Effects of visual conflicts on 3D selection task performance in stereoscopic display environments
Bibliographic record
Abstract
Mid-air direct-touch interaction in stereoscopic display environments poses challenges to the design of 3D user interfaces. Not only is passive haptic feedback usually absent when selecting a virtual object displayed with positive or negative parallax relative to a display surface, but such setups also suffer from inherent visual conflicts, such as vergence/accommodation mismatches and double vision. In particular, if the user tries to select a virtual object with a finger or input device, either the virtual object or the user's finger will appear blurred, resulting in an ambiguity for selections that may significantly impact the user's performance. In this paper we evaluate the effect of visual conflicts for mid-air 3D selection performance within arm's reach on a stereoscopic table with a Fitts' Law experiment. We compare three different techniques with different levels of visual conflicts for selecting a virtual object: real hand, virtual offset cursor, and virtual offset hand. Our results show that the error rate is highest for the real hand condition and less for the virtual offset-based techniques. However, our results indicate that selections with the real hand resulted in the highest effective throughput of all conditions. This suggests that virtual offset-based techniques do not improve overall performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".