A Multimodal Approach for Targeting Error Detection in Virtual Reality Using Implicit User Behavior
Bibliographic record
Abstract
Although the point-and-select interaction method has been shown to lead to user and system-initiated errors, it is still prevalent in VR scenarios.Current solutions to facilitate selection interactions exist, however they do not address the challenges caused by targeting inaccuracy.To reduce the effort required to target objects, we developed a model that quickly detected targeting errors after they occurred.The model used implicit multimodal user behavioral data to identify possible targeting outcomes.Using a dataset composed of 23 participants engaged in VR targeting tasks, we then trained a deep learning model to differentiate between correct and incorrect targeting events within 0.5 seconds of a selection, resulting in an AUC-ROC of 0.9.The utility of this model was then evaluated in a user study with 25 participants that identified that participants recovered from more errors and faster when assisted by the model.These results advance our understanding of targeting errors in VR and facilitate the design of future intelligent error-aware systems.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".