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快速序列视觉加工流中视觉对象的表象动力学。

The representational dynamics of visual objects in rapid serial visual processing streams.

机构信息

School of Psychology, University of Sydney, NSW, 2006, Australia; ARC Centre of Excellence in Cognition and Its Disorders, NSW, 2109, Australia; Department of Cognitive Science, Macquarie University, NSW, 2109, Australia.

School of Psychology, University of Sydney, NSW, 2006, Australia; ARC Centre of Excellence in Cognition and Its Disorders, NSW, 2109, Australia.

出版信息

Neuroimage. 2019 Mar;188:668-679. doi: 10.1016/j.neuroimage.2018.12.046. Epub 2018 Dec 26.

Abstract

In our daily lives, we are bombarded with a stream of rapidly changing visual input. Humans have the remarkable capacity to detect and identify objects in fast-changing scenes. Yet, when studying brain representations, stimuli are generally presented in isolation. Here, we studied the dynamics of human vision using a combination of fast stimulus presentation rates, electroencephalography and multivariate decoding analyses. Using a presentation rate of 5 images per second, we obtained the representational structure of a large number of stimuli, and showed the emerging abstract categorical organisation of this structure. Furthermore, we could separate the temporal dynamics of perceptual processing from higher-level target selection effects. In a second experiment, we used the same paradigm at 20Hz to show that shorter image presentation limits the categorical abstraction of object representations. Our results show that applying multivariate pattern analysis to every image in rapid serial visual processing streams has unprecedented potential for studying the temporal dynamics of the structure of representations in the human visual system.

摘要

在日常生活中,我们会受到大量快速变化的视觉输入的冲击。人类具有在快速变化的场景中检测和识别物体的非凡能力。然而,在研究大脑表示时,通常会单独呈现刺激。在这里,我们使用快速呈现率、脑电图和多元解码分析的组合来研究人类视觉的动态。使用每秒 5 张图像的呈现率,我们获得了大量刺激的表示结构,并显示了该结构中出现的抽象分类组织。此外,我们可以将感知处理的时间动态与更高层次的目标选择效应区分开来。在第二个实验中,我们在 20Hz 时使用相同的范式表明,较短的图像呈现限制了物体表示的分类抽象。我们的结果表明,将多元模式分析应用于快速连续视觉处理流中的每张图像,对于研究人类视觉系统中表示结构的时间动态具有前所未有的潜力。

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