Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
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发表时间：
2021-06
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通讯作者：
Shashi Kant Gupta;Mengmi Zhang;Chia-Chien Wu;J. Wolfe;Gabriel Kreiman
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作者：
Shashi Kant Gupta;Mengmi Zhang;Chia-Chien Wu;J. Wolfe;Gabriel Kreiman
视觉搜索是一项无处不在且经常具有挑战性的日常任务，例如在家中或在人群中寻找汽车钥匙。一些经典搜索任务的一个有趣特性是不对称性，在干扰项B中找到目标A可能比在A中找到B更容易。为了阐明视觉搜索中不对称性的机制，我们提出了一个计算模型，该模型以目标和搜索图像为输入，产生一系列眼动直到找到目标，该模型将偏心依赖的视觉识别与目标依赖的自上而下的线索相结合。我们在六个范式搜索任务中比较了人类行为的模型，这些任务显示了人类的不对称性。没有事先暴露的刺激或特定任务的培训，该模型提供了一个合理的搜索不对称的机制。我们假设，搜索不对称的极性来自自然环境的经验。我们通过在ImageNet的增强版本上训练模型来测试这一假设，其中自然图像的偏差被移除或逆转。搜索不对称的极性消失或改变，这取决于训练协议。这项研究强调了经典的感知特性如何在神经网络模型中出现，而不需要特定的任务训练，而是作为模型的发育饮食的统计特性的结果。所有源代码和数据都可以在https://github.com/kreimanlab/VisualSearchAsymmetry上公开获得。
Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys at home or a friend in a crowd. An intriguing property of some classical search tasks is an asymmetry such that finding a target A among distractors B can be easier than finding B among A. To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and a search image as inputs and produces a sequence of eye movements until the target is found. The model integrates eccentricity-dependent visual recognition with target-dependent top-down cues. We compared the model against human behavior in six paradigmatic search tasks that show asymmetry in humans. Without prior exposure to the stimuli or task-specific training, the model provides a plausible mechanism for search asymmetry. We hypothesized that the polarity of search asymmetry arises from experience with the natural environment. We tested this hypothesis by training the model on augmented versions of ImageNet where the biases of natural images were either removed or reversed. The polarity of search asymmetry disappeared or was altered depending on the training protocol. This study highlights how classical perceptual properties can emerge in neural network models, without the need for task-specific training, but rather as a consequence of the statistical properties of the developmental diet fed to the model. All source code and data are publicly available at https://github.com/kreimanlab/VisualSearchAsymmetry.