Berman Daniel, Golomb Julie D, Walther Dirk B
Department of Psychology, The Ohio State University, Columbus, Ohio, United States of America.
Department of Psychology, University of Toronto, Toronto, Ontario, Canada.
PLoS One. 2017 Dec 22;12(12):e0189828. doi: 10.1371/journal.pone.0189828. eCollection 2017.
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.
在复杂的现实世界场景中，图像内容由大量相互交织的视觉特征所传达。视觉系统会解析这些特征，以便提取有关图像内容的信息。在此，我们研究一个不可或缺的组成部分的作用：图像中空间频率的内容。具体而言，我们测量了低空间频率与高空间频率所承载的图像内容量，以用于人类视觉皮层场景选择区域中现实世界场景的表征。为此，我们试图从参与者观看包含完整空间频率谱、仅低空间频率或仅高空间频率的场景图像时的大脑活动模式中解码场景类别，所有这些都经过了对比度和亮度的仔细控制。与众多行为研究和计算模型的结果相反，这些研究强调了低空间频率如何优先编码图像内容，而从包括海马旁回位置区（PPA）在内的场景选择脑区对场景类别进行解码时，高空间频率图像的准确性显著高于低空间频率图像。事实上，在场景选择区域PPA、RSC、OPA和物体选择区域LOC中，高空间频率图像的解码准确性与包含完整空间频率谱的图像一样高。我们还发现了PPA前后亚区之间一个有趣的分离：在PPA后部，高空间频率和低空间频率场景的类别均可解码，但在PPA前部，仅高空间频率场景的类别可解码；并且空间频率仅在PPA后部可明确解码，而在PPA前部则不可。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条图所引发的场景类别神经表征与全光谱彩色照片的等效。总体而言，这些发现证明了高空间频率对于传达复杂现实世界场景内容的重要性。
