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Record W3014993926 · doi:10.1038/s41598-020-62450-9

An unperceived acoustic stimulus decreases reaction time to visual information in a patient with cortical deafness

2020· article· en· W3014993926 on OpenAlexafffund
Anthony N. Carlsen, Dana Maslovat, Kimitaka Kaga

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceGovernment of Canada
KeywordsStimulus (psychology)AudiologyNeuroscienceCortical neuronsComputer sciencePsychologyMedicineCognitive psychology

Abstract

fetched live from OpenAlex

Responding to multiple stimuli of different modalities has been shown to reduce reaction time (RT), yet many different processes can potentially contribute to multisensory response enhancement. To investigate the neural circuits involved in voluntary response initiation, an acoustic stimulus of varying intensities (80, 105, or 120 dB) was presented during a visual RT task to a patient with profound bilateral cortical deafness and an intact auditory brainstem response. Despite being unable to consciously perceive sound, RT was reliably shortened (~100 ms) on trials where the unperceived acoustic stimulus was presented, confirming the presence of multisensory response enhancement. Although the exact locus of this enhancement is unclear, these results cannot be attributed to involvement of the auditory cortex. Thus, these data provide new and compelling evidence that activation from subcortical auditory processing circuits can contribute to other cortical or subcortical areas responsible for the initiation of a response, without the need for conscious perception.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.022
GPT teacher head0.314
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2020
Admission routes2
Has abstractyes

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