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Record W2054845064 · doi:10.1177/7010.2006.00238

Saccadic Adaptation in Children

2006· article· en· W2054845064 on OpenAlexaff
Michael S. Salman, James Sharpe, Moshe Eizenman, Linda Lillakas, Teresa To, Carol A. Westall, Maureen Dennis, Martin J. Steinbach

Bibliographic record

VenueJournal of Child Neurology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity Health NetworkHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSaccadic maskingSaccadeAdaptation (eye)Eye movementMotor learningPsychologyTask (project management)Saccadic suppression of image displacementAudiologyCognitive psychologyNeurosciencePhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Saccades are fast-orienting eye movements. Saccadic adaptation, a form of motor learning, is a corrective change in the amplitude of saccades in response to error. The aim of the study was to ascertain whether saccadic adaptation occurs in typically developing children. We recorded saccades with an infrared eye tracker in 39 children, aged 8 to 19 years, at baseline to 12-degree horizontal target steps and after an adaptive task. During the adaptive task, a saccadic hypometric error was induced. This task consisted of 200 12-degree target steps that stepped backward 3 degrees during the initial saccade and without the participants' awareness. The initial saccade triggered the back-step. This paradigm required a corrective reduction of the amplitude of the initial saccades in response to the induced error. Saccadic adaptation was achieved in 26 participants, whose mean saccadic amplitudes decreased by 13% (P < .05). Saccadic adaptation was not influenced by age. We conclude that children as young as 8 years old have established functions of the neural circuits responsible for the motor learning required for saccadic adaptation.

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)0.002
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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
Published2006
Admission routes1
Has abstractyes

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