Language evolution Lab
The Language Evolution LAB is oriented to the analysis of the Origin of Evolution of Language studying the cognitive mechanisms, interaction patterns, and collective dynamics that could explain how grammatical structures arise in human language by building agent-based models and using the hypothesis that self-organization and (linguistic) selection are the primary driving forces.
The research is based on the hypothesis that language is a complex adaptive system that emerges through adaptive interactions between agents and continues to evolve in order to remain adapted to the needs and capabilities of the agents. This research has been implemented in fluid construction grammar (FCG), a formalism for construction grammars that has been specially designed for the origins and evolution of language. The approach of computational modeling and the use of virtual and robotic agents grounded in real life is claimed to be theory independent. It enables the researcher to find out exactly what cognitive capacities are needed for certain language phenomena to emerge. It also focuses the researcher in formulating hypotheses in a precise and exact manner, whereas theoretical models often stay very vague.
Steels L. 2016. I’m gonna have to science the shit out of this. Comment on “Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain” by Michael A. Arbib. Physics of Life Reviews, 16:96-98. DOI:10.1016/j.plrev.2016.02.002
Steels L. 2016. Agent-based models for the emergence and evolution of grammar. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1701). DOI:10.1098/rstb.2015.0447
Steels L, Szathmary E. 2016. Fluid Construction Grammar as a Biological System. Linguistics Vanguard 2(1): 20150022.
Hanappe P, Dunlop R, Maes A, Steels L, Duval N. 2016. Agroecology: A Fertile Field for Human Computation. Human Computation, 3(1):225-233. DOI:10.15346/hc.v3i1.13
Garcia-Casademont E, Steels L. 2016. Insight grammar learning. Journal of Cognitive Science, 17(1):27-62. DOI:10.17791/jcs.2016.17.1.27