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Record W1964705003 · doi:10.1111/1467-9280.00233

Involuntary Listening Aids Seeing: Evidence From Human Electrophysiology

2000· article· en· W1964705003 on OpenAlexafffund
John J. McDonald, Lawrence M. Ward

Bibliographic record

VenuePsychological Science · 2000
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyStimulus modalityModality (human–computer interaction)Sensory systemCognitive psychologyModalitiesNeuroscienceActive listeningCommunication

Abstract

fetched live from OpenAlex

It is well known that sensory events of one modality can influence judgments of sensory events in other modalities. For example, people respond more quickly to a target appearing at the location of a previous cue than to a target appearing at another location, even when the two stimuli are from different modalities. Such cross-modal interactions suggest that involuntary spatial attention mechanisms are not entirely modality-specific. In the present study, event-related brain potentials (ERPs) were recorded to elucidate the neural basis and timing of involuntary, cross-modal spatial attention effects. We found that orienting spatial attention to an irrelevant sound modulates the ERP to a subsequent visual target over modality-specific, extrastriate visual cortex, but only after the initial stages of sensory processing are completed. These findings are consistent with the proposal that involuntary spatial attention orienting to auditory and visual stimuli involves shared, or at least linked, brain mechanisms.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.107
GPT teacher head0.438
Teacher spread0.331 · 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 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

Citations128
Published2000
Admission routes2
Has abstractyes

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