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Fractals and artificial intelligence to decrypt ideography and understand the evolution of language

Published online by Cambridge University Press:  02 October 2023

Cédric Sueur Open the ORCID record for Cédric Sueur [Opens in a new window]  and
Marie Pelé Open the ORCID record for Marie Pelé [Opens in a new window]
Cédric Sueur
Affiliation:
Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, France cedric.sueur@iphc.cnrs.fr; https://sites.google.com/site/cedricsueuranimalbehaviour/ Institut Universitaire de France, Paris, France
Marie Pelé
Affiliation:
ANTHROPO-LAB, ETHICS EA7446, Université Catholique de Lille, Lille, France marie.pele@univ-catholille.fr www.ethobiosciences.com
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Abstract

Self-sufficient ideographies are rare because they are stifled by the issue of standardization. Similar issues arise with abstract art or drawings created by young children or great apes. We propose that mathematical indices and artificial intelligence can help us decode ideography, and if not to understand its meaning, at least to know that meaning exists.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e254
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Allen, J. A., Garland, E. C., Dunlop, R. A., & Noad, M. J. (2019). Network analysis reveals underlying syntactic features in a vocally learnt mammalian display, humpback whale song. Proceedings of the Royal Society B: Biological Sciences, 286(1917), 20192014.CrossRefGoogle Scholar
Batagelj, V., Mrvar, A., & Zaversnik, M. (2002). Network analysis of texts. University of Ljubljana, Institute of Mathematics, Physics and Mechanics.Google Scholar
Beltzung, B., Martinet, L., MacIntosh, A., Meyer, X., Hosselet, J., Pele, M., & Sueur, C. (2022a). To draw or not to draw: Understanding the temporal organization of drawing behavior using fractal analyses. Fractals, 2350009. doi:10.1101/2021.08.29.458053.Google Scholar
Beltzung, B., Pelé, M., Renoult, J. P., Shimada, M., & Sueur, C. (2022b). Using artificial intelligence to analyze non-human drawings: A first step with orangutan productions. Animals, 12(20), 2761.CrossRefGoogle ScholarPubMed
Cong, J., & Liu, H. (2014). Approaching human language with complex networks. Physics of Life Reviews, 11(4), 598–618.CrossRefGoogle ScholarPubMed
Jolley, R. P., O'Kelly, R., Barlow, C. M., & Jarrold, C. (2013). Expressive drawing ability in children with autism. British Journal of Developmental Psychology, 31(1), 143–149. doi:10.1111/bjdp.12008CrossRefGoogle ScholarPubMed
Lee, A., & Hobson, R. P. (2006). Drawing self and others: How do children with autism differ from those with learning difficulties? British Journal of Developmental Psychology, 24(3), 547–565. doi:10.1348/026151005X49881CrossRefGoogle Scholar
MacIntosh, A. (2014). The fractal primate: Interdisciplinary science and the math behind the monkey. Primate Research, 30(1), 95–119. doi: 10.2354/psj.30.011.CrossRefGoogle Scholar
Martinet, L., & Pelé, M. (2021). Drawing in nonhuman primates: What we know and what remains to be investigated. Journal of Comparative Psychology, 135(2), 176–184. doi:10.1037/com0000251CrossRefGoogle ScholarPubMed
Martinet, L., Sueur, C., Hirata, S., Hosselet, J., Matsuzawa, T., & Pelé, M. (2021). New indices to characterize drawing behavior in humans (Homo sapiens) and chimpanzees (Pan troglodytes). Scientific Reports, 11(1), 1–14. doi:10.1038/s41598-021-83043-0CrossRefGoogle ScholarPubMed
Pelé, M., Thomas, G., Liénard, A., Eguchi, N., Shimada, M., & Sueur, C. (2021). I wanna draw like you: Inter- and intra-individual differences in orangutan drawings. Animals, 11(11), 11. doi:10.3390/ani11113202CrossRefGoogle ScholarPubMed
Sueur, C., Beltzung, B., & Pelé, M. (2022a). Vers une création picturale non humaine: Quel degré d'auteurisation pour les animaux et les machines? Polygraphe(s), 4, 45–60.Google Scholar
Sueur, C., Martinet, L., Beltzung, B., & Pelé, M. (2022b). Making drawings speak through mathematical metrics. Human Nature. Retrieved from http://arxiv.org/abs/2109.02276CrossRefGoogle ScholarPubMed
Watanabe, S., & Kuczaj, S. (2012). Emotions of animals and humans: Comparative perspectives. Springer Science & Business Media.Google Scholar
Weiss, M., Hultsch, H., Adam, I., Scharff, C., & Kipper, S. (2014). The use of network analysis to study complex animal communication systems: A study on nightingale song. Proceedings of the Royal Society B: Biological Sciences, 281(1785), 20140460.CrossRefGoogle Scholar