Past reports have suggested that active visual training in virtual reality (VR) can reduce symptoms of cybersickness. Here, we adapted such a protocol to a computer-based version and compared it with a passive exposure control condition. We employed heart rate and other subjective predictors of cybersickness to try to predict the efficacy of the intervention as well as likelihood of drop out. While we found a significant decrease in heart rate across sessions, the intervention we employed did not appear to be effective at reducing cybersickness or dropout. However, a heart rate increase of 15.5 bpm from baseline, nausea self-report of 4.5 on a scale of 1–10, and dizziness self-report of 5.5 on a scale of 1–10 predicted an equal probability of experiment dropout, independent of whether participants were in the experimental or control intervention condition. Our findings suggest that a single immersion of visual training in VR or passive VR exposure may not be sufficient to provide adaptation for VR. At the same time, our findings bolster past reports suggesting the value of employing heart rate monitoring, rather than subjective reports, to monitor the onset of cybersickness.

过去的报告表明，虚拟现实 (VR) 中的主动视觉训练可以减轻网络病的症状。在这里，我们将这样的协议改编为基于计算机的版本，并将其与被动曝光控制条件进行了比较。我们使用心率和其他网络病的主观预测因素来尝试预测干预的效果以及辍学的可能性。虽然我们发现在整个训练过程中心率显着降低，但我们采用的干预措施似乎无法有效减少网络病或辍学。然而，心率从基线增加 15.5 bpm，在 1-10 的范围内恶心自我报告为 4.5，在 1-10 范围内的头晕自我报告为 5.5 预测实验退出的概率相等，独立于参与者是否处于实验或控制干预条件。我们的研究结果表明，单次沉浸在 VR 中的视觉训练或被动 VR 暴露可能不足以提供对 VR 的适应。与此同时，我们的研究结果支持了过去的报告，这些报告表明使用心率监测而不是主观报告来监测网络病的发作是有价值的。