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简单的线条图足以用于自然场景分类的功能磁共振成像解码。

Simple line drawings suffice for functional MRI decoding of natural scene categories.

机构信息

Department of Psychology, The Ohio State University, Columbus, OH 43210, USA.

出版信息

Proc Natl Acad Sci U S A. 2011 Jun 7;108(23):9661-6. doi: 10.1073/pnas.1015666108. Epub 2011 May 18.

Abstract

Humans are remarkably efficient at categorizing natural scenes. In fact, scene categories can be decoded from functional MRI (fMRI) data throughout the ventral visual cortex, including the primary visual cortex, the parahippocampal place area (PPA), and the retrosplenial cortex (RSC). Here we ask whether, and where, we can still decode scene category if we reduce the scenes to mere lines. We collected fMRI data while participants viewed photographs and line drawings of beaches, city streets, forests, highways, mountains, and offices. Despite the marked difference in scene statistics, we were able to decode scene category from fMRI data for line drawings just as well as from activity for color photographs, in primary visual cortex through PPA and RSC. Even more remarkably, in PPA and RSC, error patterns for decoding from line drawings were very similar to those from color photographs. These data suggest that, in these regions, the information used to distinguish scene category is similar for line drawings and photographs. To determine the relative contributions of local and global structure to the human ability to categorize scenes, we selectively removed long or short contours from the line drawings. In a category-matching task, participants performed significantly worse when long contours were removed than when short contours were removed. We conclude that global scene structure, which is preserved in line drawings, plays an integral part in representing scene categories.

摘要

人类在对自然场景进行分类时非常高效。事实上,场景类别可以从腹侧视觉皮层的功能磁共振成像(fMRI)数据中解码出来,包括初级视觉皮层、海马旁回位置区域(PPA)和后扣带回皮层(RSC)。在这里,我们想知道如果我们将场景简化为线条,是否还能进行场景类别解码,以及在何处可以进行场景类别解码。我们收集了参与者观看海滩、城市街道、森林、高速公路、山脉和办公室的照片和线条画时的 fMRI 数据。尽管场景统计数据存在明显差异,但我们仍能够从线条画的 fMRI 数据中解码出场景类别,就像从彩色照片的活动中解码一样,在初级视觉皮层、PPA 和 RSC 中都能做到。更令人惊讶的是,在 PPA 和 RSC 中,从线条画解码的错误模式与从彩色照片解码的模式非常相似。这些数据表明,在这些区域中,用于区分场景类别的信息对于线条画和照片来说是相似的。为了确定局部和全局结构对人类场景分类能力的相对贡献,我们从线条画中选择性地删除了长或短的轮廓。在类别匹配任务中,当长轮廓被删除时,参与者的表现明显比短轮廓被删除时差。我们得出结论,全局场景结构在保留线条画中的场景类别方面起着不可或缺的作用。

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