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Record W2108332174 · doi:10.1518/0018720024494874

Effect of a Concurrent Auditory Task on Visual Search Performance in a Driving-Related Image-Flicker Task

2002· article· en· W2108332174 on OpenAlexafffund
Christian Richard, Richard D. Wright, Cheryl Ee, Steven L. Prime, Yujiro Shimizu, John Vavrik

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2002
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsYork UniversitySimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsFlickerTask (project management)SAFERVisual searchPoison controlAudiologyPsychologyProcess (computing)Computer scienceDriving simulatorCognitive psychologyCommunicationSimulationEngineeringComputer securityMedicineComputer graphics (images)Medical emergency

Abstract

fetched live from OpenAlex

The effect of a concurrent auditory task on visual search was investigated using an image-flicker technique. Participants were undergraduate university students with normal or corrected-to-normal vision who searched for changes in images of driving scenes that involved either driving-related (e.g., traffic light) or driving-unrelated (e.g., mailbox) scene elements. The results indicated that response times were significantly slower if the search was accompanied by a concurrent auditory task. In addition, slower overall responses to scenes involving driving-unrelated changes suggest that the underlying process affected by the concurrent auditory task is strategic in nature. These results were interpreted in terms of their implications for using a cellular telephone while driving. Actual or potential applications of this research include the development of safer in-vehicle communication devices.

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)0.016
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
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.323
Teacher spread0.300 · 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

Citations70
Published2002
Admission routes2
Has abstractyes

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