Individual differences in the shape bias in preschool children with specific language impairment and typical language development: theoretical and clinical implications
Bibliographic record
Abstract
We investigated whether preschool children with specific language impairment (SLI) exhibit the shape bias in word learning: the bias to generalize based on shape rather than size, color, or texture in an object naming context ('This is a wek; find another wek') but not in a non-naming similarity classification context ('See this? Which one goes with this one?'). Fifty-four preschool children (16 with SLI, 16 children with typical language [TL] in an equated control group, and 22 additional children with TL included in individual differences analyses but not group comparisons) completed a battery of linguistic and cognitive assessments and two experiments. In Experiment 1, children made generalization choices in object naming and similarity classification contexts on separate days, from options similar to a target object in shape, color, or texture. On average, TL children exhibited the shape bias in an object naming context, but children with SLI did not. In Experiment 2, we tested whether the failure to exhibit the shape bias might be linked to ability to detect systematicities in the visual domain. Experiment 2 supported this hypothesis, in that children with SLI failed to learn simple paired visual associations that were readily learned by children with TL. Analyses of individual differences in the two studies revealed that visual paired-associate learning predicted degree of shape bias in children with SLI and TL better than any other measure of nonverbal intelligence or standard assessments of language ability. We discuss theoretical and clinical implications.
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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".