The effects of biophilic design on steering performance in virtual reality
Bibliographic record
Abstract
Biophilic design aims to connect people to nature by incorporating natural elements such as plants in built environments. In immersive virtual reality (VR) environments, experiments have shown that, for instance, the presence of virtual plants in VR leads to higher cognitive performance and psychological well-being. However, it has not been investigated so far whether such effects extend to the performance of spatial interaction tasks such as path steering. In this paper, we explore the effects of the presence of virtual plants in an immersive virtual office environment on path steering performance in VR. For this purpose, we combined and replicated two previous studies in this field. The results of our study did not find an effect of the presence of virtual plants on steering time, as our participants performed all steering tasks in a similar amount of time in both biophilic and non-biophilic environments. We could, however, replicate one of the original studies on path steering in VR by Liu et al. and show that the steering time in our study also follows the extension of the steering law proposed in their work. Accordingly, we found not only a significant effect of the length ([Formula: see text], effect size: partial eta squared [Formula: see text]) and width ([Formula: see text], effect size =.96) of the steering path, but also a significant effect of the path curvature ([Formula: see text], effect size [Formula: see text]) on the time needed for steering through the path. We also found significant effects of path latitude ([Formula: see text], effect size [Formula: see text]) and longitude ([Formula: see text], effect size [Formula: see text]) on steering time. Our findings challenge the previous research, which demonstrated the positive effects of biophilic design in VR on cognitive processes, and therefore, pave the path for future work to better understand the effects of biophilic design on users' performance in VR.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".