How Interaction Techniques Affect Workload in a Virtual Environment During Multitasking
Bibliographic record
Abstract
Abstract Virtual environments (VEs) can be modulated and adapted to the needs of each user, but in the case of patients, performance can be affected by several factors that are difficult to expertise. The objective of the study is to explore the relationship between workload and the interaction techniques used during selection in a virtual apartment. Fifty-six participants performed tasks in a VE with 2D or, more immersive, 3D interaction techniques. The VE used was the Virtual Multitasking Test (Banville et al., 2018) where participants realized several everyday tasks in a virtual apartment. Workload and variables describing how individuals felt in the VE were measured using questionnaires, and performance in VE has been assessed. Results showed that 2D selection techniques have a better usability than 3D ones. The performance (success in task realization) on the virtual tasks was not impacted by the interaction techniques. Our results suggest that the easier it is to use an interaction technique, the less workload is associated with it. Sense of presence and cybersickness were affected by 3D interaction techniques. Thus, future VEs for cognitive assessment and rehabilitation, based on instrumental activities of daily living, will have to adapt their interaction techniques to different users.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".