Object, face, body and scene representations in the human brain

人脑中的物体、面部、身体和场景表征

• 批准号: 9568274
• 负责人: Christopher Baker
• 金额: $ 132.1万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9568274
• 关键词: Anterior; Area; Behavior; Body measure procedure; Body part; Brain; Brain imaging; Brain region; Categories; Color; Complex; Computer Simulation; Data; Electroencephalography; Exhibits; Eye; Face; Functional Magnetic Resonance Imaging; Goals; Hand; Human; Image; Individual; Knowledge; Learning; Leg; Light; Maps; Measures; Medial; Memory; Mental Health; Monitor; Nature; Parietal Lobe; Participant; Perception; Property; Research; Resolution; Retina; Role; Social Behavior; Stimulus; Surface; Temporal Lobe; Theoretical model; Thinking; Time; Transcranial magnetic stimulation; Visual; Visual Cortex; Visual Fields; behavior measurement; experience; experimental study; falls; foot; insight; interest; millisecond; nervous system disorder; response; retinotopic; temporal measurement; visual processing; visual stimulus; way finding

The goal of this research is to understand how we see what we see: how does the brain analyze the light falling on the retina of the eye to reveal a world full of objects, people and things? During the past year we have focused on the perception of complex visual stimuli, in particular real-world visual scenes (NCT00001360). Perception of real world scenes: Real-world scenes are incredibly complex and heterogeneous, yet we are able to identify and categorize them effortlessly. While prior studies have identified three major brain regions that appear to be specialized for scene processing, it remains unclear what the precise roles of these different regions are and what information they contain. Building on a general framework for visual processing we proposed in the past few years, we have been investigating the basic properties of these three scene-selective regions and trying to elucidate how these three regions interact to enable us to understand the world before our eyes. In particular, we have been investigating the extent to which these regions can be explained in terms of encoding low level visual properties (e.g. contrast, color, edges) versus high level properties (e.g. objects, category, actions that can be performed in the depicted scene). One main area of focus has been the relationship between retinotopy (point-by-point mapping of the visual field onto the cortical surface of the brain) and category-selectivity (differential responses to images from different visual categories e.g. scenes versus faces). We evaluate retinotopy by presenting fragments of scenes to specific portions of the visual field and measure the response across the brain with functional magnetic resonance imaging (fMRI). Similarly, we measure category-selectivity by presenting images from different categories (e.g. faces, scenes, objects, bodies) and measuring the associated brain response. We find that there is no simple relationship between these two different organizational principles. Category-selective regions exhibit retinotopy, but individual category-selective regions overlap multiple maps. These results suggests that individual category-selective regions may contain multiple sub-regions within specific retinotopic maps that perform separate computations on the images. One of these scene-selective regions is found in medial parietal cortex and is often implicated in memory function and spatial navigation. Our data show that there may be a gradient of function within medial parietal cortex. Posterior regions show strong retinotopy and scene-selectivity and are most strongly connected with other regions of posterior visual cortex. In contrast anterior regions are much less retinotopic and scene-selective but show strong connectivity with regions of ventral temporal cortex and parietal cortex that are implicated in memory. In addition to studying how the representations of scenes are spatially distributed within the brain, we have also been examining the time course of visual scene processing using electroencephalography (EEG). In contrast to fMRI, which has good spatial but poor temporal resolution, EEG allows us to monitor brain activity at a millisecond resolution. These experiments have revealed that scene-selective activity and representation of specific scene properties such as naturalness, is not observed until approximately 200 milliseconds after the onset of a scene picture, much later then the initial visual response. Collectively these results provide important insights into the brain network that is involved in processing real-world visual scenes and we have recently developed a specific framework for thinking about the distributed processing of scenes within visual cortex. We are currently evaluating the specific roles of scene-selective regions by i) using transcranial magnetic stimulation (or TMS) to temporarily disrupt their function and observe the impact on behavior; ii) comparing explicit computational and theoretical models of scene representation with the representations observed in different parts of the brain. Elucidating how the brain enables us to recognize objects, scenes, faces and bodies provides important insights into the nature of our internal representations of the world around us. Understanding these representations is vital in trying to determine the underlying deficits in many mental health and neurological disorders.

这项研究的目的是了解我们如何看到我们所看到的：大脑如何分析落在眼睛视网膜上的光线，以揭示一个充满物体，人和事物的世界？ 在过去的一年里，我们专注于复杂视觉刺激的感知，特别是真实世界的视觉场景（NCT 00001360）。 对真实的世界场景的感知： 现实世界的场景是非常复杂和异构的，但我们能够毫不费力地识别和分类它们。虽然先前的研究已经确定了三个主要的大脑区域似乎专门用于场景处理，但仍然不清楚这些不同区域的确切作用以及它们包含的信息。 基于我们在过去几年中提出的视觉处理的一般框架，我们一直在研究这三个场景选择区域的基本属性，并试图阐明这三个区域如何相互作用，使我们能够理解我们眼前的世界。 特别是，我们一直在研究这些区域在多大程度上可以解释的编码低级别的视觉属性（例如对比度，颜色，边缘）与高级别的属性（例如对象，类别，可以在所描绘的场景中执行的动作）。一个主要的焦点领域是视网膜反应（将视野逐点映射到大脑皮层表面）和类别选择性（对来自不同视觉类别的图像的不同反应，例如场景与面孔）之间的关系。我们通过将场景片段呈现给视野的特定部分来评估视网膜病变，并通过功能性磁共振成像（fMRI）测量大脑的反应。类似地，我们通过呈现来自不同类别的图像（例如，面部，场景，物体，身体）并测量相关的大脑反应来测量类别选择性。我们发现，这两种不同的组织原则之间没有简单的关系。类别选择性区域表现出视网膜病变，但个别类别选择性区域重叠多个地图。这些结果表明，个别类别选择性区域可能包含多个子区域内的特定的retinotopic地图上执行单独的计算的图像。 其中一个场景选择区域位于内侧顶叶皮层，通常与记忆功能和空间导航有关。我们的数据表明，可能有一个梯度内顶叶皮层的功能。后部区域显示出强烈的视网膜反应和场景选择性，并且与后部视觉皮层的其他区域联系最紧密。相比之下，前部区域的视网膜定位性和场景选择性要低得多，但与腹侧颞叶皮质和顶叶皮质与记忆有关的区域显示出强烈的连接性。 除了研究场景的表征在大脑中的空间分布外，我们还使用脑电图（EEG）研究了视觉场景处理的时间过程。与功能磁共振成像（fMRI）相比，它具有良好的空间分辨率，但时间分辨率较差，EEG允许我们以毫秒级的分辨率监测大脑活动。这些实验表明，场景选择性活动和特定场景属性（如自然度）的表示直到场景图片开始后约200毫秒才被观察到，比初始视觉反应晚得多。 总的来说，这些结果提供了重要的见解，参与处理现实世界的视觉场景的大脑网络，我们最近开发了一个特定的框架，用于思考视觉皮层内场景的分布式处理。我们目前正在评估场景选择区域的具体作用，方法是：i）使用经颅磁刺激（或TMS）暂时破坏它们的功能，观察对行为的影响; ii）将场景表征的明确计算和理论模型与在大脑不同部位观察到的表征进行比较。 阐明大脑如何使我们能够识别物体，场景，面孔和身体，为我们对周围世界的内部表征的本质提供了重要的见解。理解这些表征对于试图确定许多精神健康和神经系统疾病的潜在缺陷至关重要。