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Language Emergence in Development: A Computational Perspective

IntroductionEmergentist accounts of language typically seek to explain acquisition as being rooted in simple learning mechanisms. Such an approach requires not only that the input to children be rich with cues to the structure of the language being learned, but also that children's learning mechanisms be well-attuned to these cues. Languages themselves must be, in turn, well-tailored to the learner through processes of cultural evolution (e.g., Christiansen & Chater, 2008).While a number of developmental psycholinguistic findings have lent support to such an approach, computational work has allowed researchers to directly uncover a wealth of potentially useful statistical information in corpora of child-directed speech, as well as to test explicit models of language acquisition using representative input. Computational modeling provides a means to directly test the capacity of simple learning mechanisms to give rise to complex linguistic knowledge, and therefore represents a core methodology in emergentist work on language development.Many of the early strides in emergentist modeling of language development were achieved by researchers working within the connectionist framework. In recent years, the computational limitations of connectionist techniques have lead to a shift away from artificial neural networks, towards higher-level models capable of scaling up to deal with input in the form of entire corpora of child-directed speech. A number of such models have made contact with emergentist principles, demonstrating that learning structure from input through general cognitive mechanisms can account for specific developmental patterns in syntactic production (Freudenthal, Pine, & Gobet, 2006, 2007; Gobet, Freudenthal, & Pine, 2004; Jones, Gobet, & Pine, 2000), the acquisition of constructions and construction-like units (Chang, 2008; Solan, 2