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Record W2151720026 · doi:10.3389/fnbeh.2013.00015

The role of areas MT+/V5 and SPOC in spatial and temporal control of manual interception: an rTMS study

2013· article· en· W2151720026 on OpenAlexaff
Joost C. Dessing, Michael Vesia, J. Douglas Crawford

Bibliographic record

VenueFrontiers in Behavioral Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsCanadians Living with HIVKrembil FoundationYork UniversityUniversity of WaterlooSunnybrook Health Science Centre
Fundersnot available
KeywordsInterceptionTranscranial magnetic stimulationTrajectoryComputer scienceNeural activityNeuroscienceTemporal cortexPsychologyArtificial intelligenceCartographyCommunicationGeographyStimulationBiologyPhysics

Abstract

fetched live from OpenAlex

Manual interception, such as catching or hitting an approaching ball, requires the hand to contact a moving object at the right location and at the right time. Many studies have examined the neural mechanisms underlying the spatial aspects of goal-directed reaching, but the neural basis of the spatial and temporal aspects of manual interception are largely unknown. Here, we used repetitive transcranial magnetic stimulation (rTMS) to investigate the role of the human middle temporal visual motion area (MT+/V5) and superior parieto-occipital cortex (SPOC) in the spatial and temporal control of manual interception. Participants were required to reach-to-intercept a downward moving visual target that followed an unpredictably curved trajectory, presented on a screen in the vertical plane. We found that rTMS to MT+/V5 influenced interceptive timing and positioning, whereas rTMS to SPOC only tended to increase the spatial variance in reach end points for selected target trajectories. These findings are consistent with theories arguing that distinct neural mechanisms contribute to spatial, temporal, and spatiotemporal control of manual interception.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.422

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 studies0.0000.001
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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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