Beyond Bouma's window: How to explain global aspects of crowding?
Beyond Bouma's window: How to explain global aspects of crowding?
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DOI：
10.1371/journal.pcbi.1006580
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发表时间：
2019-05-01
影响因子：
4.3
通讯作者：
Herzog, Michael H.
中科院分区：
文献类型：
--
作者：
Doerig, Adrien;Bornet, Alban;Herzog, Michael H.
在拥挤的情况下，对物体的感知在附近元素的存在下恶化。虽然拥挤是一个普遍存在的现象，因为元素很少单独出现，到目前为止，还没有达成共识，如何模拟它。以前的实验表明，整个刺激的全局配置必须考虑在内。这些发现排除了简单的汇集或替代模型，有利于全球空间方面敏感的模型。为了研究如何将全球方面纳入模型，我们测试了大量的模型与数据库的40刺激量身定制的全球方面的拥挤。我们的研究结果表明，结合分组一样的组件大大提高了模型的性能。作者摘要视觉拥挤突出了视觉领域中元素之间的相互作用。例如，如果一个物体在杂乱中出现，它就更难识别。拥挤是视觉最基本的方面之一，在物体识别、阅读和一般视觉感知中起着至关重要的作用，因此是理解视觉系统如何根据其视网膜输入编码信息的重要工具。因此，经典的拥挤模型只关注相邻视觉元素之间的局部相互作用。然而，大量的实验证据反对本地处理，这表明，全球配置的视觉元素强烈调节拥挤。在这里，我们测试了所有可用的拥挤模型，这些模型能够捕获整个视野中的全局处理。我们测试了12个模型，包括纹理平铺模型，深度卷积神经网络和LAMINART神经网络与大规模计算机模拟。我们发现，包含分组组件的模型最适合解释数据。我们的研究结果表明，为了理解一般的视觉，中级，上下文处理是不可避免的。
In crowding, perception of an object deteriorates in the presence of nearby elements. Although crowding is a ubiquitous phenomenon, since elements are rarely seen in isolation, to date there exists no consensus on how to model it. Previous experiments showed that the global configuration of the entire stimulus must be taken into account. These findings rule out simple pooling or substitution models and favor models sensitive to global spatial aspects. In order to investigate how to incorporate global aspects into models, we tested a large number of models with a database of forty stimuli tailored for the global aspects of crowding. Our results show that incorporating grouping like components strongly improves model performance.Author summary Visual crowding highlights interactions between elements in the visual field. For example, an object is more difficult to recognize if it is presented in clutter. Crowding is one of the most fundamental aspects of vision, playing crucial roles in object recognition, reading and visual perception in general, and is therefore an essential tool to understand how the visual system encodes information based on its retinal input. Hence, classic models of crowding have focused only on local interactions between neighboring visual elements. However, abundant experimental evidence argues against local processing, suggesting that the global configuration of visual elements strongly modulates crowding. Here, we tested all available models of crowding that are able to capture global processing across the entire visual field. We tested 12 models including the Texture Tiling Model, a Deep Convolutional Neural Network and the LAMINART neural network with large scale computer simulations. We found that models incorporating a grouping component are best suited to explain the data. Our results suggest that in order to understand vision in general, mid-level, contextual processing is inevitable.