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Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement

Overview of attention for article published in PLOS ONE, March 2013
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Title
Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement
Published in
PLOS ONE, March 2013
DOI 10.1371/journal.pone.0058960
Pubmed ID
Authors

Katrien Beuls, Luc Steels

Abstract

Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge.

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Geographical breakdown

Country Count As %
Germany 3 3%
Netherlands 2 2%
France 2 2%
United Kingdom 2 2%
Brazil 2 2%
Turkey 1 <1%
New Zealand 1 <1%
Belgium 1 <1%
Spain 1 <1%
Other 2 2%
Unknown 88 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 25%
Researcher 16 15%
Student > Master 15 14%
Student > Bachelor 8 8%
Student > Doctoral Student 6 6%
Other 22 21%
Unknown 12 11%
Readers by discipline Count As %
Computer Science 33 31%
Linguistics 16 15%
Psychology 8 8%
Engineering 6 6%
Philosophy 6 6%
Other 24 23%
Unknown 12 11%