发表机构
Department of Behavioral and Cognitive Biology, University of Vienna（维也纳大学行为与认知生物学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究通过开发节奏感知比对方法，将其应用于爱尔兰舞蹈曲调变体，利用四项生物信息学分析揭示旋律与分子进化不同模式，展现生物信息学为文化进化研究提供强大框架及大规模分析音乐文化传播的方法。
AI 中文摘要
进化序列可用于推断进化规则。口头传承的民间旋律是进化序列，其与蛋白质序列（一维，由有限字母组成）的相似性促使应用生物信息学方法研究文化进化。主要障碍是旋律编码节奏，这打破了标准序列比对算法的一些假设。我们开发了一种节奏感知比对方法并将其应用于40000个爱尔兰舞蹈曲调变体，实现了首次大规模自动化旋律比对。四项典型的生物信息学分析——变异性、替换矩阵、位置保守性和协方差——揭示了与分子进化不同的模式，揭示了塑造每个领域的力量：蛋白质的生化和生物物理限制；旋律的记忆、运动和社会偏差。结果共同表明，生物信息学为研究文化进化提供了一个强大的框架——在概念和算法方面都是如此。尽管音乐的文化传播已经讨论了几个世纪，但我们在此展示了如何进行大规模分析。
英文摘要
Evolved sequences can be used to infer the rules of evolution. Orally transmitted folk melodies are evolved sequences whose similarity to protein sequences (one-dimensional, drawn from a limited alphabet) invites application of bioinformatics methods to study cultural evolution. A major obstacle is that melodies encode rhythm, which breaks some assumptions of standard sequence-alignment algorithms. We develop a rhythm-aware alignment method and apply it to \num{40000} Irish dance tune variants, enabling the first large-scale automated melodic alignment. Four canonical bioinformatics analyses -- mutability, substitution matrices, positional conservation, and covariance -- reveal patterns distinct from those of molecular evolution, revealing the forces that shape each domain: biochemical and biophysical constraints for proteins; memory, motor, and social biases for melodies. Together the results show that bioinformatics provides a powerful framework -- conceptual as much as algorithmic -- for studying cultural evolution. Although the cultural transmission of music has been discussed for centuries, here we show how to analyze it at large scale.
Comments13 pages, 3 figures, 12 extra pages of supplementary information
