Representation of Visual Features in Mental Images of Complex Scenes.
复杂场景心理图像中视觉特征的表示。
基本信息
- 批准号:9033118
- 负责人:
- 金额:$ 37.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-04-01 至 2019-03-31
- 项目状态:已结题
- 来源:
- 关键词:AlgorithmsAttentionAwarenessBrainBrain imagingCategoriesComplexDataDevelopmentDiagnosisEnvironmentExhibitsFeedbackFoundationsFrequenciesFunctional Magnetic Resonance ImagingGoalsHealthImageImageryKnowledgeLinkLocationMapsMeasuresMental HealthModelingOutcomePerceptionPlayProcessPsyche structurePublic HealthResearchRetinaRetinalRoleSignal TransductionSourceSpatial DistributionSystemTestingVisionVisualVisual CortexWorkbasecognitive processdesignextrastriate visual cortexinnovationmental imageryneuromechanismnovel strategiesphysical processpredictive modelingreceptive fieldrelating to nervous systemretinotopic
项目摘要
DESCRIPTION (provided by applicant): Mental imagery is a salient part of mental awareness but very little is understood about how visual percepts are generated without retinal input, or how visual features that are known to be an important part of visual representation drive neural activity during mental imagery. Our long-term goal is to provide clinicians with the ability to objectively interpret mental images by accessing underlying neural activity. The objective of the current work is to develop a basic understanding of the similarities and differences between the representation of visual features in veridical and mental images. Our central hypothesis is that the mechanisms for representing visual features during perception are fundamentally conserved during mental imagery and that receptive fields that link activity to veridical images should predict activity evoked by mental imagery. Nonetheless, mental images are clearly distinguishable from veridical images and we consider three potential sources of difference: (1) The potential for exaggerated effects of attention on mental imagery; (2) The predominate influence of feedback connections from high-level visual areas with large receptive fields (relative to the retina) during mental imagery; (3) Differences between the neural processes of generating mental images and the physical processes that generate retinal images. Two Specific Aims are proposed that will be pursued using an innovative new approach for analyzing functional MRI signals that is based upon voxel-wise modeling of receptive fields. Under this approach, a separate predictive model is constructed for each and every voxel in the acquired volumes. The model links activity measured in a voxel directly to specific visual features, including spatial frequency, orientation, object category, and object location. The models can then be used to decode perceived or recalled scenes from measured brain activity. We expect that our contribution will be an advance in our understanding of the specific factors that determine the degree of consistency between activity during imagery and perception, as well as a significant advance in our ability to quantitatively model the high-level visual areas where activity is most consistent. This contribution will be significant because it will take us several necessary steps toward the development of imagery receptive fields-predictive receptive field models that explain how the visual features in a scene drive activity when the scene is recalled in the form of a mental image. A receptive field model for mental imagery would place within reach a decoding algorithm for objectively interpreting and even pictorially reconstructing mental images.
描述(申请人提供):心理表象是心理意识的一个重要部分,但对于视觉感知是如何在没有视网膜输入的情况下产生的,或者被认为是视觉表征的重要部分的视觉特征如何在心理表象过程中驱动神经活动,人们了解的很少。我们的长期目标是为临床医生提供通过访问潜在的神经活动来客观解释心理图像的能力。当前工作的目标是对真实图像和心理图像中视觉特征的表征之间的异同有一个基本的理解。我们的中心假设是,在感知过程中表征视觉特征的机制在心理意象过程中基本上是保守的,将活动与真实图像联系起来的接受场应该预测心理意象引起的活动。尽管如此,心理图像与真实图像是明显不同的,我们考虑了三个潜在的差异来源:(1)注意对心理图像的潜在夸大效应;(2)心理图像期间来自具有大接受视野(相对于视网膜)的高级视觉区域的反馈连接的主要影响;(3)心理图像生成的神经过程与生成视网膜图像的物理过程之间的差异。提出了两个具体的目标,将使用一种创新的新方法来分析功能磁共振信号,该方法基于感受场的体素建模。在这种方法下,为获取的体积中的每个体素构建单独的预测模型。该模型将以体素测量的活动直接链接到特定的视觉特征,包括空间频率、方向、对象类别和对象位置。然后,这些模型可以用来从测量的大脑活动中解码感知或回忆的场景。我们预计,我们的贡献将是我们对决定成像和感知期间活动之间一致性程度的特定因素的理解的进步,以及我们对活动最一致的高级视觉区域进行量化建模的能力的显著进步。这一贡献将是重要的,因为它将使我们朝着意象接受场的发展采取几个必要的步骤--预测性接受场模型,解释当场景以心理图像的形式被回忆时,场景中的视觉特征如何驱动活动。心理图像的接受场模型将使解码算法变得触手可及,从而客观地解释甚至图形化地重建心理图像。
项目成果
期刊论文数量(0)
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THOMAS P NASELARIS其他文献
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{{ truncateString('THOMAS P NASELARIS', 18)}}的其他基金
Representation of Visual Features in Mental Images of Complex Scenes.
复杂场景心理图像中视觉特征的表示。
- 批准号:
8698031 - 财政年份:2014
- 资助金额:
$ 37.38万 - 项目类别:
Population analysis of shape representation in V4
V4 中形状表示的总体分析
- 批准号:
7232722 - 财政年份:2006
- 资助金额:
$ 37.38万 - 项目类别:
Population analysis of shape representation in V4
V4 中形状表示的总体分析
- 批准号:
7111214 - 财政年份:2006
- 资助金额:
$ 37.38万 - 项目类别:
Population analysis of shape representation in V4
V4 中形状表示的总体分析
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7483599 - 财政年份:2006
- 资助金额:
$ 37.38万 - 项目类别:
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