标题： 大脑与人工智能之间的对齐：跨模态、尺度和训练轨迹的趋同进化证据 显示英文标题

标题： Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories

摘要： 人工和生物系统可能在架构和学习机制存在根本差异的情况下，进化出相似的计算解决方案——这是一种趋同进化形式。 我们通过大规模分析人类大脑活动与超过600个跨越语言和视觉领域的AI模型内部表示之间的对齐情况来证明这一现象，这些模型的参数量从133万到720亿不等。 分析6000万次对齐测量结果表明，高性能模型在没有显式神经约束的情况下自发地发展出更强的大脑对齐能力，其中语言模型表现出明显更强的相关性（r=0.89，p<7.5e-13），而视觉模型的相关性较弱（r=0.53，p<2.0e-44）。 关键的是，纵向分析表明，在训练过程中，大脑对齐始终先于性能提升，这表明开发类似大脑的表示可能是迈向更高能力的必要步骤。 我们发现存在系统性模式：语言模型与边缘系统和整合区域的对齐最强，而视觉模型则逐步与视觉皮层对齐；更深层次的处理层在不同模态之间趋于一致；随着表示规模的增加，对齐系统性地从初级感觉区域转移到更高阶的关联区域。 这些发现提供了有力的证据，表明针对任务性能的优化自然会引导AI系统走向类似大脑的计算策略，这不仅为智能信息处理的原则提供了基本见解，也为开发更强大的AI系统提供了实际指导。 显示英文摘要

摘要： Artificial and biological systems may evolve similar computational solutions despite fundamental differences in architecture and learning mechanisms -- a form of convergent evolution. We demonstrate this phenomenon through large-scale analysis of alignment between human brain activity and internal representations of over 600 AI models spanning language and vision domains, from 1.33M to 72B parameters. Analyzing 60 million alignment measurements reveals that higher-performing models spontaneously develop stronger brain alignment without explicit neural constraints, with language models showing markedly stronger correlation (r=0.89, p<7.5e-13) than vision models (r=0.53, p<2.0e-44). Crucially, longitudinal analysis demonstrates that brain alignment consistently precedes performance improvements during training, suggesting that developing brain-like representations may be a necessary stepping stone toward higher capabilities. We find systematic patterns: language models exhibit strongest alignment with limbic and integrative regions, while vision models show progressive alignment with visual cortices; deeper processing layers converge across modalities; and as representational scale increases, alignment systematically shifts from primary sensory to higher-order associative regions. These findings provide compelling evidence that optimization for task performance naturally drives AI systems toward brain-like computational strategies, offering both fundamental insights into principles of intelligent information processing and practical guidance for developing more capable AI systems.