, ages 6-15, with 40 children per 2-year age group (6-7, 8-9, 10-11, 12-13, and 14-15). Children were tested individually by a trained examiner. Based on item analysis, naming tests were finalized at 36 items for ages 8-15 and 28 items for ages 6-7. Age-stratified normative data are provided for accuracy, mean RT, tip-of-the-tongues (i.e., delayed but accurate responses plus items named following phonemic cueing), and a summary score, which incorporates all performance measures. Internal and test-retest reliability coefficients for both tests were reasonable. Accuracy scores were high across age groups, indicating that item names were within the mental lexicon of most typically developing children. By contrast, time and cue-based scores improved with age, reflecting greater efficiency in word retrieval with development. These complementary auditory naming and visual naming tests for children address a longstanding clinical need, improving upon the current standard with respect to the sensitivity of performance measures and the addition of an auditory verbal component to the assessment of naming in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".