Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.
Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.
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DOI：
10.1371/journal.pcbi.1002726
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发表时间：
2012
影响因子：
4.3
通讯作者：
Scholte HS
中科院分区：
文献类型：
--
作者：
Groen II;Ghebreab S;Lamme VA;Scholte HS
视觉世界是复杂的、不断变化的。然而，我们的大脑在几百毫秒内将投射到视网膜上的光的模式转换为连贯的感知。可能，低水平的神经反应已经携带了大量信息，以促进视觉输入的快速表征。在这里，我们对计算机生成的自然图像的低水平对比度反应进行了计算估计，并测试了这些反应的空间集合是否可以在神经和行为水平上预测图像的相似性。使用脑电，我们表明，从汇集的反应得出的统计数据解释了单个受试者的单幅图像诱发电位(ERPs)之间的大量差异。对多电极事件相关电位的差异性分析表明，集合反应统计量的图像差异较大，预示着诱发活动的不同模式较多，而统计差异较小的图像，则产生高度相似的诱发活动模式。在另一项单独的行为实验中，统计学上差异较大的图像被判断为不同的类别，而差异较小的图像被混淆。这些发现表明，从低水平对比度反应中获得的统计数据可以在早期的视觉加工中提取出来，并与快速判断视觉相似性有关。我们将我们的结果与另外两个众所周知的对比度统计进行了比较：傅立叶功率谱和对比度分布的高阶性质(偏度和峰度)。有趣的是，尽管这些统计数据可以进行准确的图像分类，但它们并不能预测事件相关电位反应模式或行为分类混乱。这些融合的计算、神经和行为结果表明，在快速、低水平的分类任务中，汇集的对比反应的统计数据包含与感知的视觉相似性相对应的信息。人类擅长快速、准确地处理视觉场景。然而，目前还不清楚哪些计算允许视觉系统以快速有效的方式将照射到视网膜上的光线转化为视觉输入的连贯表示。在这里，我们使用简单的、计算机生成的具有相似低级结构的图像类别作为自然场景来测试早期整合低级信息的模型是否可以预测感知到的类别相似性。具体地说，我们表明，覆盖整个视野的模型神经元(群体反应)对视觉输入(对比)的低水平属性的汇总(空间汇集)反应可能已经提供了关于早期视觉诱发活动以及这些类别的行为混淆的差异的信息。这些结果表明，低水平的群体反应可以携带相关信息来估计受控图像的相似性，并提出了视觉系统可能利用这些反应来快速处理真实自然场景的令人兴奋的假设。我们认为，允许提取这种信息的空间汇集可能是提取场景主旨以形成对视觉输入的快速印象的可信的第一步。
The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task. Humans excel in rapid and accurate processing of visual scenes. However, it is unclear which computations allow the visual system to convert light hitting the retina into a coherent representation of visual input in a rapid and efficient way. Here we used simple, computer-generated image categories with similar low-level structure as natural scenes to test whether a model of early integration of low-level information can predict perceived category similarity. Specifically, we show that summarized (spatially pooled) responses of model neurons covering the entire visual field (the population response) to low-level properties of visual input (contrasts) can already be informative about differences in early visual evoked activity as well as behavioral confusions of these categories. These results suggest that low-level population responses can carry relevant information to estimate similarity of controlled images, and put forward the exciting hypothesis that the visual system may exploit these responses to rapidly process real natural scenes. We propose that the spatial pooling that allows for the extraction of this information may be a plausible first step in extracting scene gist to form a rapid impression of the visual input.
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