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.
Abstracts, papers, chapters, and other documents are posted on this site as an efficient way to distribute reprints. The respective authors and publishers of these works retain all of the copyrights to this material. Anyone copying, downloading, bookmarking, or printing any of these materials agrees to comply with all of the copyright terms. Other than having an electronic or printed copy for fair personal use, none of these works may be reposted, reprinted, or redistributed without the explicit permission of the relevant copyright holders.
To obtain a PDF reprint of this particular article, signal your agreement with these copyright terms by clicking on the statement below.