This paper describes computer simulations that investigate the role of self-organisation in explaining the universals of human vowel systems. It has been observed that human vowel systems show remarkable regularities, and that these regularities optimise acoustic distinctiveness and are therefore adaptive for good communication. Traditionally, universals have been explained as the result of innate properties of the human language faculty, and therefore need an evolutionary explanation. In this paper it is argued that the regularities emerge as the result of self-organisation in a population and therefore need not be the result of biological evolution.
The hypothesis is investigated with two different computer simulations that are based on a population of agents that try to imitate each other as well as possible. Each agent can produce and perceive vowels in a human-like way and stores vowels as articulatory and acoustic prototypes. The aim of the agents is to imitate each other as well as possible.
It will be shown that successful repertoires of vowels emerge that show the same regularities as human vowel systems.
Miranda, Eduardo Reck, Richie Yeung, Anna Pearson, Konstantinos Meichanetzidis & Bob Coecke
2022. A Quantum Natural Language Processing Approach to Musical Intelligence. In Quantum Computer Music, ► pp. 313 ff.
Burridge, J. & B. Vaux
2020. Brownian dynamics for the vowel sounds of human language. Physical Review Research 2:1
Tamariz, Mónica
2014. Experiments and Simulations Can Inform Evolutionary Theories of the Cultural Evolution of Language. In The Evolution of Social Communication in Primates [Interdisciplinary Evolution Research, 1], ► pp. 249 ff.
2013. Language Dynamics in the Framework of Complex Networks: A Case Study on Self-Organization of the Consonant Inventories. In Cognitive Aspects of Computational Language Acquisition [Theory and Applications of Natural Language Processing, ], ► pp. 51 ff.
Dowman, Mike
2007. Explaining Color Term Typology With an Evolutionary Model. Cognitive Science 31:1 ► pp. 99 ff.
Dowman, Mike
2007. Explaining Color Term Typology With an Evolutionary Model. Cognitive Science: A Multidisciplinary Journal 30:1 ► pp. 99 ff.
Martins, João M. & Eduardo R. Miranda
2006. A Connectionist Architecture for the Evolution of Rhythms. In Applications of Evolutionary Computing [Lecture Notes in Computer Science, 3907], ► pp. 696 ff.
Dowman, M.
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