Investigating Cross-Modal Approaches for Evaluating Error Acceptability of a Recognition-Based Input Technique
Bibliographic record
Abstract
Emerging input techniques that rely on sensing and recognition can misinterpret a user's intention, resulting in errors and, potentially, a negative user experience. To enhance the development of such input techniques, it is valuable to understand implications of these errors, but they can very costly to simulate. Through two controlled experiments, this work explores various low-cost methods for evaluating error acceptability of freehand mid-air gestural input in virtual reality. Using a gesture-driven game and a drawing application, the first experiment elicited error characteristics through text descriptions, video demonstrations, and a touchscreen-based interactive simulation. The results revealed that video effectively conveyed the dynamics of errors, whereas the interactive modalities effectively reproduced the user experience of effort and frustration. The second experiment contrasts the interactive touchscreen simulation with the target modality - a full VR simulation - and highlights the relative costs and benefits for assessment in an alternative, but still interactive, modality. These findings introduce a spectrum of low-cost methods for evaluating recognition-based errors in VR and a series of characteristics that can be understood in each.
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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 | 0.009 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".