{"ID":6620636,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12673","arxiv_id":"2607.12673","title":"Contrasting statistical patterns in melodic and molecular evolution reveal distinctive constraints in a culturally evolving system","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2607.12673","url_pdf":"https://arxiv.org/pdf/2607.12673v1","authors":"[\"John M McBride\",\"W Tecumseh Fitch\"]","published":"2026-07-14T12:03:02Z","proceeding":"q-bio.PE","tasks":"[\"q-bio.PE\",\"cs.SD\",\"physics.soc-ph\"]","methods":"[]","has_code":false}