Breadth over depth in the semantic representations of adults with nonverbal learning disabilities
Bibliographic record
Abstract
abstract Oral language in individuals with nonverbal learning disabilities (NLD) has been described as empty of meaning, despite apparently average word knowledge. The present study explored the hypothesis that depth but not breadth of semantic representations would be reduced in NLD, and that depth but not breadth would be related to nonverbal gestalt perception. A cross-sectional design compared breadth and depth of vocabulary in 50 adults with or without a diagnosis of NLD. Vocabulary results were also compared with a visual closure test. Participants with NLD had reduced vocabulary depth in comparison with controls. The NLD group also had lower scores for gestalt perception, the ability to perceive a meaningful whole from unrelated parts. Across the sample, this measure predicted scores for vocabulary depth, but not breadth. The NLD group was also less able than the Control group to estimate the size of unknown, physical features of everyday objects. Results supported clinical observations that semantic representations are unconventional and imprecise in individuals with NLD, and suggested specific cognitive underpinnings for such difficulties. Results were also compatible with separate theories of embodied and lateralized semantics. A proposal uniting these theories in a designation over elaboration model is presented.
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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 | 0.000 | 0.005 |
| 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 | 0.000 |
| 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".