Waiting for the Web: How Screen Color Affects Time Perception
Bibliographic record
Abstract
The authors investigate the link between the color of a Web page's background screen while the page is downloading and the perceived quickness of the download. They draw on research that supports links between color and feelings of relaxation and between feelings of relaxation and time perception. The authors predict that the background screen color influences how quickly a page is perceived to download and that feelings of relaxation mediate this influence. In a series of experiments, they manipulate the hue, value, and chroma dimensions of the color to induce more or less relaxed feeling states. The findings suggest that for each dimension, colors that induce more relaxed feeling states lead to greater perceived quickness. The authors provide triangulating evidence with an alternative manipulation: the number of times subjects wait for a download. As does color, this also leads to variation in levels of relaxation and perceived quickness. A final experiment reveals that color not only affects perceived download quickness but also has consequences for users' evaluations of the Web site and their likelihood of recommending it to others.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".