How Color Properties Can Be Used to Elicit Emotions in Video Games
Bibliographic record
Abstract
Classifying the many types of video games is difficult, as their genres and supports are different, but they all have in common that they seek the commitment of the player through exciting emotions and challenges. Since the income of the video game industry exceeds that of the film industry, the field of inducting emotions through video games and virtual environments is attracting more attention. Our theory, widely supported by substantial literature, is that the chromatic stimuli intensity, brightness, and saturation of a video game environment produce an emotional effect on players. We have observed a correlation between the RGB additives color spaces, HSV, HSL, and HSI components of video game images, presented to n = 85 participants, and the emotional statements expressed in terms of arousal and valence, recovered in a subjective semantic questionnaire. Our results show a significant correlation between luminance, saturation, lightness, and the emotions of joy, sadness, fear, and serenity experienced by participants viewing 24 video game images. We also show strong correlations between the colorimetric diversity, saliency volume, and stimuli conspicuity and the emotions expressed by the players. These results allow us to propose video game environment development methods in the form of a circumplex model. It is aimed at game designers for developing emotional color scripting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".