Chen Chiu-Yueh, Leys Gaëlle, Bracci Stefania, Op de Beeck Hans
KU Leuven, Leuven Brain Institute, Brain & Cognition, Leuven, Belgium.
University of Trento, CiMeC Mind/Brain Institute, Rovereto, Italy.
Imaging Neurosci (Camb). 2023 Aug 10;1. doi: 10.1162/imag_a_00006. eCollection 2023.
The human visual system has a seemingly unique tendency to interpret zoomorphic objects as animals, not as objects. This animal appearance bias is very strong in the ventral visual pathway as measured through functional magnetic resonance imaging (fMRI), but it is absent in feedforward deep convolutional neural networks. Here we investigate how this bias emerges over time by probing its representational dynamics through multivariate electroencephalography (EEG). The initially activated representations to lookalike zoomorphic objects are very similar to the representations activated by animal pictures and very different from the neural responses to regular objects. Neural responses that reflect the true identity of the zoomorphic objects as inanimate objects are weaker and appear later, as do effects of task context. The strong early emergence of an animal appearance bias strongly supports a feedforward explanation, indicating that lack of recurrence in deep neural networks is not an explanation for their failure to show this bias.
人类视觉系统有一种看似独特的倾向，即将兽形物体解读为动物，而非物体。通过功能磁共振成像（fMRI）测量发现，这种动物外观偏差在腹侧视觉通路中非常强烈，但在前馈深度卷积神经网络中却不存在。在这里，我们通过多变量脑电图（EEG）探究这种偏差的表征动态，以研究它是如何随着时间出现的。最初对相似兽形物体激活的表征与动物图片激活的表征非常相似，与对普通物体的神经反应截然不同。反映兽形物体作为无生命物体真实身份的神经反应较弱且出现较晚，任务背景的影响也是如此。动物外观偏差的早期强烈出现有力地支持了一种前馈解释，表明深度神经网络中缺乏循环并非其未能表现出这种偏差的原因。
