Shultz, T. R. (2005). Generalization in a
model of infant sensitivity to syntactic variation. Proceedings of the Twenty-seventh Annual
Conference of the Cognitive Science Society (pp. 2009-2014).
Computer simulations show that an unstructured neural-network model (Shultz & Bale, 2001) covers the essential features of infant differentiation of simple grammars in an artificial language, and generalizes by both extrapolation and interpolation. Other simulations (Vilcu & Hadley, 2003) claiming to show that this model did not really learn these grammars were flawed by confounding syntactic patterns with other factors and by lack of statistical significance testing. Thus, this model remains a viable account of infant ability to learn and discriminate simple syntactic structures.
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