MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
Department of Psychology, University of Cambridge, Cambridge, UK.
Commun Biol. 2023 Nov 27;6(1):1207. doi: 10.1038/s42003-023-05565-9.
Visual object recognition has been traditionally conceptualised as a predominantly feedforward process through the ventral visual pathway. While feedforward artificial neural networks (ANNs) can achieve human-level classification on some image-labelling tasks, it's unclear whether computational models of vision alone can accurately capture the evolving spatiotemporal neural dynamics. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate to visual and semantic object properties. Critically, we show that recurrent processing between the anterior and posterior ventral temporal cortex relates to higher-level visual properties prior to semantic object properties, in addition to semantic-related feedback from the frontal lobe to the ventral temporal lobe between 250 and 500 ms after stimulus onset. These results demonstrate the distinct contributions made by semantic object properties in explaining neural activity and connectivity, highlighting it as a core part of object recognition not fully accounted for by current biologically inspired neural networks.
传统上，视觉对象识别被概念化为通过腹侧视觉通路的主要前馈过程。虽然前馈人工神经网络 (ANN) 可以在某些图像标记任务上达到人类水平的分类，但尚不清楚仅通过计算模型是否可以准确地捕捉到不断发展的时空神经动力学。在这里，我们使用 fMRI 和 MEG 数据的代表性相似性和连接性分析来探测这些动态，这些数据是在识别熟悉、明确的物体时记录的。使用人工神经网络和语义特征模型来模拟我们刺激的视觉和语义属性，我们发现神经结构和连接动力学的独特方面与视觉和语义对象属性有关。至关重要的是，我们表明，在前侧和后侧腹侧颞叶皮层之间进行的递归处理与语义对象属性之前的更高层次的视觉属性有关，此外，在刺激开始后 250 到 500 毫秒之间，额叶到腹侧颞叶的反馈与语义相关。这些结果表明语义对象属性在解释神经活动和连接方面做出了独特的贡献，突出了它作为对象识别的核心部分，这部分不能完全由当前基于生物学的神经网络来解释。
