School of Psychology, University of Sydney, Sydney, NSW, Australia; Perception in Action Research Centre, Macquarie University, Sydney, NSW, Australia.
School of Psychology, University of Sydney, Sydney, NSW, Australia; Perception in Action Research Centre, Macquarie University, Sydney, NSW, Australia.
Neuroimage. 2019 Nov 15;202:116083. doi: 10.1016/j.neuroimage.2019.116083. Epub 2019 Aug 7.
How are visual inputs transformed into conceptual representations by the human visual system? The contents of human perception, such as objects presented on a visual display, can reliably be decoded from voxel activation patterns in fMRI, and in evoked sensor activations in MEG and EEG. A prevailing question is the extent to which brain activation associated with object categories is due to statistical regularities of visual features within object categories. Here, we assessed the contribution of mid-level features to conceptual category decoding using EEG and a novel fast periodic decoding paradigm. Our study used a stimulus set consisting of intact objects from the animate (e.g., fish) and inanimate categories (e.g., chair) and scrambled versions of the same objects that were unrecognizable and preserved their visual features (Long et al., 2018). By presenting the images at different periodic rates, we biased processing to different levels of the visual hierarchy. We found that scrambled objects and their intact counterparts elicited similar patterns of activation, which could be used to decode the conceptual category (animate or inanimate), even for the unrecognizable scrambled objects. Animacy decoding for the scrambled objects, however, was only possible at the slowest periodic presentation rate. Animacy decoding for intact objects was faster, more robust, and could be achieved at faster presentation rates. Our results confirm that the mid-level visual features preserved in the scrambled objects contribute to animacy decoding, but also demonstrate that the dynamics vary markedly for intact versus scrambled objects. Our findings suggest a complex interplay between visual feature coding and categorical representations that is mediated by the visual system's capacity to use image features to resolve a recognisable object.
人类视觉系统如何将视觉输入转化为概念表示？人类感知的内容，如视觉显示器上呈现的物体，可以从 fMRI 中的体素激活模式以及 MEG 和 EEG 中的诱发传感器激活中可靠地解码。一个流行的问题是，与物体类别相关的大脑激活在多大程度上归因于物体类别中视觉特征的统计规律。在这里，我们使用 EEG 和一种新的快速周期解码范式来评估中级特征对概念类别解码的贡献。我们的研究使用了一个刺激集，其中包括来自有生命的（例如，鱼）和无生命的（例如，椅子）类别的完整物体，以及相同物体的打乱版本，这些物体无法识别，但保留了它们的视觉特征（Long 等人，2018）。通过以不同的周期性速率呈现图像，我们将处理偏向于视觉层次结构的不同级别。我们发现，打乱的物体及其完整的对应物会引起相似的激活模式，这些模式可用于解码概念类别（有生命的或无生命的），即使对于无法识别的打乱物体也是如此。然而，对于打乱的物体，只有在最慢的周期性呈现速率下才能进行有生命的解码。完整物体的有生命解码更快、更稳健，并且可以在更快的呈现速率下实现。我们的结果证实，在打乱的物体中保留的中级视觉特征有助于有生命的解码，但也表明完整物体和打乱物体之间的动态变化明显不同。我们的研究结果表明，视觉特征编码和类别表示之间存在复杂的相互作用，这种相互作用是由视觉系统利用图像特征来识别可识别物体的能力介导的。
