权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Hemispheric and topographic neural organization of high-level visual representations

高级视觉表征的半球和地形神经组织

基本信息

批准号：
2123069
负责人：
David Plaut
金额：
$ 75.02万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123069&HistoricalAwards=false
关键词：
Hemispheric topographic neural organization level

项目摘要

People recognize faces, words, and other common objects with remarkable speed and accuracy. This project addresses the way that visual knowledge of faces, words, and objects becomes organized in the brain through learning. Prior research has shown that the right cerebral hemisphere is specialized for the representation of faces, whereas the left cerebral hemisphere has a stronger representation for words, with the representation of objects approximately balanced across both hemispheres. These differences in hemispheric specialization are a matter of degree and vary across individuals. Investigation of these differences requires behavioral testing of recognition abilities, combined with neuroimaging of brain structure and brain activation patterns, to understand how the visual recognition system is organized both within and between the two cerebral hemispheres, how this organization emerges with visual experience, and how and why it varies across individuals. In parallel with human behavioral and neuroimaging studies, another facet of this project is the development of a computational simulation of the visual recognition system, using an artificial neural network that learns to recognize faces, words and objects as well as people do. This simulation model is designed to mimic the properties of the brain. Variants of the model can reproduce the behavior and even capture the underlying differences in brain organization of different individuals. This project advances our understanding of the neural basis and underlying brain organization for acquisition of face and word recognition. This research has profound therapeutic implications for millions of Americans who require remediation of developmental disorders like dyslexia, who need assistance to overcome difficulties in letter and word recognition, and remediation for recovery of reading and language abilities after stroke or neurosurgery. Visual recognition is supported by a network of brain regions in both cerebral hemispheres, that starts with some initial structure in early childhood and then develops through experience to have graded specialization for different types of stimuli. To understand better how recognition occurs, this large-scale study investigates brain structure (e.g., white matter connectivity), brain function (e.g., selectivity of neural activation) and behavior (e.g., with visual stimuli such as faces, words, and objects presented to one hemisphere) over a large group of subjects. This multi-pronged approach allows us to test the prediction that participants with greater within-hemisphere and/or weaker between-hemisphere connectivity will show greater lateralization for faces to the left hemisphere and words to the right hemisphere (but no difference for objects). A second study tests predictions that variability in language lateralization of the brain (across right- and left-handed individuals) explains individual differences in word lateralization, and this, in turn, influences face lateralization. A final study examines fine-grained changes in brain activity as individuals are exposed to, and learn, novel visual object categories, allowing us to test predictions about competition in representing new information compared with known categories (faces and words). In parallel with these studies, this research includes the development of a computational model of the visual recognition system by training a spatially constrained multi-layer artificial neural network to recognize a large number of faces, words, and objects. The model’s performance is evaluated against data of the human studies to determine whether this model can re-create and explain aspects of the system under investigation, including the distinct patterns of different individuals. In identifying the principles underlying the organization of high-level visual information, the work has important implications for knowledge acquisition and learning, both in typical individuals across the lifespan, and in those facing difficulties due to developmental or neuropsychological disorders.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们以惊人的速度和准确度识别人脸、单词和其他常见物体。这个项目阐述了通过学习在大脑中组织关于人脸、文字和物体的视觉知识的方式。先前的研究表明，右脑专门用于表征面孔，而左半球对词汇的表征更强，在两个半球上对物体的表征大致平衡。半球专业化的这些差异是一个程度问题，并因个体而异。对这些差异的研究需要对识别能力进行行为测试，结合对大脑结构和大脑激活模式的神经成像，以了解视觉识别系统是如何在两个大脑半球内和之间组织的，这个组织是如何产生视觉体验的，以及它如何以及为什么在不同的人之间存在差异。除了人类行为和神经成像研究，该项目的另一个方面是开发视觉识别系统的计算模拟，使用人工神经网络，它可以像人一样学习识别人脸、单词和物体。这个模拟模型旨在模拟大脑的特性。该模型的变体可以重现行为，甚至可以捕捉到不同个体大脑组织的潜在差异。这个项目促进了我们对获得人脸和单词识别的神经基础和潜在的大脑组织的理解。这项研究对数以百万计的美国人具有深远的治疗意义，他们需要治疗阅读困难等发育障碍，需要帮助克服字母和单词识别困难，需要治疗中风或神经外科手术后恢复阅读和语言能力。视觉识别是由两个大脑半球的大脑区域网络支持的，该网络从儿童早期的一些初始结构开始，然后通过经验发展到对不同类型的刺激具有分级的专门化。为了更好地理解识别是如何发生的，这项大规模的研究调查了一大群受试者的大脑结构(例如，白质连通性)、大脑功能(例如，神经激活的选择性)和行为(例如，与视觉刺激，如脸、单词和物体呈现在一个半球上)。这种多管齐下的方法让我们可以测试这样一种预测，即大脑半球内连接性较强和/或大脑半球间连接性较弱的参与者将表现出更大的面孔向左半球和词汇向右半球的偏侧化(但对物体没有差异)。第二项研究测试了这样的预测，即大脑语言侧化的可变性(左右利手个体)解释了词汇侧化的个体差异，而这反过来又影响面孔侧化。最后一项研究考察了当个人接触和学习新的视觉对象类别时，大脑活动的细粒度变化，使我们能够测试与已知类别(人脸和单词)相比，在表示新信息方面存在竞争的预测。与这些研究并行的是，这项研究包括通过训练空间受限的多层人工神经网络来识别大量的人脸、单词和对象来开发视觉识别系统的计算模型。该模型的性能是根据人体研究的数据进行评估的，以确定该模型是否可以重新创建和解释所调查系统的各个方面，包括不同个人的不同模式。在确定组织高级视觉信息的基本原则方面，这项工作对知识的获取和学习具有重要影响，无论是在典型的人的一生中，还是在那些因发育或神经心理障碍而面临困难的人中。这一奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。