Longer Delays in Rehearsal-based Interfaces Increase Expert Use
Bibliographic record
Abstract
Rehearsal-based interfaces are designed to encourage a transition from novice to expert, but many users fail to make this transition. Most of these interfaces activate novice mode after a short delay, between 150 and 500 ms. We investigate the impact of delay time on expert usage and learning in three crowdsourced experiments. The first experiment examines an 8-item marking menu with delay times from 200 ms to 2 s. Results show longer delays increase successful expert selections. The second and third experiments generalise this result to a different rehearsal-based menu, a desktop clone of FastTap with 8 items and 15 items. Together, our results show that expert use correlates positively with increased delay time, but can increase errors since users are less risk averse. We also find imperceptible delays of 200 ms can harm long-term retention of menu items. Designers should consider longer delays in rehearsal-based interfaces to encourage a transition to expert usage.
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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 | 0.002 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".