Department of Cognitive Science, Macquarie University, Sydney, Australia; ARC Centre of Excellence in Cognition and its Disorders and Perception in Action Research Centre, Macquarie University, Australia.
Department of Cognitive Science, Macquarie University, Sydney, Australia; ARC Centre of Excellence in Cognition and its Disorders and Perception in Action Research Centre, Macquarie University, Australia.
Neuropsychologia. 2017 Oct;105:165-176. doi: 10.1016/j.neuropsychologia.2017.02.013. Epub 2017 Feb 17.
Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time-series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low-level visual features dominate decodable object representations early in the time-course, with more abstract representations related to object category emerging later. A key finding is that the time-course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low-level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time-course of object processing.
视觉对象识别是一个复杂、动态的过程。多元模式分析方法，如解码，已经开始揭示大脑如何处理复杂的视觉信息。最近，EEG 和 MEG 的时间解码方法为评估对象识别的时间动态提供了可能。在这里，我们回顾了 M/EEG 时序列解码方法对理解人类大脑中视觉对象识别的贡献。与视觉处理层次结构的当前理解一致，低水平视觉特征在时间过程的早期主导可解码对象表示，而与对象类别相关的更抽象的表示则在后期出现。一个关键的发现是，对象处理的时间过程是高度动态和快速演变的，可解码信息的时间概括有限。几项研究已经研究了对象类别结构的出现，我们考虑到类别解码在多大程度上可以通过对低水平视觉特征的敏感性来解释。最后，我们评估了最近试图将人类行为与对象处理的神经时间过程联系起来的工作。
