Virtual reality (VR) and head-mounted displays are constantly gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent virtual reality publications. This work proposes a novel experimental analysis using symbolic machine learning to rank potential causes of CS in VR games. We estimate CS causes and rank them according to their impact using classical machine learning. Experiments are performed using two virtual reality games and 6 experimental protocols along with 37 valid samples from a total of 88 volunteers. Our results show that rotation and acceleration triggered cybersickness more frequently in a flight game in contrast to a race game. We could also observe that subjects that are less experienced with VR are more prone to feel discomfort. Former experience plays a more important role on the race game, as this game provides more liberty to the user in terms of controllers, more displacement alternatives and a more user-controlled acceleration. Furthermore, different causes that trigger discomfort arise based on short or long term VR exposures. We suggest strategies for mitigating CS for these two scenarios: short and long term exposure experiences and compare the two highlighted scenarios (race and flight).

翻译：虚拟现实（VR）与头戴式显示器在教育、军事、娱乐及健康等领域的应用日益普及。尽管此类技术能提供高度沉浸感，但也可能引发不适症状。这种被称为"晕动症"（CS）的状况在近期虚拟现实研究中相当常见。本文提出了一种基于符号机器学习的创新实验分析方法，用于对VR游戏中晕动症潜在诱因进行排序。我们通过经典机器学习方法评估晕动症成因，并按其影响程度排序。实验采用两款虚拟现实游戏、6种实验方案，并基于88名志愿者中的37份有效样本展开。结果表明：与竞速游戏相比，飞行游戏中旋转与加速度更频繁地诱发晕动症。我们还发现，VR经验较少的受试者更易感到不适。在竞速游戏中，既往经验的影响更为显著——该游戏在控制器操作、位移选择及加速度控制方面赋予用户更大自由度。此外，短期与长期VR暴露会触发不同的不适诱因。我们针对这两种场景（短期与长期暴露体验）提出缓解晕动症的应对策略，并对竞速与飞行两类重点场景进行对比分析。