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Record W2131490110 · doi:10.1093/cercor/bhl084

Activation of Area MT/V5 and the Right Inferior Parietal Cortex during the Discrimination of Transient Direction Changes in Translational Motion

2006· article· en· W2131490110 on OpenAlexafffund
Julio Martínez-Trujillo, Douglas Cheyne, William Gaetz, Evgueni Simine, John K. Tsotsos

Bibliographic record

VenueCerebral Cortex · 2006
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork UniversityHospital for Sick ChildrenMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMagnetoencephalographyNeuroscienceFunctional magnetic resonance imagingSensory systemParietal lobeMotion perceptionPerceptionPosterior parietal cortexTransient (computer programming)PsychologyComputer scienceElectroencephalography

Abstract

fetched live from OpenAlex

The perception of changes in the direction of objects that translate in space is an important function of our visual system. Here we investigate the brain electrical phenomena underlying such a function by using a combination of magnetoencephalography (MEG) and magnetic resonance imaging. We recorded MEG-evoked responses in 9 healthy human subjects while they discriminated the direction of a transient change in a translationally moving random dot pattern presented either to the right or to the left of a central fixation point. We found that responses reached their maximum in 2 main regions corresponding to motion processing area middle temporal (MT)/V5 contralateral to the stimulated visual field, and to the right inferior parietal lobe (rIPL). The activation latencies were very similar in both regions ( approximately 135 ms) following the direction change onset. Our findings suggest that area MT/V5 provides the strongest sensory signal in response to changes in the direction of translational motion, whereas area rIPL may be involved either in the sensory processing of transient motion signals or in the processing of signals related to orienting of attention.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.246

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 head0.025
GPT teacher head0.261
Teacher spread0.237 · 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

Citations42
Published2006
Admission routes2
Has abstractyes

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