Sensory and Perceptual Abilities in People With Down Syndrome
Bibliographic record
Abstract
During typical development, the efficiency with which distinct image attributes, objects such as faces, and emotions are analyzed depends on the development of ocular functioning (sensory level) and brain mechanisms mediating perceptual abilities (neural level). Compared to typically developing individuals, ocular differences are more frequent in persons with Down syndrome (DS) and include an increased incidence of refractive errors, i.e. hyperopia and myopia, and difficulties with accommodation (focusing up close). In most cases, these conditions can be corrected with bifocals, resulting in greatly improved literary skills that underlie reading, particularly for younger persons with DS. In addition, individuals with DS seem to benefit from the spatially structured presentation of visual material when learning, another accommodation that can be implemented during instruction. Finally, although not specific to DS, young children and adults with DS generally identify and recognize emotions less efficiently than do persons without DS. However, among persons with DS, these difficulties seem to be more pronounced for more intense emotions, including fear and anger. In sum, visual profiles based on sensory and perceptual performance can advance our understanding of atypical information processing among persons with DS, as well as provide practical information aimed at improving learning and instruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".