MétaCan
Menu
Back to cohort
Record W2765231191 · doi:10.1044/2017_jslhr-l-16-0457

Procedural Motor Learning in Children With Specific Language Impairment

2017· article· en· W2765231191 on OpenAlexaff
Teenu Sanjeevan, Elina Mainela‐Arnold

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2017
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProcedural memorySpecific language impairmentPsychologyMotor skillCognitive psychologySerial reaction timeTask (project management)Typically developingTyingDevelopmental psychologyAudiologyCognitionSequence learningNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Purpose: Specific language impairment (SLI) is a developmental disorder that affects language and motor development in the absence of a clear cause. An explanation for these impairments is offered by the procedural deficit hypothesis (PDH), which argues that motor difficulties in SLI are due to deficits in procedural memory. The aim of this study was to test the PDH by examining the procedural motor learning abilities of children with and without SLI. Method: Thirteen children with SLI and 14 age-matched typically developing children completed the following procedural measures: (a) a knot-tying task as a measure of motor sequencing and (2) a mirror-drawing task as a measure of visual-motor adaptation. Results: Although children with SLI produced significantly more errors on certain knot-tying tasks, they performed comparably on others. Also, children with SLI performed comparably with typically developing children on the mirror-drawing task. Conclusions: The PDH requires reframing. The sequence learning deficits in SLI are modest and specific to more difficult tasks. Visual-motor adaptation, on the other hand, appears to be unaffected in SLI.

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)0.001
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.365
Teacher spread0.330 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Speech Language and Hearing ResearchSame topicChildren's Physical and Motor DevelopmentFrench-language works237,207