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arXiv 1812.02478q-bio.NC
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双眼竞争中的知觉反转:来自OKN的改进检测

Perceptual reversals in binocular rivalry: improved detection from OKN

Stepan Aleshin, Gergo Ziman, Ilona Kovacs, Jochen Braun

摘要:

当双眼竞争由对手的运动显示引起时,感知反转通常与改变的眼行为相关(Frassle等人,2014; Fujiwara等人,2017年)的报告。具体而言,在视动性眼震(OKN)中,平滑追踪相位的方向通常对应于在任何给定时间主导感知外观的运动方向。在这里,我们报告了一个改进的分析,不断估计感知运动的“累积平滑的追求”。本质上,平滑追踪段被识别,在必要时被内插,并且概率性地连接到“累积平滑追踪”的连续记录中(即,不考虑眨眼、扫视、信号损失和伪影的眼睛位置的概率)。该分析在健康、发育和患者人群中是完全自动化和稳健的。为了验证可靠性,我们比较了意志报告的知觉逆转的竞争显示,和物理逆转的非竞争性控制显示。“累积平滑追踪”检测物理反转并比现有方法更准确地估计眼睛速度(Frassle等人,2014年)。它似乎也区分主导和过渡知觉状态,检测变化的精度为$\pm100\,\mathit{ms}$。我们的结论是“累积平滑追求”显着提高了监测的双眼竞争通过记录OKN。

英文摘要:

When binocular rivalry is induced by opponent motion displays, perceptual reversals are often associated with changed oculomotor behaviour (Frassle et al., 2014; Fujiwara et al., 2017). Specifically, the direction of smooth pursuit phases in optokinetic nystagmus (OKN) typically corresponds to the direction of motion that dominates perceptual appearance at any given time. Here we report an improved analysis that continuously estimates perceived motion in terms of `cumulative smooth pursuit'. In essence, smooth pursuit segments are identified, interpolated where necessary, and joined probabilistically into a continuous record of `cumulative smooth pursuit' (i.e., a probability of eye position disregarding blinks, saccades, signal losses, and artefacts). The analysis is fully automated and robust in healthy, developmental, and patient populations. To validate reliability, we compare volitional reports of perceptual reversals in rivalry displays, and of physical reversals in non-rivalrous control displays. `Cumulative smooth pursuit' detects physical reversals and estimates eye velocity more accurately than existing methods do (Frassle et al., 2014). It also appears to distinguish dominant and transitional perceptual states, detecting changes with a precision of $\pm100\,\mathit{ms}$. We conclude that `cumulative smooth pursuit' significantly improves the monitoring of binocular rivalry by means of recording OKN.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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