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Record W2050404578 · doi:10.3104/reports.137

Performing movement sequences with knowledge of results under different visual conditions in adults with Down syndrome

2003· letter· en· W2050404578 on OpenAlexaff
Naznin Virji‐Babul, Jennifer Lloyd, Geraldine Van Gyn

Bibliographic record

VenueDown Syndrome Research and Practice · 2003
Typeletter
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsDown Syndrome Research Foundation
Fundersnot available
KeywordsMovement (music)PsychologyTask (project management)Physical medicine and rehabilitationVisual feedbackCognitive psychologyComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the learning of movement sequences in individuals with Down syndrome under different visual information conditions. 10 adults with Down syndrome and 14 neurologically typical adults performed a sequence of movements under two different visual information conditions: full visual feedback of the limb and environment and no visual feedback of the limb. Participants were given knowledge of results of their total movement time after each trial. The entire task was presented as a game and movement time information was given as a "score" after each trial. Participants were also given verbal encouragement throughout the task. As expected, individuals with Down syndrome had significantly slower reaction and movement times than neurologically typical participants. Interestingly, however, mean reaction and movement time was not affected by the visual condition, in either group. Participants with Down syndrome improved their performance over the presented trials, in both visual information conditions. These findings indicate that providing knowledge of results of movement performance can facilitate the performance and coordination of movement sequences even under conditions where visual information of the moving limb is restricted.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.360
Teacher spread0.292 · 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 designNot applicable
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

Citations9
Published2003
Admission routes1
Has abstractyes

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