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Record W2022687896 · doi:10.1371/journal.pone.0090498

Enhancement of Visual Motion Detection Thresholds in Early Deaf People

2014· article· en· W2022687896 on OpenAlexafffund
Martha M. Shiell, François Champoux, Robert J. Zatorre

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversité de MontréalMontreal Neurological Institute and HospitalInternational Laboratory for Brain, Music and Sound ResearchMcGill UniversityCentre for Research on Brain Language and Music
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMotion (physics)AudiologySensory systemPsychologyMotion perceptionMotion detectionTask (project management)Sensory processingVisual cortexComputer scienceCognitive psychologyNeuroscienceComputer visionMedicine

Abstract

fetched live from OpenAlex

In deaf people, the auditory cortex can reorganize to support visual motion processing. Although this cross-modal reorganization has long been thought to subserve enhanced visual abilities, previous research has been unsuccessful at identifying behavioural enhancements specific to motion processing. Recently, research with congenitally deaf cats has uncovered an enhancement for visual motion detection. Our goal was to test for a similar difference between deaf and hearing people. We tested 16 early and profoundly deaf participants and 20 hearing controls. Participants completed a visual motion detection task, in which they were asked to determine which of two sinusoidal gratings was moving. The speed of the moving grating varied according to an adaptive staircase procedure, allowing us to determine the lowest speed necessary for participants to detect motion. Consistent with previous research in deaf cats, the deaf group had lower motion detection thresholds than the hearing. This finding supports the proposal that cross-modal reorganization after sensory deprivation will occur for supramodal sensory features and preserve the output functions.

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 score0.013
Threshold uncertainty score0.244

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 head0.046
GPT teacher head0.267
Teacher spread0.221 · 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

Citations85
Published2014
Admission routes2
Has abstractyes

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