Emory University, Department of Biology, O. Wayne Rollins Research Center, Atlanta, Georgia.
J Vis. 2022 Jun 1;22(7):3. doi: 10.1167/jov.22.7.3.
Visual search is a complex behavior influenced by many factors. To control for these factors, many studies use highly simplified stimuli. However, the statistics of these stimuli are very different from the statistics of the natural images that the human visual system is optimized by evolution and experience to perceive. Could this difference change search behavior? If so, simplified stimuli may contribute to effects typically attributed to cognitive processes, such as selective attention. Here we use deep neural networks to test how optimizing models for the statistics of one distribution of images constrains performance on a task using images from a different distribution. We train four deep neural network architectures on one of three source datasets-natural images, faces, and x-ray images-and then adapt them to a visual search task using simplified stimuli. This adaptation produces models that exhibit performance limitations similar to humans, whereas models trained on the search task alone exhibit no such limitations. However, we also find that deep neural networks trained to classify natural images exhibit similar limitations when adapted to a search task that uses a different set of natural images. Therefore, the distribution of data alone cannot explain this effect. We discuss how future work might integrate an optimization-based approach into existing models of visual search behavior.
视觉搜索是一种受多种因素影响的复杂行为。为了控制这些因素，许多研究使用高度简化的刺激。然而，这些刺激的统计数据与人类视觉系统通过进化和经验优化来感知的自然图像的统计数据大不相同。这种差异会改变搜索行为吗？如果是这样，简化的刺激可能会促进通常归因于认知过程的效果，例如选择性注意。在这里，我们使用深度神经网络来测试优化模型对一种图像分布的统计数据如何限制使用不同分布的图像进行任务的性能。我们在三个源数据集（自然图像、人脸和 X 射线图像）中的一个上训练了四个深度神经网络架构，然后使用简化的刺激将它们适应于视觉搜索任务。这种适应会产生表现出类似于人类的性能限制的模型，而仅在搜索任务上训练的模型则没有表现出这种限制。然而，我们还发现，当适应使用不同的自然图像集的搜索任务时，经过训练用于分类自然图像的深度神经网络也会表现出类似的限制。因此，数据的分布本身并不能解释这种效果。我们讨论了未来的工作如何将基于优化的方法整合到现有的视觉搜索行为模型中。
