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Record W2952812138 · doi:10.25039/x46.2019.op26

INFLUENCE OF COLOUR ON VISUAL CONSPICUITY: TAKING SUBWAY ROUTE MAPS AS AN EXAMPLE

2019· article· en· W2952812138 on OpenAlexaff
Ting-Yun Lu, Li‐Chen Ou

Bibliographic record

VenuePROCEEDINGS OF the 29th Quadrennial Session of the CIE · 2019
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsPQ Corporation (Canada)
FundersMinistry of Science and Technology, Taiwan
KeywordsComputer scienceComputer visionComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

To investigate the influence of colour on visual conspicuity on subway route maps, two experiments were conducted in this study. Experiment 1 was the main experiment, aiming to develop a visual conspicuity model based on the experimental results. The experiment used coloured subway route maps as the stimuli, generated from a 15-route map designed by the research team, with the route colours varied for each test. During the experiment, each observer was asked to identify as fast as possible whether a target coloured route was present on that map, and the observer's reaction time was recorded for each test. The experimental results show that the larger colour difference between the target route and the other routes, the better visual conspicuity for the target. Experiment 2 was a verification test of this finding, showing a good predictive performance of the developed model in this study.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.331
Teacher spread0.292 · 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 teacher head, not a consensus.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venuePROCEEDINGS OF the 29th Quadrennial Session of the CIESame topicColor perception and designFrench-language works237,207