提出生物与人工神经网络的相似性源于生态约束重叠，而非全球统一表征。
The Umwelt Representation Hypothesis: Rethinking Universality
- 系统表征对齐源于发展环境的生态约束重叠，非单一最优解
- 跨物种、个体及模型的表征差异具有系统性且适应性强
- 将模型对比转为生态约束空间中的对齐集群映射
近期研究揭示人工神经网络（ANNs）与生物大脑之间存在显著的表征对齐现象，促使人们提出：所有足够强大的系统会收敛于对现实的统一表征。本文认为这一‘普遍性’主张尚不成熟。我们提出‘环境表征假说’（Umwelt Representation Hypothesis, URH），认为表征对齐并非来自向单一全局最优解的收敛，而是源于系统在发展过程中所处生态约束的重叠。我们回顾了实证证据，表明物种间、个体间及ANN之间的表征差异具有系统性且具适应性，这与普遍性理论难以调和。最后，我们将人工神经网络模型比较重新定义为在生态约束空间中绘制对齐集群的方法，而非寻找单一最优世界模型。
原文摘要 · Abstract (English)
Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. Here, we argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis (URH), proposing that alignment arises not from convergence toward a single global optimum, but from overlap in ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in ecological constraint space rather than searching for a single optimal world model.
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