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Record W2117064296 · doi:10.1113/jphysiol.2007.136192

Fast visuomotor processing made faster by sound

2007· article· en· W2117064296 on OpenAlexaff
Raymond F. Reynolds, Brian L. Day

Bibliographic record

VenueThe Journal of Physiology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsStimulus (psychology)Choice reaction timeSound (geography)Latency (audio)PsychologyComputer scienceCommunicationSpeech recognitionCognitive psychologyNeuroscienceAcousticsCognition

Abstract

fetched live from OpenAlex

Reaction time to a visual event can be dramatically reduced if the visual stimulus is accompanied by a startling sound. The mechanism may involve a motor programme being stored and triggered early by the sound. However, in a choice reaction task the required response is not known in advance, and so cannot be stored. In this case startling sound does not usually speed up the reaction and may even be detrimental to performance. Here we show that the reaction time of a special type of visually evoked movement can be substantially reduced by startling sound, even though the movement requires choice. The task involved stepping onto an illuminated target that sometimes moved mid-step left or right, requiring a foot trajectory adjustment. These adjustments occur at much shorter latency than conventional visuomotor reaction tasks and are thought to involve subcortical brain areas. The presence of the sound, which carried no information, shortened the already fast mean response time of 134 ms by approximately 20 ms. We attribute this to auditory-visual interaction since sound alone had no effect. Although we observed startle responses, the quickening effect was not contingent upon their presence. Given minimum motor and sensory conduction time, we estimate that the loud sound reduced the central visuomotor processing time by at least 30%.

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 score0.009
Threshold uncertainty score0.184

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 head0.034
GPT teacher head0.316
Teacher spread0.282 · 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

Citations78
Published2007
Admission routes1
Has abstractyes

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