Representation of Visual Features in Mental Images of Complex Scenes.

描述（由申请人提供）：心理意象是心理意识的重要组成部分，但很少有人了解视觉感知是如何在没有视网膜输入的情况下产生的，或者已知是视觉表征重要组成部分的视觉特征如何在心理意象期间驱动神经活动。我们的长期目标是通过访问潜在的神经活动，为临床医生提供客观解释心理图像的能力。目前的工作的目标是发展一个基本的理解的相似性和差异之间的视觉特征的真实和心理图像的表示。我们的中心假设是，在感知过程中代表视觉特征的机制在心理意象中基本上是保守的，并且将活动与真实图像联系起来的感受野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，我们认为有三个潜在的差异来源：（1）注意力对心理图像的夸大效应的可能性;（2）来自具有大感受野的高级视觉区域的反馈连接的主导影响（3）产生心理意象的神经过程与产生视网膜意象的物理过程之间的差异。提出了两个具体的目标，将采用创新的新方法来分析功能性MRI信号，是基于体素的感受野建模。在这种方法下，为采集体积中的每个体素构建单独的预测模型。该模型将在体素中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，这些模型可以用于从测量的大脑活动中解码感知或回忆的场景。我们希望我们的贡献将是我们的理解的具体因素，确定在图像和感知活动之间的一致性程度的进步，以及我们的能力，定量建模的活动是最一致的高层次视觉区域的显着进步。这一贡献将是重要的，因为它将使我们朝着意象感受野的发展迈出几步预测感受野模型解释了当场景以心理意象的形式被回忆时，场景中的视觉特征如何驱动活动。心理意象的感受野模型将使解码算法变得触手可及，从而客观地解释甚至用图像重建心理意象。

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.