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Publications

Journal Articles

  • Youngblood, M., & Passmore, S. (2026). Simulation-based inference with deep learning shows speed climbers combine innovation and copying to improve performance. Proceedings of the Royal Society B, 293(2062), 20251433. https://doi.org/10.1098/rspb.2025.1433

  • Wascher, C., & Youngblood, M. (2025). Vocal efficiency in crows. Animal Cognition, 28(75). https://doi.org/10.1007/s10071-025-01985-8

  • Whiten, A., & Youngblood, M. (2025). Convergent evolution in whale and human vocal cultures. Science, 387(6734), 581--582. https://doi.org/10.1126/science.adv2318

  • Youngblood, M. (2025). Language-like efficiency in whale communication. Science Advances, 11(6), eads6014. https://doi.org/10.1126/sciadv.ads6014

  • Pitocchelli, J., Albina, A., Bentley, R.A., Guerra, D., & Youngblood, M. (2025). Temporal stability in songs across the breeding range of Geothlypis philadelphia (mourning warbler) may be due to learning fidelity and transmission biases. Ornithology, 142(1), ukae046. https://doi.org/10.1093/ornithology/ukae046

  • Kornreich, A., Partridge, D., Youngblood, M., & Parkins, K. (2024). Rehabilitation outcomes of bird-building collision victims in the Northeastern United States. PLoS ONE, 19(8), e0306362. https://doi.org/10.1371/journal.pone.0306362

  • Sobchuk, O., Youngblood, M., & Morin, O. (2024). First-mover advantage in music. EPJ Data Science, 13(1), 37. https://doi.org/10.1140/epjds/s13688-024-00476-z

  • Youngblood, M. (2024). Language-like efficiency and structure in house finch song. Proceedings of the Royal Society B, 291(2020), 20240250. https://doi.org/10.1098/rspb.2024.0250

  • Youngblood, M., Stubbersfield, J.M., Morin, O., Glassman, R., & Acerbi, A. (2023). Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election. Humanities and Social Sciences Communications, 10(1), 1--11. https://doi.org/10.1057/s41599-023-02106-x

  • Youngblood, M., Miton, H., & Morin, O. (2023). Statistical signals of copying are robust to time-and space-averaging. Evolutionary Human Sciences, 5, e10. https://doi.org/10.1017/ehs.2023.5

  • Benjamin, F.J., Kaaronen, R.O., Moser, C., Rorot, W., Tan, J., Varma, V., Williams, T., & Youngblood, M. (2023). All intelligence is collective intelligence. Journal of Multiscale Neuroscience, 2(1), 169--191. https://doi.org/10.56280/1564736810

  • Youngblood, M., & Lahti, D.C. (2022). Content bias in the cultural evolution of house finch song. Animal Behaviour, 185, 37--48. https://doi.org/10.1016/j.anbehav.2021.12.012

  • Youngblood, M., Baraghith, K., & Savage, P.E. (2021). Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975--1999). Evolution & Human Behavior, 42(6), 573--582. https://doi.org/10.1016/j.evolhumbehav.2021.06.002

  • Kornreich, A., Youngblood, M., Mundinger, P.C., & Lahti, D.C. (2020). Female song can be as long and complex as male song in wild house finches (Haemorhous mexicanus). The Wilson Journal of Ornithology, 132(4), 840--849. https://doi.org/10.1676/19-00126

  • Youngblood, M. (2020). Extremist ideology as a complex contagion: the spread of far-right radicalization in the United States between 2005 and 2017. Humanities & Social Sciences Communications, 7(1), 1--10. https://doi.org/10.1057/s41599-020-00546-3

  • Youngblood, M. (2019). A Raspberry Pi-based, RFID-equipped birdfeeder for the remote monitoring of wild bird populations. Ringing & Migration, 34(1), 25--32. https://doi.org/10.1080/03078698.2019.1759908

  • Youngblood, M. (2019). Conformity bias in the cultural transmission of music sampling traditions. Royal Society Open Science, 6(9), 191149. https://doi.org/10.1098/rsos.191149

  • Youngblood, M. (2019). Cultural transmission modes of music sampling traditions remain stable despite delocalization in the digital age. PLoS ONE, 14(2), e0211860. https://doi.org/10.1371/journal.pone.0211860

  • Youngblood, M., & Lahti, D. (2018). A bibliometric analysis of the interdisciplinary field of cultural evolution. Palgrave Communications, 4(1), 1--9. https://doi.org/10.1057/s41599-018-0175-8

Book Chapters

Preprints

  • Youngblood, M. (2025). chatter: a Python library for applying information theory and AI/ML models to animal communication. arXiv. https://doi.org/10.48550/arXiv.2512.17935

  • Youngblood, M. (2025). Zebra finches transform random songs to exhibit linguistic laws. PsyArXiv. https://doi.org/10.31234/osf.io/m6z23_v1

  • Youngblood, M., Marie, A., & Morin, O. (2025). Status quo conservatism: A theory and a model. SocArXiv. https://osf.io/ngb58

  • Savage, P.E., Ampiah-Bonney, A., Arabadjiev, A., Arhine, A., Ariza, J.F., Bamford, J.S., Barbosa, B.S., Beck, A., Belyk, M., Benetos, E., Bulbulia, J., Cabildo, A., Calhoun, S., Chiba, G., Duran, S.I., Færøvik, U., Fitch, T., Fujii, S., Gabriel, S., Haiduk, F., Hansen, N.C., Hegde, S., Honbolygó, F., Huang, J., Jacoby, N., Jadoul, Y., Jia, Z., Jung, T., Kertész, C., Khasanah, U., Kim, I., Kitayama, Y., Krzyżanowski, W., Kuikuro, U., Kurdova, D., Larrouy-Maestri, P., Leongómez, J.D., Liu, F., Lomsadze, T., Loui, P., Ma, Y., Mcbride, J., Moya, D., Natsitsabui, R., Novembre, G., Nweke, F.E., Opondo, P., Ozaki, Y., Parkinson, H., Parselelo, M.L., Pavlovich, D., Pfordresher, P., Pisanski, K., Podlipniak, P., Popescu, T., Proutskova, P., Purdy, S., Ravignani, A., Raviv, L., Sadaphal, D.P., Shakya, S.R., Shilton, D., Silva-Zurita, J., Soto-Silva, I., Tarr, B., Tierney, A., Tiratanti, P., Trainor, L., Der Nederlanden, C., Varella, M.A.C., Varnosfaderani, S.D., Youngblood, M., & Zariquiey, R. (2024). Does singing enhance cooperation more than speaking does? A global experimental Stage 1 Registered Report. PsyArXiv. https://doi.org/10.31234/osf.io/pv3m9

  • GomezdelaTorre Clavel, M.G., Youngblood, M., & Lahti, D. (2020). Relationship between personality and cognitive traits in domestic rabbits (Oryctolagus cuniculus). bioRxiv. https://doi.org/10.1101/2020.10.12.336024