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Record W2028578253 · doi:10.1167/9.1.13

Effect of visual field locus and oscillation frequencies on posture control in an ecological environment

2009· article· en· W2028578253 on OpenAlexafffund
Jean-Claude Piponnier, Jean-Marie Hanssens, Jocelyn Faubert

Bibliographic record

VenueJournal of Vision · 2009
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeripheral visionVisual fieldStimulus (psychology)PeripheralAmplitudePsychologyVisual controlAudiologyPhysical medicine and rehabilitationGeodesyCommunicationPhysicsComputer scienceComputer visionGeologyCognitive psychologyOpticsMedicineNeuroscience

Abstract

fetched live from OpenAlex

To examine the respective roles of central and peripheral vision in the control of posture, body sway amplitude (BSA) and postural perturbations (given by velocity root mean square or vRMS) were calculated in a group of 19 healthy young adults. The stimulus was a 3D tunnel, either static or moving sinusoidally in the anterior-posterior direction. There were nine visual field conditions: four central conditions (4, 7, 15, and 30 degrees); four peripheral conditions (central occlusions of 4, 7, 15, and 30 degrees); and a full visual field condition (FF). The virtual tunnel respected all the aspects of a real physical tunnel (i.e., stereoscopy and size increase with proximity). The results show that, under static conditions, central and peripheral visual fields appear to have equal importance for the control of stance. In the presence of an optic flow, peripheral vision plays a crucial role in the control of stance, since it is responsible for a compensatory sway, whereas central vision has an accessory role that seems to be related to spatial orientation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.364
Teacher spread0.354 · 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.

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

Citations29
Published2009
Admission routes2
Has abstractyes

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