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Scene content is predominantly conveyed by high spatial frequencies in scene-selective visual cortex

机译:场景内容主要由场景选择性视觉皮层中的高空间频率传达

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摘要

In complex real-world scenes, image content is conveyed by a large collection of intertwined visual features. The visual system disentangles these features in order to extract information about image content. Here, we investigate the role of one integral component: the content of spatial frequencies in an image. Specifically, we measure the amount of image content carried by low versus high spatial frequencies for the representation of real-world scenes in scene-selective regions of human visual cortex. To this end, we attempted to decode scene categories from the brain activity patterns of participants viewing scene images that contained the full spatial frequency spectrum, only low spatial frequencies, or only high spatial frequencies, all carefully controlled for contrast and luminance. Contrary to the findings from numerous behavioral studies and computational models that have highlighted how low spatial frequencies preferentially encode image content, decoding of scene categories from the scene-selective brain regions, including the parahippocampal place area (PPA), was significantly more accurate for high than low spatial frequency images. In fact, decoding accuracy was just as high for high spatial frequency images as for images containing the full spatial frequency spectrum in scene-selective areas PPA, RSC, OPA and object selective area LOC. We also found an interesting dissociation between the posterior and anterior subdivisions of PPA: categories were decodable from both high and low spatial frequency scenes in posterior PPA but only from high spatial frequency scenes in anterior PPA; and spatial frequency was explicitly decodable from posterior but not anterior PPA. Our results are consistent with recent findings that line drawings, which consist almost entirely of high spatial frequencies, elicit a neural representation of scene categories that is equivalent to that of full-spectrum color photographs. Collectively, these findings demonstrate the importance of high spatial frequencies for conveying the content of complex real-world scenes.
机译:在复杂的现实世界场景中,图像内容是通过大量相互交织的视觉功能传达的。视觉系统解开这些功能,以提取有关图像内容的信息。在这里,我们研究了一个不可或缺的组成部分的作用:图像中空间频率的内容。具体来说,我们测量低空间频率和高空间频率所载图像内容的数量,以表示人类视觉皮层的场景选择区域中的真实场景。为此,我们尝试从参与者观看场景图像的参与者的大脑活动模式中解码场景类别,这些场景图像包含完整的空间频谱,仅低空间频率或仅高空间频率,所有这些都严格控制了对比度和亮度。与众多行为研究和计算模型的发现相反,这些研究和计算模型强调了低空间频率如何优先编码图像内容,对来自场景选择性大脑区域(包括海马旁地方区域(PPA))的场景类别的解码对于高视场的准确度要高得多比低空间频率的图像实际上,对于高空间频率图像,解码精度与在场景选择区域PPA,RSC,OPA和对象选择区域LOC中包含完整空间频谱的图像一样高。我们还发现了PPA的后细分和前细分之间的有趣分离:类别可从后PPA的高和低空间频率场景中解码,而只能从前PPA的高空间频率场景中解码;并且空间频率可以明确地从后PPA而非前PPA解码。我们的结果与最近的发现一致,即几乎完全由高空间频率组成的线图会引起场景类别的神经表示,其等效于全光谱彩色照片。这些发现共同证明了高空间频率对于传达复杂的真实世界场景的内容的重要性。

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