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Discrepancy between Motor and Cognitive Control in Adults with Intellectual Disabilities

2014· article· en· W2150655377 on OpenAlexvenueno aff
Shogo Hirata, Hideyuki Okuzumi, Yoshio Kitajima, Tomio Hosobuchi, Mitsuru Kokubun

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsPsychologyCognitionTask (project management)Motor controlIntellectual disabilityControl (management)AutismMotor skillIntelligence quotientMatching (statistics)Developmental psychologyCognitive psychologyPhysical medicine and rehabilitationAudiologyComputer scienceMedicineArtificial intelligencePsychiatryEngineering

Abstract

fetched live from OpenAlex

Purpose: To investigate the relationship between motor and cognitive control in adults with intellectual disabilities (ID), focusing on two aspects, speed and accuracy. Method: Participants were 62 adults with ID aged 20 to 47 years. Their intelligence quotients (IQ) ranged from 13 to 61. Nine of the adults with ID had Down syndrome, and 8 of the adults had autism. We conducted three tasks: seal affixation task, tray-carrying task, and the Matching Familiar Figures Test (MFFT). The seal affixation and tray-carrying tasks are motor tasks we devised that can separately measure the speed and accuracy of motor control. MFFT is a cognitive control task that can be used to evaluate cognitive styles, such as impulsive-reflective. Results: Adults with ID showed high motor accuracy and similar motor speed regardless of their MFFT performance. That is, discrepancies between motor and cognitive control existed in adults with ID. Conclusions: The results of this study indicate that some types of motor control problem may become unclear with growth. A longitudinal investigation focused on the motor skill development of persons with ID is therefore necessary.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.314
Teacher spread0.282 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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