Dataset Information

Neural mechanisms of rapid natural scene categorization in human visual cortex.

ABSTRACT: The visual system has an extraordinary capability to extract categorical information from complex natural scenes. For example, subjects are able to rapidly detect the presence of object categories such as animals or vehicles in new scenes that are presented very briefly. This is even true when subjects do not pay attention to the scenes and simultaneously perform an unrelated attentionally demanding task, a stark contrast to the capacity limitations predicted by most theories of visual attention. Here we show a neural basis for rapid natural scene categorization in the visual cortex, using functional magnetic resonance imaging and an object categorization task in which subjects detected the presence of people or cars in briefly presented natural scenes. The multi-voxel pattern of neural activity in the object-selective cortex evoked by the natural scenes contained information about the presence of the target category, even when the scenes were task-irrelevant and presented outside the focus of spatial attention. These findings indicate that the rapid detection of categorical information in natural scenes is mediated by a category-specific biasing mechanism in object-selective cortex that operates in parallel across the visual field, and biases information processing in favour of objects belonging to the target object category.

SUBMITTER: Peelen MV

PROVIDER: S-EPMC2752739 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Publications

Neural mechanisms of rapid natural scene categorization in human visual cortex.

Peelen Marius V MV Fei-Fei Li L Kastner Sabine S

Nature 20090607 7251

The visual system has an extraordinary capability to extract categorical information from complex natural scenes. For example, subjects are able to rapidly detect the presence of object categories such as animals or vehicles in new scenes that are presented very briefly. This is even true when subjects do not pay attention to the scenes and simultaneously perform an unrelated attentionally demanding task, a stark contrast to the capacity limitations predicted by most theories of visual attention ...[more]

PMID: 19506558

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Project description: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.

Dataset Information

Neural mechanisms of rapid natural scene categorization in human visual cortex.

Publications

Neural mechanisms of rapid natural scene categorization in human visual cortex.

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