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

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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.