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Record W2765424864 · doi:10.1177/1541931213601956

Destination, Seen Unclearly: Relevance of Head-Up Display Information to Driving Is Unrelated to Its Processing

2017· article· en· W2765424864 on OpenAlexaff
Robert J. Sall, Sijing Wu, Ian Spence, Jing Feng

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2017
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRelevance (law)Task (project management)Affect (linguistics)Primary (astronomy)Computer scienceInformation processingCognitive psychologyPsychologyCommunicationEngineering

Abstract

fetched live from OpenAlex

Studies have shown that secondary information presented in spatially commingled environments along with primary tasks can adversely affect the performance of that task, as might be the case for information delivered to drivers by a Heads-Up Display (HUD). However, it is unclear how that message’s relevance to the task at hand (e.g., driving) might impact either detection of that message, or performance of a primary task. In this study, participants were asked to enumerate cars on a screen in an experiment that sporadically presented them with semantically similar and dissimilar pieces of information in a spatially intermingled environment. The results indicate that when occupying the same spatial proximity as the primary stimuli, all secondary stimuli disrupt primary task performance, regardless of its relevance to the primary task.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.025
GPT teacher head0.325
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 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
Published2017
Admission routes1
Has abstractyes

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