Adults With Down Syndrome Demonstrate Peripheral, Not Central, Deficits When Integrating Movements During Multiple-Target Sequences
Bibliographic record
Abstract
The perceptual-motor impairments of individuals with Down syndrome (DS) are attributed to central (e.g., neurophysiology deficits that affect the retrieval or initiation of motor programs) and peripheral (e.g., anatomical deficits relating to issues with inertia of limb mechanics and muscle organization) processes. However, recent research suggests that central deficits do not affect the integration between movements. We investigate the impact of central and peripheral DS deficits on movement integration by examining the planning and execution of multiple-target multiple-arm movements. Individuals with DS, typically developing (TD), and individuals with an undifferentiated intellectual disability (UID) completed five aiming tasks: a one target; a one-arm, two-target extension; a two-arm, two-target extension (movement one was performed with one arm and movement two performed with the other); a one-arm, two-target reversal; and a two-arm, two-target reversal. Movement times (MTs) to the first target were longer in the two-target tasks compared with the one-target task. For the one-arm, two-target reversal task, this effect emerged only in individuals with DS. These results indicate that individuals with DS use central processing for movement integration similarly to their TD and UID counterparts but cannot exploit peripheral-level integration to enhance integration in one-arm reversal tasks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".