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Record W2081286620 · doi:10.1167/11.11.900

Corrective response reaction times and multi-motor coordination after countermanding failures

2011· article· en· W2081286620 on OpenAlexaff
Gordon Tao, Gunnar Blohm

Bibliographic record

VenueJournal of Vision · 2011
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsQueen's University
Fundersnot available
KeywordsFixation (population genetics)Stop signalElectromyographyVisual feedbackComputer scienceSIGNAL (programming language)Response inhibitionPsychologyCommunicationArtificial intelligencePhysical medicine and rehabilitationNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Operating in a dynamic environment often requires inhibition of responses. Countermanding tasks have been used extensively to probe the mechanisms behind inhibition. We used a countermanding task to investigate the processes at play during failure of inhibition. Specifically, we investigated the reaction times for correcting mistakes. Participants were instructed to respond to a 30° visual target presented randomly left or right of central fixation by orienting their eyes, head, and arm to the target. A visual stop signal was presented in 30% of the trials at central fixation, 25, 75, 125, 175 or 225 ms after target appearance and prompted participants to cancel their response. In case of inhibition failure, participants were instructed to reorient all effectors back to center. Reaction times (RT) and times to correct a failed stop (CRT) were measured for all effectors using video eye tracking (Chronos Vision), 3D infrared marker tracking of the head and arm (Optotrak), and electromyography of shoulder and neck muscles (DelSys EMG). Estimated stop signal reaction times (SSRT) measured the efficiency of response inhibition. The RT distributions and SSRTs of all effectors were consistent with predictions from a dual LATER model describing a race between a ‘go’ and a ‘stop’ process. We extended this model to capture CRT distributions. We found a delay between the end of the go/stop race and the corrective response onset. This delay could be characterized by a second ‘go’ process starting after the previous stop signal reached threshold. Correlations of latencies (RTs and CRTs) between effectors suggest a supervisory control mechanism for both initial and corrective responses, pointing towards effector-specific decision processes receiving input from the common controller. Our results demonstrate that correcting incorrect responses relies on processes similar to the ones governing response initiation and inhibition and that this is true across all effectors.

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)0.001
metaresearch head score (Gemma)0.012
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.301
Teacher spread0.274 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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