• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Simple line drawings suffice for functional MRI decoding of natural scene categories.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。
Proc Natl Acad Sci U S A. 2011 Jun 7;108(23):9661-6. doi: 10.1073/pnas.1015666108. Epub 2011 May 18.
2
Neural correlates of local parallelism during naturalistic vision.自然视觉过程中局部并行性的神经关联
PLoS One. 2022 Jan 21;17(1):e0260266. doi: 10.1371/journal.pone.0260266. eCollection 2022.
3
Natural scene categories revealed in distributed patterns of activity in the human brain.人类大脑活动分布模式中揭示的自然场景类别。
J Neurosci. 2009 Aug 26;29(34):10573-81. doi: 10.1523/JNEUROSCI.0559-09.2009.
4
Contour junctions underlie neural representations of scene categories in high-level human visual cortex.轮廓连接是人类高级视觉皮层中场景类别神经表征的基础。
Neuroimage. 2016 Jul 15;135:32-44. doi: 10.1016/j.neuroimage.2016.04.021. Epub 2016 Apr 23.
5
Representational differences between line drawings and photographs of natural scenes: A dissociation between multi-voxel pattern analysis and repetition suppression.自然场景线条图和照片之间的表象差异:多体素模式分析与重复抑制的分离。
Neuropsychologia. 2018 Aug;117:513-519. doi: 10.1016/j.neuropsychologia.2018.06.013. Epub 2018 Jun 21.
6
Evidence for participation by object-selective visual cortex in scene category judgments.客体选择性视觉皮层参与场景类别判断的证据。
J Vis. 2014 Aug 21;14(9):19. doi: 10.1167/14.9.19.
7
Functional Subdomains within Scene-Selective Cortex: Parahippocampal Place Area, Retrosplenial Complex, and Occipital Place Area.场景选择性皮层内的功能亚区:海马旁回位置区、压后复合体和枕叶位置区。
J Neurosci. 2016 Oct 5;36(40):10257-10273. doi: 10.1523/JNEUROSCI.4033-14.2016.
8
Real-world scene representations in high-level visual cortex: it's the spaces more than the places.高级视觉皮层中的真实场景表示:重要的是空间而不是地点。
J Neurosci. 2011 May 18;31(20):7322-33. doi: 10.1523/JNEUROSCI.4588-10.2011.
9
Spatial frequency processing in scene-selective cortical regions.场景选择性皮质区域中的空间频率处理
Neuroimage. 2015 May 15;112:86-95. doi: 10.1016/j.neuroimage.2015.02.058. Epub 2015 Mar 6.
10
Distinct representations of spatial and categorical relationships across human scene-selective cortex.人类场景选择性皮层中空间和类别关系的不同表示。
Proc Natl Acad Sci U S A. 2019 Oct 15;116(42):21312-21317. doi: 10.1073/pnas.1903057116. Epub 2019 Sep 30.

