Integrating neural and ocular attention reorienting signals in virtual reality
Integrating neural and ocular attention reorienting signals in virtual reality
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DOI：
10.1088/1741-2552/ac4593
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发表时间：
2021-12
影响因子：
4
通讯作者：
Pawan Lapborisuth;Sharath C. Koorathota;Qi Wang;P. Sajda
中科院分区：
文献类型：
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作者：
Pawan Lapborisuth;Sharath C. Koorathota;Qi Wang;P. Sajda
Objective.重新定向是人类如何将注意力引导到环境中不同刺激的核心。以往的研究通常采用控制良好的范式与有限的眼睛和头部运动，研究神经和生理过程的注意力重新定向。在这里，我们的目标是更好地理解凝视和注意力重新定向之间的关系，使用自然主义的虚拟现实（VR）为基础的目标检测范式。Approach.受试者被引导穿过一个城市，并被指示计数出现在街上的目标的数量。受试者在固定的条件下没有头部运动，并在自由的条件下，头部运动是允许的任务。收集脑电图（EEG）、凝视和瞳孔数据。为了研究神经和生理重定向信号如何分布在不同的凝视事件中，我们使用分层判别成分分析（HDCA）来识别EEG和基于瞳孔的判别成分。混合效应一般线性模型（GLM）被用来确定这些区分组件和不同的凝视事件时间之间的相关性。HDCA还用于结合联合收割机EEG、瞳孔和停留时间信号来分类重定向事件。主要结果。在EEG和瞳孔中，停留时间对重定向信号的贡献最大。然而，当停留时间与其他凝视事件正交时，重新定向信号的分布在两种模态中是不同的，其中EEG重新定向信号领先于瞳孔重新定向信号。我们还发现，混合分类器，集成EEG，瞳孔和停留时间功能检测到的重定向信号在固定（AUC = 0.79）和自由（AUC = 0.77）的条件。意义我们表明，神经和眼重定向信号分布不同的凝视事件时，一个主题是沉浸在VR，但仍然可以被捕获和集成分类目标与干扰对象的人类主体的方向。
Objective. Reorienting is central to how humans direct attention to different stimuli in their environment. Previous studies typically employ well-controlled paradigms with limited eye and head movements to study the neural and physiological processes underlying attention reorienting. Here, we aim to better understand the relationship between gaze and attention reorienting using a naturalistic virtual reality (VR)-based target detection paradigm. Approach. Subjects were navigated through a city and instructed to count the number of targets that appeared on the street. Subjects performed the task in a fixed condition with no head movement and in a free condition where head movements were allowed. Electroencephalography (EEG), gaze and pupil data were collected. To investigate how neural and physiological reorienting signals are distributed across different gaze events, we used hierarchical discriminant component analysis (HDCA) to identify EEG and pupil-based discriminating components. Mixed-effects general linear models (GLM) were used to determine the correlation between these discriminating components and the different gaze events time. HDCA was also used to combine EEG, pupil and dwell time signals to classify reorienting events. Main results. In both EEG and pupil, dwell time contributes most significantly to the reorienting signals. However, when dwell times were orthogonalized against other gaze events, the distributions of the reorienting signals were different across the two modalities, with EEG reorienting signals leading that of the pupil reorienting signals. We also found that the hybrid classifier that integrates EEG, pupil and dwell time features detects the reorienting signals in both the fixed (AUC = 0.79) and the free (AUC = 0.77) condition. Significance. We show that the neural and ocular reorienting signals are distributed differently across gaze events when a subject is immersed in VR, but nevertheless can be captured and integrated to classify target vs. distractor objects to which the human subject orients.