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，但对于没有视网膜输入的视觉感知是如何产生的，或者已知的视觉表征的重要组成部分的视觉特征是如何在心理意象中驱动神经活动的，人们知之甚少。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。当前工作的目的是发展对视觉特征在真实图像和心理图像中表示的异同的基本理解。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中基本上是保守的，并且将活动与真实图像联系起来的接受野应该预测心理意象所诱发的活动。尽管如此，心理图像与真实图像明显不同，我们认为三个潜在的差异来源：(1)注意对心理图像的夸大效应的可能性；(2)在心理意象过程中，来自具有大接收野（相对于视网膜）的高级视觉区域的反馈连接的主要影响；(3)产生心理图像的神经过程与产生视网膜图像的物理过程的差异。提出了两个具体的目标，将采用一种创新的新方法来分析功能性MRI信号，该方法基于接受野的体素建模。在这种方法下，对获取的体素中的每一个体素构建一个单独的预测模型。该模型将体素中测量的活动直接与特定的视觉特征联系起来，包括空间频率、方向、对象类别和对象位置。然后，这些模型可以用来解码从测量的大脑活动中感知或回忆的场景。我们期望我们的贡献将有助于我们进一步了解决定图像和感知活动之间一致性程度的具体因素，以及我们对活动最一致的高级视觉区域进行定量建模的能力的重大进步。这一贡献意义重大，因为它将使我们朝着意象感受野的发展迈出几个必要的步骤，即预测感受野模型，它解释了当场景以心理图像的形式被回忆起来时，场景中的视觉特征是如何驱动活动的。心理意象的接受野模型将提供一种解码算法，用于客观地解释甚至形象地重建心理意象。

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.