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Record W2888669144 · doi:10.1177/1747021818798824

The effect of galvanic vestibular stimulation on path trajectory during a path integration task

2018· article· en· W2888669144 on OpenAlexafffund
Tanya Karn, Michael E. Cinelli

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2018
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGalvanic vestibular stimulationVestibular systemTask (project management)TrajectoryPath (computing)Path integrationGalvanic cellStimulationPhysical medicine and rehabilitationAudiologyPsychologyComputer scienceMedicineNeurosciencePhysicsEngineeringMaterials science

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the effects of galvanic vestibular stimulation (GVS) on path trajectory and body rotation during a triangle completion task. Participants ( N = 17, female, 18-30 years) completed the triangle completion task in virtual reality using two different size triangles. GVS was delivered at three times each participant's threshold in either the left or right direction prior to the final leg of the triangle and continued until the participant reached their final position. Whole body kinematics were collected using an NDI Optotrak motion tracking system. Results revealed a significant main effect of GVS on arrival error such that no GVS (NGVS) had significantly smaller arrival errors than when GVS was administered. There was also a significant main effect of GVS on angular error such that NGVS had significantly smaller error than GVSaway and GVStowards. There was no significant difference between GVS trials in path variability during the final leg on route to the final position. These results demonstrate that vestibular perturbation reduced the accuracy of the triangle completion task, affecting path trajectory and body position during a path integration task in the absence of visual cues.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.346
Teacher spread0.325 · 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 designBench or experimental
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

Citations11
Published2018
Admission routes2
Has abstractyes

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