Cultural evolution via iterated learning and communication explains efficient color naming systems (2305.10154v2)
Abstract: It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient.
- (2016). Focal colors across languages are representative members of color categories. Proceedings of the National Academy of Sciences, 113, 11178 - 11183.
- (2010). Modeling the emergence of universality in color naming patterns. Proceedings of the National Academy of Sciences, 107(6), 2403-2407.
- (2005). Explaining universal color categories through a constrained acquisition process. Adaptive Behavior, 13(4), 293-310.
- (2020). Simplicity and informativeness in semantic category systems. Cognition, 202, 104289.
- (2015). Language evolution in the lab tends toward informative communication. In Proceedings of the 37th Annual Meeting of the Cognitive Science Society.
- (2021). Communicating artificial neural networks develop efficient color-naming systems. Proceedings of the National Academy of Sciences, 118, e2016569118.
- (2005). The World Color Survey database: History and use. In H. Cohen C. Lefebvre (Eds.), Handbook of categorization in cognitive science (p. 223-241). Amsterdam: Elsevier.
- (2022). Indefinite pronouns optimize the simplicity/informativeness trade-off. Cognitive Science, 46(5), e13142.
- (2024). Recursive numeral systems optimize the trade-off between lexicon size and average morphosyntactic complexity. Cognitive Science, 48(3), e13424.
- Dowman, M. (2007). Explaining color term typology with an evolutionary model. Cognitive Science, 31(1), 99-132.
- Gärdenfors, P. (2000). Conceptual spaces: The geometry of thought. MIT Press, 3, 16.
- (2009). Why some spatial semantic categories are harder to learn than others: The typological prevalence hypothesis. In Crosslinguistic approaches to the psychology of language: Research in the tradition of Dan Isaac Slobin (pp. 465–480). Hove, UK: Psychology Press.
- (2022). Communicative efficiency or iconic learning: Do acquisition and communicative pressures interact to shape colour-naming systems? Entropy, 24(11). doi: 10.3390/e24111542
- Hunter, J. D. (2007). Matplotlib: A 2d graphics environment. Computing in Science & Engineering, 9(3), 90–95. doi: 10.1109/MCSE.2007.55
- (2022). Modal semantic universals optimize the simplicity/informativeness trade-off. In Proceedings of SALT 32 (Semantics and Linguistic Theory) (p. 227-248).
- Jäger, G. (2010). Natural color categories are convex sets. In M. Aloni, H. Bastiaanse, T. de Jager, K. Schulz (Eds.), Logic, language and meaning (pp. 11–20). Berlin, Heidelberg: Springer Berlin Heidelberg.
- (2007). Language structure: Psychological and social constraints. Synthese, 159(1), 99–130.
- (2009, 07). Evolutionary models of color categorization i population categorization systems based on normal and dichromat observers. Journal of the Optical Society of America. A, Optics, image science, and vision, 26, 1414-23.
- (2019). Season naming and the local environment. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society.
- (2012). Kinship categories across languages reflect general communicative principles. Science, 336, 1049-54.
- (2015). Adam: A method for stochastic optimization. 3rd International Conference for Learning Representations.
- Kirby, S. (2001). Spontaneous evolution of linguistic structure - an iterated learning model of the emergence of regularity and irregularity. IEEE Transactions on Evolutionary Computation, 5, 102 - 110.
- (2015). Compression and communication in the cultural evolution of linguistic structure. Cognition, 141, 87-102.
- (2020). A reinforcement-learning approach to efficient communication. PLoS ONE, 15(7), 1–26.
- Levinson, S. C. (2012). Kinship and human thought. Science, 336(6084), 988-989.
- (2010). Data structures for statistical computing in python. In Scipy (Vol. 445, pp. 51–56).
- (2021). The forms and meanings of grammatical markers support efficient communication. Proceedings of the National Academy of Sciences, 118(49).
- (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
- (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
- (2007). Color naming reflects optimal partitions of color space. Proceedings of the National Academy of Sciences of the United States of America, 104(4), 1436–1441.
- (2020). Compositional languages emerge in a neural iterated learning model. In International Conference on Learning Representations.
- Rosch, E. (1978). Principles of categorization. In E. Rosch B. B. Lloyd (Eds.), Cognition and categorization (p. 27-48). New York: Lawrence Erlbaum Associates.
- Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. Retrieved from https://www.sciencedirect.com/science/article/pii/0377042787901257 doi: https://doi.org/10.1016/0377-0427(87)90125-7
- (2010). Language evolution in the laboratory. Trends in Cognitive Sciences, 14(9), 411-417.
- (2003, 02). Iterated learning: A framework for the emergence of language. Artificial life, 9, 371-86.
- (2005). Coordinating perceptually grounded categories through language: A case study for colour. Behavioral and brain sciences, 28(4), 469–488.
- (2020). Ease of learning explains semantic universals. Cognition, 195, 104076.
- (1999). The information bottleneck method. In Proceedings of the 37th Allerton Conference on Communication, Control and Computation (p. 368–377).
- (2022). Trading off utility, informativeness, and complexity in emergent communication. In A. H. Oh, A. Agarwal, D. Belgrave, K. Cho (Eds.), Advances in Neural Information Processing Systems.
- (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261–272. doi: 10.1038/s41592-019-0686-2
- von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395–416.
- Waskom, M. L. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. Retrieved from https://doi.org/10.21105/joss.03021 doi: 10.21105/joss.03021
- Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3), 229–256.
- (2013). Cultural transmission results in convergence towards colour term universals. Proceedings of the Royal Society B: Biological Sciences, 280(1758), 20123073.
- (2020). Numeral systems across languages support efficient communication: From approximate numerosity to recursion. Open Mind, 4, 57–70.
- (2016). Historical semantic chaining and efficient communication: The case of container names. Cognitive Science, 40, 2081-2094.
- (2022). The evolution of color naming reflects pressure for efficiency: Evidence from the recent past. Journal of Language Evolution.
- (2018). Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences of the United States of America, 115(31), 7937–7942.
- (2021). Let’s talk (efficiently) about us: Person systems achieve near-optimal compression. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society.
- (2019). Semantic categories of artifacts and animals reflect efficient coding. In 41st Annual conference of the Cognitive Science Society.
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