Deep convolutional neural networks (DCNNs) are able to partially predict brain activity during object categorization tasks, but factors contributing to this predictive power are not fully understood. Our study aimed to investigate the factors contributing to the predictive power of DCNNs in object categorization tasks. We compared the activity of four DCNN architectures with EEG recordings obtained from 62 human participants during an object categorization task. Previous physiological studies on object categorization have highlighted the importance of figure-ground segregation—the ability to distinguish objects from their backgrounds. Therefore, we investigated whether figure-ground segregation could explain the predictive power of DCNNs. Using a stimulus set consisting of identical target objects embedded in different backgrounds, we examined the influence of object background versus object category within both EEG and DCNN activity. Crucially, the recombination of naturalistic objects and experimentally controlled backgrounds creates a challenging and naturalistic task, while retaining experimental control. Our results showed that early EEG activity (< 100 msec) and early DCNN layers represent object background rather than object category. We also found that the ability of DCNNs to predict EEG activity is primarily influenced by how both systems process object backgrounds, rather than object categories. We demonstrated the role of figure-ground segregation as a potential prerequisite for recognition of object features, by contrasting the activations of trained and untrained (i.e., random weights) DCNNs. These findings suggest that both human visual cortex and DCNNs prioritize the segregation of object backgrounds and target objects to perform object categorization. Altogether, our study provides new insights into the mechanisms underlying object categorization as we demonstrated that both human visual cortex and DCNNs care deeply about object background.

深度卷积神经网络 (DCNN) 能够在对象分类任务期间部分预测大脑活动，但影响这种预测能力的因素尚不完全清楚。我们的研究旨在调查影响 DCNN 在对象分类任务中的预测能力的因素。我们将四种 DCNN 架构的活动与在对象分类任务期间从 62 名人类参与者获得的 EEG 记录进行了比较。先前关于物体分类的生理学研究强调了图形-背景分离的重要性——将物体与其背景区分开来的能力。因此，我们研究了图形-背景分离是否可以解释 DCNN 的预测能力。使用由嵌入不同背景中的相同目标对象组成的刺激集，我们检查了 EEG 和 DCNN 活动中对象背景与对象类别的影响。至关重要的是，自然物体和实验控制背景的重新组合创造了一项具有挑战性和自然主义的任务，同时保留实验控制。我们的结果表明，早期 EEG 活动（< 100 毫秒）和早期 DCNN 层代表对象背景而不是对象类别。我们还发现，DCNN 预测脑电图活动的能力主要受到两个系统处理对象背景的方式的影响，而不是对象类别的影响。通过对比经过训练和未经训练（即随机权重）DCNN 的激活，我们证明了图形-背景分离作为识别对象特征的潜在先决条件的作用。这些发现表明，人类视觉皮层和 DCNN 都会优先考虑对象背景和目标对象的分离来执行对象分类。总而言之，我们的研究为对象分类的机制提供了新的见解，因为我们证明了人类视觉皮层和 DCNN 都非常关心对象背景。