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Record W2893620045 · doi:10.1177/1541931218621444

The Effects of Distraction on Anticipatory Driving

2018· article· en· W2893620045 on OpenAlexaff
Dengbo He, Birsen Donmez

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2018
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDistractionAnticipation (artificial intelligence)Driving simulatorTask (project management)PsychologyPoison controlHuman factors and ergonomicsApplied psychologyCognitive psychologyInjury preventionSimulationComputer scienceEngineeringMedicineMedical emergency

Abstract

fetched live from OpenAlex

The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., actions in preparation for a potential upcoming conflict) have been found to be more prevalent among experienced drivers in a driving simulator study where driving was the sole task. The influence of secondary tasks on anticipatory driving has not yet been investigated, despite the prevalence and negative effects of distraction widely documented in the literature. A driving simulator experiment was conducted with 16 experienced and 16 novice drivers to address this gap with half of the participants provided with a self-paced visual-manual secondary task. More anticipatory actions were observed among experienced drivers in general compared to novices; experienced drivers also exhibited more efficient visual scanning behaviors. Secondary task engagement reduced anticipatory actions for both experienced and novice drivers.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0010.000

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 head0.015
GPT teacher head0.303
Teacher spread0.287 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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