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Record W2163793309 · doi:10.1002/hbm.23006

Evidence that smooth pursuit velocity, not eye position, modulates alpha and beta oscillations in human middle temporal cortex

2015· article· en· W2163793309 on OpenAlexaff
Benjamin T. Dunkley, Tom C. A. Freeman, Suresh Muthukumaraswamy, Krish D. Singh

Bibliographic record

VenueHuman Brain Mapping · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsSmooth pursuitBETA (programming language)Eccentricity (behavior)Eye movementAlpha (finance)Beta RhythmPhysicsAmplitudeNeurosciencePosition (finance)PsychologyElectroencephalographyOpticsDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Suppression of 5-25 Hz oscillations have been observed in MT+ during pursuit eye movements, suggesting oscillations that play a role in oculomotor control and/or the integration of extraretinal signals during pursuit. The amplitude of these rhythms appears to covary with head-centered eye position, but an alternative is that they depend on a velocity signal that lags the movement of the eyes. To investigate, we explored how alpha and beta amplitude changes related to ongoing eye movement depended on pursuit at different eccentricities. The results revealed largely identical patterns of modulation in the alpha and beta amplitude, irrespective of the eccentricity at which the pursuit eye movement was performed. The signals we measured therefore do not depend on head-centered position. A second experiment was designed to investigate whether the alpha and beta oscillations depended on the direction of pursuit, as opposed to just speed. We found no evidence that alpha or beta oscillations depended on direction, but there was a significant effect of eye speed on the magnitude of the beta suppression. This suggests distinct functional roles for alpha and beta suppression in pursuit behavior.

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 score0.929
Threshold uncertainty score0.963

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.175
GPT teacher head0.311
Teacher spread0.136 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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