引用本文的文献

1
Characterizing internal models of the visual environment.表征视觉环境的内部模型。
Proc Biol Sci. 2025 Aug;292(2053):20250602. doi: 10.1098/rspb.2025.0602. Epub 2025 Aug 20.
2
Identifying and characterizing scene representations relevant for categorization behavior.识别并表征与分类行为相关的场景表征。
Imaging Neurosci (Camb). 2025 Jan 21;3. doi: 10.1162/imag_a_00449. eCollection 2025.
3
Function over form: The temporal evolution of affordance-based scene categorization.功能胜于形式:基于可供性的场景分类的时间演变
J Vis. 2025 Jul 1;25(8):10. doi: 10.1167/jov.25.8.10.
4
A scene-selective region in the superior parietal lobule for visually guided navigation.顶上小叶中用于视觉引导导航的场景选择区域。
Cereb Cortex. 2025 Apr 1;35(4). doi: 10.1093/cercor/bhaf082.
5
Spatial frequency preferences of representations of indoor and natural scene categories in scene-selective regions under different conditions of contrast.在不同对比度条件下,场景选择区域中室内和自然场景类别表征的空间频率偏好。
Front Neurosci. 2025 Feb 7;19:1534588. doi: 10.3389/fnins.2025.1534588. eCollection 2025.
6
No evidence for a privileged role of global ensemble statistics in rapid scene perception: A registered replication attempt.没有证据表明全局整体统计在快速场景感知中具有特殊作用:一项注册复制尝试。
Atten Percept Psychophys. 2025 Feb;87(2):685-697. doi: 10.3758/s13414-024-02994-4. Epub 2024 Dec 10.
7
Drawing as a versatile cognitive tool.绘画作为一种多功能的认知工具。
Nat Rev Psychol. 2023 Sep;2(9):556-568. doi: 10.1038/s44159-023-00212-w. Epub 2023 Jul 17.
8
The Development of Object Recognition Requires Experience with the Surface Features of Objects.物体识别的发展需要对物体表面特征有体验。
Animals (Basel). 2024 Jan 17;14(2):284. doi: 10.3390/ani14020284.
9
Memorability of line drawings of scenes: the role of contour properties.场景线条画的记忆性:轮廓属性的作用。
Mem Cognit. 2025 Jan;53(1):33-53. doi: 10.3758/s13421-023-01478-4. Epub 2023 Oct 30.
10
Testing the generalization of neural representations.测试神经表示的泛化能力。
Neuroimage. 2023 Sep;278:120258. doi: 10.1016/j.neuroimage.2023.120258. Epub 2023 Jul 8.

本文引用的文献

1
Where do objects become scenes?物体在何处成为场景?
Cereb Cortex. 2011 Aug;21(8):1738-46. doi: 10.1093/cercor/bhq240. Epub 2010 Dec 8.
2
Natural scene categories revealed in distributed patterns of activity in the human brain.人类大脑活动分布模式中揭示的自然场景类别。
J Neurosci. 2009 Aug 26;29(34):10573-81. doi: 10.1523/JNEUROSCI.0559-09.2009.
3
Neural mechanisms of rapid natural scene categorization in human visual cortex.人类视觉皮层中快速自然场景分类的神经机制。
Nature. 2009 Jul 2;460(7251):94-7. doi: 10.1038/nature08103. Epub 2009 Jun 7.
4
Decoding the representation of multiple simultaneous objects in human occipitotemporal cortex.解码人类枕颞叶皮质中多个同时存在的物体的表征。
Curr Biol. 2009 Jun 9;19(11):943-7. doi: 10.1016/j.cub.2009.04.020. Epub 2009 May 14.
5
The briefest of glances: the time course of natural scene understanding.最短暂的一瞥:自然场景理解的时间进程。
Psychol Sci. 2009 Apr;20(4):464-72. doi: 10.1111/j.1467-9280.2009.02316.x.
6
What do we perceive in a glance of a real-world scene?在一瞥现实世界场景时我们能感知到什么?
J Vis. 2007 Jan 31;7(1):10. doi: 10.1167/7.1.10.
7
Differential parahippocampal and retrosplenial involvement in three types of visual scene recognition.海马旁回和压后皮质在三种视觉场景识别中的差异参与情况
Cereb Cortex. 2007 Jul;17(7):1680-93. doi: 10.1093/cercor/bhl079. Epub 2006 Sep 22.
8
Decoding the visual and subjective contents of the human brain.解读人类大脑的视觉及主观内容。
Nat Neurosci. 2005 May;8(5):679-85. doi: 10.1038/nn1444. Epub 2005 Apr 24.
9
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.猫视觉皮层中的感受野、双眼相互作用及功能结构
J Physiol. 1962 Jan;160(1):106-54. doi: 10.1113/jphysiol.1962.sp006837.
10
Cortical analysis of visual context.视觉情境的皮质分析。
Neuron. 2003 Apr 24;38(2):347-58. doi: 10.1016/s0896-6273(03)00167-3.

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.

DOI:10.1073/pnas.1015666108
PMID:21593417
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3111263/
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。