用计算机模拟进化，揭示环境如何塑造眼睛形态与视觉能力。
What if Eye...? Computationally Recreating Vision Evolution
- 构建具身智能体，同步演化眼睛结构与神经处理机制。
- 任务不同催生不同眼型：导航促发复眼，识别任务催生高精度相机眼。
- 发现视觉敏锐度与神经处理能力的系统性演化规律，适合生物启发设计。
自然界中的视觉系统展现出惊人多样性，从简单的光敏感区域到带有透镜的复杂相机眼。尽管自然选择通过数百万年的突变造就了这些眼睛，但它们仅是演化路径中的一小部分。由于无法实验分离单一影响因素，验证环境压力如何塑造眼进化仍具挑战。计算进化提供了一种系统探索替代路径的方法。本文通过一个协同演化物理眼结构与神经处理的具身智能体框架，揭示环境需求如何驱动视觉演化的三大核心方面：首先，任务特异性选择导致眼睛分化——迷宫导航任务催生分布式复眼，而物体识别任务则促进高分辨率相机眼的出现；其次，光学创新如透镜自然涌现，以解决集光能力与空间精度间的根本矛盾；第三，发现视觉敏锐度与神经处理能力间存在系统性标度律，表明任务复杂度推动感觉与计算能力的协同进化。本研究提出新范式，通过设计单人游戏让具身智能体同时演化视觉系统并学习复杂行为。借助统一的遗传编码框架，这些智能体成为下一代假说验证工具，并为可制造的仿生视觉系统奠定基础。网站：http://eyes.mit.edu/
原文摘要 · Abstract (English)
Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through countless mutations over millions of years, they represent just one set of realized evolutionary paths. Testing hypotheses about how environmental pressures shaped eye evolution remains challenging since we cannot experimentally isolate individual factors. Computational evolution offers a way to systematically explore alternative trajectories. Here we show how environmental demands drive three fundamental aspects of visual evolution through an artificial evolution framework that co-evolves both physical eye structure and neural processing in embodied agents. First, we demonstrate computational evidence that task specific selection drives bifurcation in eye evolution - orientation tasks like navigation in a maze leads to distributed compound-type eyes while an object discrimination task leads to the emergence of high-acuity camera-type eyes. Second, we reveal how optical innovations like lenses naturally emerge to resolve fundamental tradeoffs between light collection and spatial precision. Third, we uncover systematic scaling laws between visual acuity and neural processing, showing how task complexity drives coordinated evolution of sensory and computational capabilities. Our work introduces a novel paradigm that illuminates evolutionary principles shaping vision by creating targeted single-player games where embodied agents must simultaneously evolve visual systems and learn complex behaviors. Through our unified genetic encoding framework, these embodied agents serve as next-generation hypothesis testing machines while providing a foundation for designing manufacturable bio-inspired vision systems. Website: http://eyes.mit.edu/
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