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Record W1807823257 · doi:10.3233/ves-2006-16603

Linear vection as a function of stimulus eccentricity, visual angle, and fixation

2007· article· en· W1807823257 on OpenAlexaff
Luminita Tarita‐Nistor, Esther G. González, Ashley J. Spigelman, Martin J. Steinbach

Bibliographic record

VenueJournal of Vestibular Research · 2007
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsToronto Western HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsStimulus (psychology)PeripheralFixation (population genetics)AudiologyPsychologySensationNeuroscienceCommunicationMedicineCognitive psychologyInternal medicinePopulation

Abstract

fetched live from OpenAlex

The effects of stimulus eccentricity, visual angle, and fixation on linear vection (sensation of self-translation induced by large moving scenes) were examined in healthy young people. Three aspects of vection were measured: latency, total vection time, and strength. The results showed that when peripheral and central stimuli are equal in area, they induce similar vection, but only when they are presented with a fixation cross. When presented without a fixation cross, peripheral stimuli are more effective in inducing vection than central stimuli. In addition, central stimuli with a fixation cross elicited more vection than central stimuli without a fixation cross. Fixation had no influence on the vection induced by peripheral stimuli. These findings indicate that statements about the role of central and peripheral stimuli of equal area in inducing vection should be made only in conjunction with reports about whether these stimuli are presented with or without fixation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.122
GPT teacher head0.448
Teacher spread0.327 · 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 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

Citations29
Published2007
Admission routes1
Has abstractyes

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