CAREER: Hierarchical Representations for Visual Categorization and Decision Making

职业:视觉分类和决策的层次表示

基本信息

  • 批准号:
    0955640
  • 负责人:
  • 金额:
    $ 96.33万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2010
  • 资助国家:
    美国
  • 起止时间:
    2010-07-01 至 2015-06-30
  • 项目状态:
    已结题

项目摘要

Humans and other advanced animals have an impressive capacity to recognize the behavioral significance, or category membership, of a wide range of sensory stimuli. This ability is critical, because it allows us to respond appropriately to the continuous stream of stimuli and events that we encounter in our interactions with the environment. Of course, we are not born with a built-in library of meaningful categories, such as 'tables' and 'chairs,' that we are pre-programmed to recognize. Instead, we learn to recognize the meaning of such stimuli through experience. With National Science Foundation Funding, Dr. David J. Freedman is carrying out studies whose goal is to understand how visual-feature encoding in early visual processing areas is transformed into more meaningful representations at more advanced neuronal processing stages in the brain. The goals of the proposed studies are to compare neuronal representations of visual-motion processing stages across a network of interconnected brain areas in and around the parietal lobe during visual motion categorization tasks. Specifically, one series of experiments compares neuronal responses in two distinct interconnected regions of parietal cortex, the lateral and medial interparietal areas, which are known to be more involved in visual and somatosensory or motor processing, respectively. Activity in these two areas is examined during a categorization task that requires motor decisions to be executed in response to visual stimuli, allowing the relative roles of the two areas in the decision making process to be determined. A second series of experiments is comparing cortical activity in the lateral intraparietal and prefrontal cortices during a novel visual categorization task in which subjects learn multiple independent category rules and apply those rules flexibly and dynamically to incoming visual stimuli. This study gives critical insights into the contributions of frontal and parietal cortex to flexible rule-based categorization. Together, these studies can yield important insights into how learning influences the encoding of visual information and into the roles of interconnected networks of parietal and frontal cortices in visual recognition and decision making.While much is known about how the brain processes simple sensory features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meanings or category of stimuli. A greater understanding of visual learning and categorization is critical for addressing a number of brain diseases and conditions (e.g., stroke, Alzheimer's disease, attention deficit disorder, and schizophrenia) that leave patients impaired in everyday tasks that require visual learning, recognition, and/or evaluating and responding appropriately to sensory information. Dr. Freedman's research is helping to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed basic understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction of school age children and young adults. A more detailed understanding of the basic brain mechanisms underlying learning, memory and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive faculties.
人类和其他高级动物具有令人印象深刻的能力,能够识别各种感觉刺激的行为意义或类别成员关系。这种能力是至关重要的,因为它使我们能够对我们在与环境互动中遇到的源源不断的刺激和事件做出适当的反应。当然,我们与生俱来就没有一个内置的有意义的类别库,比如我们预先编程识别的“桌子”和“椅子”。取而代之的是,我们学会通过体验来认识这些刺激的意义。在国家科学基金会的资助下,大卫·J·弗里德曼博士正在进行研究,其目标是了解早期视觉处理区域的视觉特征编码是如何在大脑中更高级的神经元处理阶段转化为更有意义的表征的。这项研究的目的是比较视觉运动分类任务中顶叶及其周围相互连接的脑区网络中视觉运动加工阶段的神经元表征。具体地说,一系列实验比较了顶叶皮质两个不同的相互关联区域--外侧和内侧顶叶间区--的神经元反应,这两个区域分别被认为更多地参与视觉、躯体感觉或运动处理。这两个区域的活动在一项分类任务中被检查,该任务要求对视觉刺激执行运动决策,从而确定这两个区域在决策过程中的相对角色。第二个系列实验是比较在一项新的视觉分类任务中,被试学习多个独立的分类规则,并灵活地、动态地将这些规则应用于传入的视觉刺激的情况下,大脑外侧、顶内和前额叶皮质的活动。这项研究对额叶和顶叶皮质对灵活的基于规则的分类的贡献给予了关键的见解。总而言之,这些研究可以对学习如何影响视觉信息的编码以及顶叶和额叶皮质相互连接的网络在视觉识别和决策中的作用产生重要的见解。虽然人们对大脑如何处理简单的感觉特征(如颜色、方向和运动方向)知道得很多,但对大脑如何学习和代表刺激的含义或类别知之甚少。更好地了解视觉学习和分类对于解决许多大脑疾病和状况(例如,中风、阿尔茨海默病、注意力缺陷障碍和精神分裂症)至关重要,这些疾病和条件会使患者在需要视觉学习、识别和/或适当评估和响应感官信息的日常任务中受损。弗里德曼博士的研究帮助人们对构成学习、记忆和认知的大脑机制有了详细的基本了解,从而有助于指导下一代针对这些脑部疾病和障碍的治疗。这些研究还与了解和解决学习障碍有关,如注意力缺陷障碍和阅读障碍,这些障碍影响到相当一部分学龄儿童和年轻人。更详细地了解学习、记忆和注意力背后的基本大脑机制,可能会为涉及这些认知能力的疾病的原因和潜在治疗方法提供重要的见解。

