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Record W2319781957 · doi:10.1017/s095457941200123x

Multiple-object tracking among individuals with Down syndrome and typically developing children

2013· article· en· W2319781957 on OpenAlexaff
Darlene A. Brodeur, Lana M. Trick, Heidi Flores, Caitlin Marr, Jacob A. Burack

Bibliographic record

VenueDevelopment and Psychopathology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicWilliams Syndrome Research
Canadian institutionsMcGill UniversityUniversity of GuelphHôpital Rivière-des-PrairiesAcadia University
Fundersnot available
KeywordsPsychologyTypically developingTracking (education)Object (grammar)Cognitive psychologyEye trackingDevelopmental psychologyEncoding (memory)Down syndromeArtificial intelligenceComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

We investigated differences in multiple-object tracking among individuals with Down syndrome (DS) as compared to typically developing children matched on a visual-spatial mental age of approximately 5.5 years. In order to ensure that these effects did not originate in differences in encoding or reporting the positions of targets in distracters after a delay, immediate and delayed report were measured for static items. Although their immediate and delayed report for multiple static items was comparable to that of the typically developing children, the participants with DS performed as if they were only capable of tracking a single item at a time regardless of the number of targets that needed to be tracked. This finding is surprising because the operations used in multiple-object tracking are thought to be necessary for visuospatial tasks, which are an area of relative strength among persons with DS. These results call into question the idea that abilities or deficits in multiple-object tracking predict visuospatial performance, and highlight ways that atypical development can inform our understanding of typical development.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2013
Admission routes1
Has abstractyes

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Same venueDevelopment and PsychopathologySame topicWilliams Syndrome ResearchFrench-language works237,207