Construction of Graphic Symbol Utterances by Children, Teenagers, and Adults: The Effect of Structure and Task Demands
Bibliographic record
Abstract
PURPOSE: This study investigated the impact of syntactic complexity and task demands on construction of utterances using picture communication symbols by participants from 3 age groups with no communication disorders. METHOD: Participants were 30 children (7;0 [years;months] to 8;11), 30 teenagers (12;0 to 13;11), and 30 adults (18 years and above). All participants constructed graphic symbol utterances to describe photographs presented with spoken French stimuli. Stimuli included simple and complex (object relative and subject relative) utterances describing the photographs, which were presented either 1 at a time (neutral condition) or in an array of 4 (contrast condition). RESULTS: Simple utterances lead to more uniform response patterns than complex utterances. Among complex utterances, subject relative sentences appeared more difficult to convey. Increasing the need for message clarity (i.e., contrast condition) elicited changes in the production of graphic symbol sequences for complex propositions. The effects of syntactic complexity and task demands were more pronounced for children. CONCLUSION: Graphic symbol utterance construction appears to involve more than simply transferring spoken language skills. One possible explanation is that this type of task requires higher levels of metalinguistic ability. Clinical implications and directions for further research are discussed.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".