Abstract
Human physical reasoning relies on internal "body" representations - coarse, volumetric approximations that capture an object's extent and support intuitive predictions about motion and physics. While psychophysical evidence suggests humans use such coarse representations, their internal structure remains largely unknown. Here we test whether vision models trained for segmentation develop comparable representations. We adapt a psychophysical experiment conducted with 50 human participants to a semantic segmentation task and test a family of seven segmentation networks, varying in size. We find that smaller models naturally form human-like coarse body representations, whereas larger models tend toward overly detailed, fine-grain encodings. Our results demonstrate that coarse representations can emerge under limited computational resources, and that machine representations can provide a scalable path toward understanding the structure of physical reasoning in the brain.
Abstract (translated)
人类的物理推理依赖于内部“身体”表示形式——这是一种粗糙、体积化的近似，能够捕捉到物体的范围，并支持对运动和物理现象的直观预测。虽然心理物理学证据表明人类使用这种粗略的表示方法，但其内在结构仍然很大程度上未知。在此研究中，我们测试了视觉模型在训练分割任务时是否发展出了类似的表示形式。我们将一项与50名人类参与者进行的心理物理学实验改编为一个语义分割任务，并对七种不同大小的分割网络进行了测试。我们发现较小规模的模型自然地形成了类似人体粗略的身体表示，而较大规模的模型则倾向于产生过于详细、细粒度的编码。我们的研究结果表明，在有限计算资源下可以出现粗糙的表示形式，并且机器表示为理解大脑物理推理结构提供了一条可扩展路径。
URL
https://arxiv.org/abs/2512.00365
