Exploring the Effects of Virtually-Augmented Display Sizes on Users’ Spatial Memory in Smartwatches
Bibliographic record
Abstract
The small display size of the smartwatches makes it difficult to display large amounts of information on the device. Prior work explored leveraging a second device (e.g., Head-mounted displays) to extend the space where users can access large information space with virtual displays anchored on their wrists. Though researchers showed that having an additional virtual screen increased information bandwidth, little is known about the effect of virtual display sizes on users’ performance. In this paper, we examined the impact of display sizes on spatial memory, workload, and user experience to better understand the prospects of virtually-augmented displays for smartwatches. Results from a user study revealed that a 4.8 inches display size can be the “sweet spot” for the virtually-augmented displays to ensure improved spatial memory performance and better user experience with less workload. Finally, we provided a set of design guidelines focusing to display size, spatial memory, user experience, and workload for designing virtually augmented user interfaces for smartwatches.
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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 | 0.022 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".