Multisensory Proximity and Transition Cues for Improving Target Awareness in Narrow Field of View Augmented Reality Displays
Bibliographic record
Abstract
Augmented reality applications allow users to enrich their real surroundings with additional digital content. However, due to the limited field of view of augmented reality devices, it can sometimes be difficult to become aware of newly emerging information inside or outside the field of view. Typical visual conflicts like clutter and occlusion of augmentations occur and can be further aggravated especially in the context of dense information spaces. In this paper, we evaluate how multisensory cue combinations can improve the awareness for moving out-of-view objects in narrow field of view augmented reality displays. We distinguish between proximity and transition cues in either visual, auditory or tactile manner. Proximity cues are intended to enhance spatial awareness of approaching out-of-view objects while transition cues inform the user that the object just entered the field of view. In study 1, user preference was determined for 6 different cue combinations via forced-choice decisions. In study 2, the 3 most preferred modes were then evaluated with respect to performance and awareness measures in a divided attention reaction task. Both studies were conducted under varying noise levels. We show that on average the Visual-Tactile combination leads to 63% and Audio-Tactile to 65% faster reactions to incoming out-of-view augmentations than their Visual-Audio counterpart, indicating a high usefulness of tactile transition cues. We further show a detrimental effect of visual and audio noise on performance when feedback included visual proximity cues. Based on these results, we make recommendations to determine which cue combination is appropriate for which application.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".