How Are Your Participants Feeling Today? Accounting For and Assessing Emotions in Virtual Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emotions affect our perception, attention, and behavior. Hereby, the emotional state is greatly affected by the surrounding environment that can seamlessly be designed in Virtual Reality (VR). However, research typically does not account for the influence of the environment on participants’ emotions, even if this influence might alter acquired data. To mitigate the impact, we formulated a design space that explains how the creation of virtual environments influences emotions. Furthermore, we present EmotionEditor, a toolbox that assists researchers in rapidly developing virtual environments that influence and asses the users’ emotional state. We evaluated the capability of EmotionEditor to elicit emotions in a lab study (n=30). Based on interviews with VR experts (n=13), we investigate how they consider the effect of emotions in their research, how the EmotionEditor can prospectively support them, and analyze prevalent challenges in the design as well as development of VR user studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
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 it