项目成果

期刊论文数量(0)
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David Freedman其他文献

Real People: Personal Identity without Thought Experiments
真实的人:没有思想实验的个人身份
  • DOI:
  • 发表时间:
    1989
  • 期刊:
  • 影响因子:
    0
  • 作者:
    David Freedman
  • 通讯作者:
    David Freedman
Detection of an intracellular transforming protein (v-Ki-ras p21) using the flow activated cell sorter (FACS)
The Markov moment problem and de Finetti’s theorem: Part II
  • DOI:
    10.1007/s00209-003-0636-6
  • 发表时间:
    2004-01-14
  • 期刊:
  • 影响因子:
    1.000
  • 作者:
    Persi Diaconis;David Freedman
  • 通讯作者:
    David Freedman
Are There Algorithms That Discover Causal Structure?
是否有发现因果结构的算法?
  • DOI:
  • 发表时间:
    1999
  • 期刊:
  • 影响因子:
    1.5
  • 作者:
    David Freedman;P. Humphreys
  • 通讯作者:
    P. Humphreys
The empirical distribution of the fourier coefficients of a sequence of independent, identically distributed long-tailed random variables

David Freedman的其他文献

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{{ truncateString('David Freedman', 18)}}的其他基金

SBIR Phase II: High-Throughput and Scalable Nanoparticle Characterization for Life Sciences Applications
SBIR 第二阶段:生命科学应用的高通量和可扩展的纳米颗粒表征
  • 批准号:
    1831192
  • 财政年份:
    2018
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Standard Grant
SBIR Phase I: High-Throughput Nanoparticle Characterization for Life Sciences Applications
SBIR 第一阶段:生命科学应用的高通量纳米颗粒表征
  • 批准号:
    1721652
  • 财政年份:
    2017
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Standard Grant
NCS-FO: Collaborative Research: Flexible Rule-Based Categorization in Neural Circuits and Neural Network Models
NCS-FO:协作研究:神经电路和神经网络模型中基于规则的灵活分类
  • 批准号:
    1631571
  • 财政年份:
    2016
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Standard Grant
SBIR Phase I: Single molecule detection of biomarkers using gold nanorods for in-vitro diagnostics
SBIR 第一阶段:使用金纳米棒进行生物标志物的单分子检测,用于体外诊断
  • 批准号:
    1448319
  • 财政年份:
    2015
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Standard Grant
I-Corps: Multiplexed Diagnostic Platform for Rapid Diagnostics
I-Corps:用于快速诊断的多重诊断平台
  • 批准号:
    1357654
  • 财政年份:
    2013
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Standard Grant
Mathematical Sciences: Chance Processes and the Foundation of Statistics
数学科学:机会过程和统计学基础
  • 批准号:
    9208677
  • 财政年份:
    1992
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Continuing Grant
Mathematical Sciences: Chance Processes
数学科学:机会过程
  • 批准号:
    8901714
  • 财政年份:
    1989
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Continuing Grant
Mathematical Sciences: Chance Processes
数学科学:机会过程
  • 批准号:
    8601634
  • 财政年份:
    1986
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Continuing Grant
Mathematical Sciences: Chance Processes
数学科学:机会过程
  • 批准号:
    8301812
  • 财政年份:
    1983
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Continuing Grant
Chance Processes
机会过程
  • 批准号:
    8002535
  • 财政年份:
    1980
  • 资助金额:
    $ 96.33万
  • 项目类别:
    Continuing Grant

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丙烷脱氢Pt@hierarchical zeolite催化剂的设计制备与反应调控
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Hierarchical Deep Representations of Anatomy (HiDRA)
解剖学的层次深度表示 (HiDRA)
  • 批准号:
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    10587270
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Computational modelling visual perception in a biologically realistic neural network: Developing rich, hierarchical representations of visual scenes
生物现实神经网络中视觉感知的计算建模:开发视觉场景的丰富、分层表示
  • 批准号:
    2108388
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    2018
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    $ 96.33万
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顺序技能的分层控制:使用脑电图解码底层表征
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