Computational and evolutionary aspects of language

By Martin A. Nowak, N. Komarova, Partha Niyogi. Nature, 2002

412 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Cultural Studies

View Paper: https://www.semanticscholar.org/paper/92b2cee3504bded7bf1373172db264b527c74c5f

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Citations in the corpus

Language Evolution by Iterated Learning With Bayesian Agents (2007, background): “…via a number of avenues (Briscoe, 2002), including simulation of language learners (Kirby, 2001; Brighton, 2002; Smith et al., 2003) and use of formal models of population dynamics (Niyogi & Berwick, 1995, 1997a,b; Nowak, Plotkin, & Jansen, 2000; Komarova et al., 2001; Nowak et al., 2001, 2002).”
Language Is a Complex Adaptive System: Position Paper (2009, methodology): “…population of linguistic utterances is therefore not connected in a simple way to that of the underlying human population; hence, the role of natural selection in shaping languages is likely to be diminished compared to what has sometimes been assumed elsewhere (Croft, 2002; Nowak et al., 2002).”
Iterated learning and the evolution of language. (2014, positive): “The potential of these mathematical tools was demonstrated in a series of papers by Nowak and colleagues [29, 30, 31, 32], who showed how one of the basic models of biological evolution – the replicator dynamics – could be modified to capture aspects of language evolution.”
Language as a Complex Adaptive System (2000, background): “But conversely, success in the language game influences the score of the categories used, so the categorisation process becomes tuned to be better adapted to the language in the environment of the agents.”
Toward physics of the mind: Concepts, emotions, consciousness, and symbols (2006, background): “Is it possible to prove these assertions? Current research in evolution of languages and cultures uses mathematical simulations of communities of interacting agents [96] and related statistical models [100,101].”
On the role of locality in learning stress patterns* (2009, background): “Nowak et al. (2002) provide an accessible overview of this learning framework and de la Higuera (in press) provides an excellent in-depth introduction to this learning framework and others (see Jain et al. 1999 and Niyogi 2006 for more detailed presentations).”
Towards the Quantification of the Semantic Information Encoded in Written Language (2009, background): “Language has developed following the laws of natural evolution [14, 18, 19] with the functional goal of encoding and transmitting information between humans.”
Intuitive theories as grammars for causal inference (2007, background): “Just as the grammar of a language generates a constrained space of syntactic structures that could serve as hypotheses for parsing in that language, the principles of UG could be said to generate a highly constrained space of possible grammars for all human languages, thereby enabling grammar acquis”