Deep neural network representations align well with brain activity in the
ventral visual stream. However, the primate visual system has a distinct dorsal
processing stream with different functional properties. To test if a model
trained to perceive 3D scene geometry aligns better with neural responses in
dorsal visual areas, we trained a self-supervised geometry-aware recurrent
neural network (GRNN) to predict novel camera views using a 3D feature memory.
We compared GRNN to self-supervised baseline models that have been shown to
align well with ventral regions using the large-scale fMRI Natural Scenes
Dataset (NSD). We found that while the baseline models accounted better for
ventral brain regions, GRNN accounted for a greater proportion of variance in
dorsal brain regions. Our findings demonstrate the potential for using
task-relevant models to probe representational differences across visual
streams.

深度神经网络在腹侧视觉流中与脑活动相吻合。然而，灵长类动物视觉系统具有与之不同的背侧处理流，具有不同的功能性质。本研究旨在通过训练自监督几何感知递归神经网络（GRNN）来预测新的相机视图，使用 3D 特征存储器来测试该模型是否与背侧视觉区域的神经响应更吻合。与已被证明在腹侧区域中吻合良好的自监督基准模型相比较，我们发现 GRNN 在背侧脑区能更好地解释方差的比例。我们的研究结果表明了利用任务相关模型来研究视觉流之间的表征差异的潜力。