The neural basis of multisensory processing during self motion in real and virtual environments

真实和虚拟环境中自我运动过程中多感官处理的神经基础

• 批准号: RGPIN-2020-03977
• 负责人: BarnettCowan, Michael
• 金额: $ 2.91万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739600
• 关键词: neural; basis; multisensory; processing; during

To safely interact with the environment, the central nervous system (CNS) synthesizes information across our senses to guide decision-making and behaviour. As multisensory integration affects how decisions and actions are executed, knowing how the CNS does this is fundamental to understanding neural function and disorder. However, we know relatively little about how sensory information is processed during self-motion or how to predict motion sickness incidence when multisensory events present conflicting information, which is currently the main limiting barrier of virtual reality technology adoption. My lab researches how human observers respond to conflicting multisensory information that varies in space and time. Our work is conducted in both real and in virtual environments using virtual and augmented reality technology. Having made significant contributions to advancing our basic understanding of multisensory processing during self-motion using primarily behavioural data, this proposal will develop three objectives: (1) Temporal processing of multisensory events during self-motion. Parametric assessment of human self-motion paired with physiological and neural responses to self-motion will be used to construct a theoretical model of the neural mechanisms that sub serve multisensory integration over time. (2) Sensory cue integration during self-motion. Maximum-likelihood estimation can often be used to reliably predict how sensory cues are integrated by the CNS. Here, we will assess whether discrepancies in finding optimal integration of visual and vestibular cues in the literature result from change in visual sensitivity during self-motion and perceived temporal delays between sensory cues, as well as the role of individual differences in the neural activity recorded that is associated with perceptual decision-making. (3) Motion sickness countermeasures during self-motion. Our work has demonstrated the potential of predicting motion sickness susceptibility primarily from assessments of visual optic flow on balance control in addition to other factors such as thresholds for self-motion. Other work has shown comparable predictions for motion sickness, using physiological recordings in response to sensory conflict such as heart rate, perspiration, respiration, and gastric responses. Here we propose to combine these methods to assess whether motion sickness in real and virtual environments can be better predicted. HQP will gain analytical, technical, and presentation skills to be highly competitive in today's skills and knowledge-based economy. Knowing how multisensory information is processed and how it affects perceptual decision-making and action is critical not only to understand how the CNS works but also to reduce the incidence of motion sickness, and errors made in sensory conflicting environments which is holding back wider adoption of cost-saving technology in the aviation, space, design, mobile computing, and healthcare industries.

为了安全地与环境互动，中枢神经系统(CNS)通过感官合成信息来指导决策和行为。由于多感官整合影响决策和行动的执行方式，了解中枢神经系统是如何做到这一点的，是理解神经功能和紊乱的基础。然而，我们对自我运动过程中感觉信息是如何处理的，或者当多感觉事件呈现相互冲突的信息时如何预测晕动病的发生情况知之甚少，这是目前虚拟现实技术采用的主要限制障碍。我的实验室研究人类观察者如何应对在空间和时间上变化的相互冲突的多感官信息。我们的工作是使用虚拟和增强现实技术在真实和虚拟环境中进行的。本研究主要利用行为学数据，对促进我们对自我运动过程中多感觉加工的基本理解做出了重大贡献，并将发展三个目标：(1)自我运动过程中多感觉事件的时间加工。人类自我运动的参数评估与对自我运动的生理和神经反应相结合，将被用来构建一个神经机制的理论模型，这些神经机制随着时间的推移而服务于多感觉整合。(2)自我运动过程中的感觉线索整合。最大似然估计通常可以用来可靠地预测中枢神经系统如何整合感觉线索。在这里，我们将评估文献中在寻找视觉和前庭线索的最佳整合方面的差异是否源于自我运动过程中视觉敏感度的变化和感觉线索之间的时间延迟，以及个体差异在与知觉决策相关的神经活动中的作用。(3)自主运动时的晕动病对策。我们的工作已经证明，除了自我运动阈值等其他因素外，主要通过对平衡控制的视觉光流的评估来预测运动病易感性的可能性。其他研究也显示了类似的对晕动病的预测，使用了对感觉冲突的反应，如心率、出汗、呼吸和胃部反应的生理记录。在这里，我们建议结合这些方法来评估是否可以更好地预测真实和虚拟环境中的晕动病。HQP将获得分析、技术和演示技能，以便在当今的技能和知识经济中具有很强的竞争力。了解多感官信息是如何处理的，以及它如何影响感知决策和行动，不仅对于了解中枢神经系统是如何工作的，而且对于减少晕动病和在感官冲突环境中犯下的错误的发生率至关重要，这阻碍了节省成本的技术在航空、空间、设计、移动计算和医疗保健行业的广泛采用。