Combined fMRI- and eye movement-based decoding of bistable plaid motion perception
Combined fMRI- and eye movement-based decoding of bistable plaid motion perception
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DOI：
10.1016/j.neuroimage.2017.12.094
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发表时间：
2018-05-01
期刊：
影响因子：
5.7
通讯作者：
Sterzer, Philipp
中科院分区：
文献类型：
--
作者：
Wilbertz, Gregor;Ketkar, Madhura;Sterzer, Philipp
尽管有持续的感官刺激，感知仍然会自发地交替，这种现象在探索有意识感知的神经基础方面特别有用。这种双稳态的研究需要访问观察者的感知动态，这通常是通过主动报告来实现的。然而，这份报告在有意识知觉的研究中构成了一个混杂因素，在某些实验操作的背景下也可能有偏见。规避这些问题的一种方法是使用来自眼睛或大脑的信号而不是观察者的报告来跟踪感知变化。在这里，我们的目标是通过结合眼睛和大脑信号来优化这种感知变化的解码。在20名参与者中进行了眼动追踪和功能性磁共振成像（fMRI），同时他们观看了一个连续的视觉格子运动刺激并报告了感知变化。将用于fMRI的多体素模式分析（MVPA）与支持向量机中的眼动跟踪相结合，以从fMRI和眼动信号中解码参与者的感知时间过程。虽然这两种措施单独已经产生了很高的解码精度（平均86%和88%正确，分别）分类的基础上，这两个措施一起进一步提高了准确性（91%正确）。这些研究结果表明，利用功能磁共振成像和眼动数据可能会铺平道路，优化无报告范式，通过提高可解码性的运动知觉，从而更好地了解神经相关的意识。
The phenomenon of bistable perception, in which perception alternates spontaneously despite constant sensory stimulation, has been particularly useful in probing the neural bases of conscious perception. The study of such bistability requires access to the observer's perceptual dynamics, which is usually achieved via active report. This report, however, constitutes a confounding factor in the study of conscious perception and can also be biased in the context of certain experimental manipulations. One approach to circumvent these problems is to track perceptual alternations using signals from the eyes or the brain instead of observers' reports. Here we aimed to optimize such decoding of perceptual alternations by combining eye and brain signals. Eye-tracking and functional magnetic resonance imaging (fMRI) was performed in twenty participants while they viewed a bistable visual plaid motion stimulus and reported perceptual alternations. Multivoxel pattern analysis (MVPA) for fMRI was combined with eye-tracking in a Support vector machine to decode participants' perceptual time courses from fMRI and eye-movement signals. While both measures individually already yielded high decoding accuracies (on average 86% and 88% correct, respectively) classification based on the two measures together further improved the accuracy (91% correct). These findings show that leveraging on both fMRI and eye movement data may pave the way for optimized no-report paradigms through improved decodability of bistable motion perception and hence for a better understanding of the neural correlates of consciousness.