The emergence of quantifiers

Abstract

Human natural languages use quantifiers as ways to designate the number of objects of a set. They include numerals, such as "three", or circumscriptions, such as "a few". The latter are not only underdetermined but also context dependent. We provide a cultural-evolution explanation for the emergence of such quantifiers, focusing in particular on the role of environmental constraints on strategy choices. Through a series of situated interaction experiments, we show how a community of robotic agents can self-organize a quantification system. Different perceptions of the scene make underdetermined quantifiers useful and environments in which the distribution of objects exhibits some degree of predictability creates favorable conditions for contextdependent quantifiers.

Environmental constraints in the evolution of scalar concepts: Road to most

“…2 If at least one of the players cannot do this using his current strategy, we view this as a failure: the sender has no incentive to use such a (non-discriminative) signal, while the receiver will not understand which of the contexts is the topic. Our game is a simplified version of that considered in Pauw and Hilferty (2012).…”

“…This theoretical stance has gained support in several domains, including spatial descriptions (Levinson, 1996(Levinson, , 2003, color categories (Lindsey & Brown, 2002;Plewczyński et al, 2014), kinship terms (Kemp & Regier, 2012), and constituent order (Christensen, Fusaroli, & Tylén, 2016). Quantifiers, despite their ubiquity in natural language, are less explored from this perspective, with notable exceptions focusing on fuzziness and context-dependence (Pauw & Hilferty, 2012, 2016.…”

Models of language evolution and change

“…Language games have been devised to explore not only the development of shared lexicons as described above, but also the emergence of compositional language, and they are increasingly used to probe the development of specific and more complex grammatical features such as case systems, Aktionsart, quantifiers and grammatical agreement . In all these cases, the desired features emerge because the models implement both functional pressures which make the linguistic structures desirable and useful, and appropriate cognitive mechanisms which allow them to be represented.…”

“…Computational simulations have been the bedrock of language evolution models, from the earliest simulations exploring the biological evolution of symbols 70 and the 'critical period' for language learning, 71,72 to recent simulations investigating the evolution of very specific grammatical features. [73][74][75][76] Many language processing models use simple recurrent networks 77 to explore the conditions under which language can be learnt from experience. This work has had considerable success in showing that language acquisition can be accounted for using just general learning strategies which are not specific to language, and have consequently posed a major challenge to poverty of the stimulus argument and thus nativism.…”

Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning

“…Short after that, a group of Alife researchers started to focus on the origins and emergence of human language-like communication systems through experiments with populations of artificial agents (Smith et al, 2003;Steels, 2003;Wagner et al, 2003). This line of research has shed light on the emergence of spatial terms and categories (Spranger, 2013), case systems (van Trijp, 2012), quantifiers (Pauw and Hilferty, 2012) or syntax (Kirby, 1999;Steels and Casademont, 2015). However, the success of these experiments usually relies on the control of complexity by the experimenter.…”