人工神经网络中的被动注意预测人类视觉选择性
Passive Attention in Artificial Neural Networks Predicts Human Visual Selectivity
摘要：
在过去的十年中，机器学习可解释性技术的发展提供了新的工具来观察人工神经网络（ANN）中最具分类和定位信息的图像区域。同样的区域对人类观察者来说也是同样的信息吗？使用来自79个新实验和7，810名参与者的数据，我们发现被动注意技术与来自6个不同行为任务的人类视觉选择性估计有显着重叠，这些任务包括视觉辨别，空间定位，可识别性，自由观看，线索对象搜索和显着性搜索固定。我们发现，从相对简单的人工神经网络架构探测使用引导反向传播方法的输入可视化是最好的预测共享组件的联合变化的人类措施。我们验证这些相关的结果与因果操纵识别实验。我们发现，在一个快速识别实验中，人工神经网络注意力图掩盖的图像比控制掩模更容易被人类分类。同样，我们发现，在相同的人工神经网络模型的识别性能同样受到使用人类视觉选择性地图掩蔽输入图像。这项工作提供了一种新的方法来评估领先的人工神经网络作为人类视觉模型的生物和心理有效性：通过研究它们对图像中包含的信息的视觉选择性的相似性和差异性。
英文摘要：
Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification and localization in artificial neural networks (ANNs). Are the same regions similarly informative to human observers? Using data from 79 new experiments and 7,810 participants, we show that passive attention techniques reveal a significant overlap with human visual selectivity estimates derived from 6 distinct behavioral tasks including visual discrimination, spatial localization, recognizability, free-viewing, cued-object search, and saliency search fixations. We find that input visualizations derived from relatively simple ANN architectures probed using guided backpropagation methods are the best predictors of a shared component in the joint variability of the human measures. We validate these correlational results with causal manipulations using recognition experiments. We show that images masked with ANN attention maps were easier for humans to classify than control masks in a speeded recognition experiment. Similarly, we find that recognition performance in the same ANN models was likewise influenced by masking input images using human visual selectivity maps. This work contributes a new approach to evaluating the biological and psychological validity of leading ANNs as models of human vision: by examining their similarities and differences in terms of their visual selectivity to the information contained in images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
