Back

Using green space virtual reality to prevent stress-induced working memory impairment

Xu, Y.; Yang, Z.; Zhou, X.; Yao, Q.; Dai, G.; Liu, W.; Zhao, H.

2025-01-02 neuroscience
10.1101/2024.12.31.630298 bioRxiv
Show abstract

Exposure to green space is associated with both physical and mental health benefits, including the potential to buffer acute stress responses, positioning it as a promising non-pharmacological approach to protect cognitive functions against stress. However, urban residents often face significant barriers in accessing green spaces, which are not equitably distributed. Virtual Reality (VR) technology offers a potential solution by simulating green space that could be made accessible to a broader demographic. Here, we explored whether VR-stimulated green space could dampen biological stress responses (i.e., heart rate and cortical response) and prevent stress-induced working memory (WM) impairment. Healthy young participants underwent acute stress induction followed by 15 minutes of VR-based green space (N=36) or control empty space (N=30) intervention. Although we observed the expected stress-induced increase in heart rates and elevated cortisol levels under stress, VR green space exposure failed to temper cortisol responses compared to the control VR space. Further, VR green space exposure did not bring benefits to protect working memory performance under stress across three WM tasks. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VR-based green space exposure on biological markers of acute stress responses and working memory performance. Our findings suggest that VR-generated green space may not effectively replicate the stress-buffering effects of actual green space exposure. We discussed the implications of our findings regarding the potential and limitations of using VR or green space exposure to buffer stress responses.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.