Color value of virtual spaces can affect on sadness in major depressive disorder.
Bibliographic record
Abstract
Visual preferences of colors are supposed to be different between people who suffer from major depressive disorder (MDD) and non-depressed people (Nolan et al, Perceptual and Motor Skill, 1995). However, this issue has not been investigated in relation to various dimensions of colors (hue, saturation, and value). Here, we investigated whether the preferences of value of the color are affected by depression, while the hue and saturation are controlled. Twenty MDD people and 37 non-depressed individuals participated in this study. 3D MAX was used to design an animation and participants changed colors (RGB model). We selected 18 hues (between 0 and 255/constant intervals, maximum saturation). In each hue we ask them to select the value (0 to 255) of colors in response to four questions (what is your preference to select a color; 1) for your consulting space?, 2) as your favorite color?, 3) that make you happy?, 4) that make you sad?), Bonferroni multiple-comparison correction indicated in response to question four, both groups selected low values but the MDD group selected significantly higher values than control in several hues (green H= 90,120,150, and yellow H=60). In the first question, they selected significantly lower value of color in just one hue (orange H=30). In response to question three, the MDD group chose significantly higher value just in one case (red H=0). These results suggest that the most significant differences are found in selection of colors in response to negative emotions (sadness). This is consistent with previous studies that suggested colors can affect on sadness in MDD (Hanada, Color Research & Application, 2018). But, more precisely, this study suggested that color values of different hues in an architectural space can affect on sadness in MDD. More investigations with neuroimaging approaches are required to find the neural basis of this mechanism.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".