Bibliographic record
Abstract
We face delays in a variety of situations. They are either inevitable, e.g., due to system limits, or are intentionally added, e.g., advertisements. In many situations, a visual feedback is provided during the delay to manage expectations. This feedback is usually provided through progress bars, percentages, or countdowns, depending on design limitations such as screen size. In this article, we use 15-second delays and examine (a) how delays affect users’ decision-making and task satisfaction, and (b) how to manipulate time perception to reduce the negative consequences of delays. Experiment 1 ( N =421) shows that faster countdowns increase task satisfaction and lead to more rational decisions in the subsequent task. In Experiment 2, we investigate the effect of countdown speed on delay perception and recall ( N =531). We show that faster countdowns lead to shorter perceived delays, while the delay will be recalled as longer after the task. The opposite is obtained for slower countdowns. We also increased the countdown rate and found a limit for the effect of increased speed. Thus, designers have to trade-off between how delays are perceived at the moment of experience and how they are recalled. We discuss the implications of these findings for user interface design.
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".