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Record W4289704815 · doi:10.1111/jir.12958

Visual filtering in time and space among persons with Down syndrome

2022· article· en· W4289704815 on OpenAlexafffund
Erin S. M. Matsuba, Natalie Russo, Elizabeth P. McKernan, Ryan Curl, Tamara Dawkins, Heidi Flores, Margarita Miseros, Jillian Stewart, A. Loebus, Darlene A. Brodeur, Jacob A. Burack

Bibliographic record

VenueJournal of Intellectual Disability Research · 2022
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsAcadia UniversityDown Syndrome Research FoundationMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyStimulus (psychology)AudiologyMental ageDevelopmental psychologyTypically developingCognitionCognitive psychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0180.000

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.

Opus teacher head0.051
GPT teacher head0.362
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

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