Invariant representation of physical stability in the human brain.
Invariant representation of physical stability in the human brain.
复制标题
DOI：
10.7554/elife.71736
复制
发表时间：
2022-05-30
期刊：
影响因子：
7.7
通讯作者：
Kanwisher, Nancy
中科院分区：
文献类型：
--
作者：
Pramod, R. T.;Cohen, Michael A.;Tenenbaum, Joshua B.;Kanwisher, Nancy
成功地与世界接触，需要有能力预测接下来会发生什么。在这里，我们研究大脑是如何对物理世界做出基本预测的：我们面前的情况是稳定的，因此可能保持不变，还是不稳定，因此可能在不久的将来发生变化。具体地说，我们问，稳定性的判断是否可以得到在机器和大脑中被证明在视觉对象识别方面非常有效的各种表征的支持，或者相反，确定自然场景的物理稳定性的能力可能需要模拟世界物理的生成性算法。为了找出答案，我们测量了卷积神经网络(CNN)和大脑(使用功能磁共振成像)对身体稳定和不稳定场景的自然图像的反应。我们发现，无论是在视觉对象和场景分类(ImageNet)上训练的标准CNN，还是在人类腹侧视觉通路中，都没有证据表明身体稳定性的概括性表征，而人类腹侧视觉通路长期以来一直与同样的过程有关。然而，在先前涉及直觉物理推理的额顶区域，我们发现物理稳定性的场景不变表示，以及对不稳定场景的更高单变量反应。这些结果证明了背侧通路而不是腹侧通路的物理稳定性的抽象表示，这与稳定性的计算不仅需要模式分类，而且需要向前物理模拟的假设是一致的。
Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future. Specifically, we ask if judgments of stability can be supported by the kinds of representations that have proven to be highly effective at visual object recognition in both machines and brains, or instead if the ability to determine the physical stability of natural scenes may require generative algorithms that simulate the physics of the world. To find out, we measured responses in both convolutional neural networks (CNNs) and the brain (using fMRI) to natural images of physically stable versus unstable scenarios. We find no evidence for generalizable representations of physical stability in either standard CNNs trained on visual object and scene classification (ImageNet), or in the human ventral visual pathway, which has long been implicated in the same process. However, in frontoparietal regions previously implicated in intuitive physical reasoning we find both scenario-invariant representations of physical stability, and higher univariate responses to unstable than stable scenes. These results demonstrate abstract representations of physical stability in the dorsal but not ventral pathway, consistent with the hypothesis that the computations underlying stability entail not just pattern classification but forward physical simulation.