Visual filtering in time and space among persons with Down syndrome
Bibliographic record
Abstract
BACKGROUND: Individuals with Down syndrome (DS) appear to perform at a level that is commensurate with developmental expectations on simple tasks of selective attention. In this study, we examine how their selective attention is impacted by target changes that unfold over both time and space. This increased complexity reflects an attempt at greater ecological validity in an experimental task, as a steppingstone for better understanding attention among persons with DS in real-world environments. METHODS: A modified flanker task was used to assess visual temporal and spatial filtering among persons with DS (n = 14) and typically developing individuals (n = 14) matched on non-verbal mental age (mental age = 8.5 years). Experimental conditions included varying the stimulus onset asynchronies between the onset of the target and flankers, the distances between the target and flankers, and the similarity of the target and flankers. RESULTS: Both the participants with DS and the typically developing participants showed slower reaction times and lower accuracy rates when the flankers appeared closer in time and/or space to the target. CONCLUSION: No group differences were found on a broad level, but the findings suggest that dynamic stimuli may be processed differently by those with DS. Implications of the findings are discussed in relation to the developmental approach to intellectual disability originally articulated by Ed Zigler.
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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 | 0.001 | 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) | 0.002 | 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".