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Record W4402452689 · doi:10.11159/mhci24.101

The Power Of Colors To Maximize Attention And Readability In Visual Communication: Insights From An Eye-Tracking Behavioural Study

2024· article· en· W4402452689 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsnot available
Fundersnot available
KeywordsReadabilityEye trackingComputer scienceTracking (education)Visual attentionArtificial intelligenceVisual communicationComputer visionHuman–computer interactionPsychologyMultimediaCognitionNeuroscience

Abstract

fetched live from OpenAlex

With the increase in digital visual content and the need to capture and maintain audience attention, understanding the impact of colors on attention and legibility is essential.This research explores the role of colors in visual communication and their influence on human attention and reading difficulty.The two primary goals of this study were to determine which colors are more visually appealing and which are more readable.We used eye-tracking technology (Gazepoint) to monitor 27 participants with an average age of 19.125 years (SD= 0.95) as they read slides with different background colors, aiming to discover which colors attract more attention and provide better legibility.When it comes to the colors that draw the most attention, yellow was the color that people see the most, appearing in 49 different instances.Blue has 19 occurrences, orange has 23 occurrences, and green has 31 occurrences.On the other hand, red and purple attracted less attention, with only 8 occurrences for red and 5 for purple.In relation to pupil dilation for different colours, it was observed that the average dilation values were similar, suggesting no significant difference in pupillary response regarding the attention and concentration required during the reading of the evaluated slides.However, when considering the maximum dilation values, it was observed that black, followed by purple and green, caused a more pronounced pupil dilation.On the other hand, red and yellow showed the lowest maximum dilations, suggesting reading that requires less focus.Similarly, when analysing the minimum dilation values, it was found that purple, followed by black and orange, resulted in lower minimum dilations, indicating less concentration required during reading.On the other hand, yellow, green, and black recorded the highest minimum dilations, suggesting a higher level of required concentration.This suggests that these colors can be deliberately employed to draw attention and guide the viewer's gaze.These are the intriguing results about the effects of color on attention and legibility.Additionally, this knowledge has consequences for advancing communication techniques and increasing accessibility.In conclusion, future research on the individual, contextual, and multidimensional subtleties of color perception present a wealth of opportunities for enhancing design techniques, visual communication, and marketing strategies.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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