Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images.
Generative Feedback Explains Distinct Brain Activity Codes for Seen and Mental Images.
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DOI：
10.1016/j.cub.2020.04.014
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发表时间：
2020-06-22
期刊：
影响因子：
9.2
通讯作者：
Naselaris, Thomas
中科院分区：
文献类型：
--
作者：
Breedlove, Jesse L.;St-Yves, Ghislain;Olman, Cheryl A.;Naselaris, Thomas
心理意象和视觉之间的关系是神经科学中一个长期存在的问题。目前，还不知道在视觉期间诱发的活动和在图像期间恢复的活动之间的差异是否反映了所看到的图像和心理图像的不同代码。为了解决这个问题，我们将人脑中的心理意象建模为分层生成网络中的反馈。这样的网络通过从网络层次结构的较高层向较低层馈送抽象表示来合成图像。当较高的处理水平比较低的处理水平对刺激变化不那么敏感时，如在人类大脑中，低水平视觉区域的活动应该以比所看到的图像更低的精度编码心理图像的变化。为了验证这一预测，我们进行了一项功能磁共振成像实验，让受试者想象并观看数百个空间变化的自然刺激。为了分析这些数据，我们开发了图像编码模型。这些模型准确地预测了大脑对想象刺激的反应，并能够准确解码它们的位置和内容。它们还允许我们比较每个体素，调整到看到和想象的空间频率，以及视觉和想象空间中感受野的位置和大小。我们证实了我们的预测，表明，在低层次的视觉领域，想象的空间频率在个别体素相对于看到的空间频率减少，在想象的空间感受野大于视觉空间。这些发现揭示了视觉图像和心理图像的不同代码，并将心理图像与生成网络的计算能力联系起来。Breedlove，St-Yves等人分析了人类受试者的大脑活动，产生了数百个心理图像。低级视觉皮层区域编码心理图像，就像高级区域编码看到的图像一样。这种编码策略出现在视觉皮层的网络模型中，该模型通过将表征从较高层次馈送到较低层次来生成图像。
The relationship between mental imagery and vision is a long-standing problem in neuroscience. Currently, it is not known whether differences between the activity evoked during vision and reinstated during imagery reflect different codes for seen and mental images. To address this problem, we modeled mental imagery in the human brain as feedback in a hierarchical generative network. Such networks synthesize images by feeding abstract representations from higher to lower levels of the network hierarchy. When higher processing levels are less sensitive to stimulus variation than lower processing levels, as in the human brain, activity in low-level visual areas should encode variation in mental images with less precision than seen images. To test this prediction, we conducted an fMRI experiment in which subjects imagined and then viewed hundreds of spatially varying naturalistic stimuli. To analyze these data, we developed imagery-encoding models. These models accurately predicted brain responses to imagined stimuli and enabled accurate decoding of their position and content. They also allowed us to compare, for every voxel, tuning to seen and imagined spatial frequencies, as well as the location and size of receptive fields in visual and imagined space. We confirmed our prediction, showing that, in low-level visual areas, imagined spatial frequencies in individual voxels are reduced relative to seen spatial frequencies and that receptive fields in imagined space are larger than in visual space. These findings reveal distinct codes for seen and mental images and link mental imagery to the computational abilities of generative networks. Breedlove, St-Yves, et al. analyze brain activity of human subjects generating hundreds of mental images. Low-level visual cortical areas encode mental images much like high-level areas encode seen images. This coding strategy emerges in a network model of visual cortex that generates images by feeding representations from higher to lower levels.
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