Effects of Different Visual Feedback Types on Perception of Online Wait
Bibliographic record
Abstract
This study aims to understand the effects of visual feedback designs on time perception and user perception in online wait. We manipulated the salience (implicit/explicit) and framing (hedonic/function) of visual feedback, and the music (exist or not). 23 subjects participated in the two experiments. 8 (2*2*2) visual feedbacks were compared directly in pairs to rank how these augmentations of visual feedbacks compare to one another. We also tested the effects of visual designs on waiting perception such as attention, perceived control, and emotion in the online waiting. In addition, we discussed the potential effects of music and immersion for time perception. MANOVA and subsequent ANOVA tests were conducted. The study findings indicate the salience of visual feedback may significantly affect users’ time perception, explicit visual feedback provides users with more perceived control as well as more attention. The hedonic of visual feedback is important to affect users’ waiting perception such as. The hedonic designs provide more perceived entertainment. However, inappropriate embellishment design may have a negative effect on user experience. Music plays a significant role in affecting users’ time perception and deep involvement. Appropriate music accompaniment will make the whole waiting faster and easier.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".