Artificial grammer learning by infants: an auto-associator perspective Export

@article{citeulike:445726, abstract = { This paper reviews a recent article suggesting that infants use a system of algebraic rules to learn an artificial grammar (Marcus, Vijayan, Bandi Rao \& Vishton, Rule learning by seven-month-old infants. Science, 183 (1999), 77\&\#150;80). In three reported experiments, infants exhibited increased responding to auditory strings that violated the pattern of elements they were habituated to. We argue that a perceptual interpretation is more parsimonious, as well as more consistent with a broad array of habituation data, and we report successful neural network simulations that implement this lower-level interpretation. In the discussion, we discuss how our model relates to other habituation research, and how it compares to other neural network models of habituation in general, and models of the Marcus et al. (1999) task specifically.}, author = {Sirois and Buckingham, D. and Shultz, T. R.}, citeulike-article-id = {445726}, citeulike-linkout-0 = {http://www.ingentaconnect.com/content/bpl/desc/2000/00000003/00000004/art00138}, journal = {Developmental Science}, keywords = {artificial\_grammar, connectionist, language\_acquisition, learning, statistical\_learning, syntax}, month = {November}, number = {4}, pages = {442--456}, posted-at = {2006-02-24 14:42:10}, priority = {2}, title = {Artificial grammer learning by infants: an auto-associator perspective}, url = {http://www.ingentaconnect.com/content/bpl/desc/2000/00000003/00000004/art00138}, volume = {3}, year = {2000} }

TY - JOUR ID - citeulike:445726 L3 - citeulike-article-id:445726 N2 - This paper reviews a recent article suggesting that infants use a system of algebraic rules to learn an artificial grammar (Marcus, Vijayan, Bandi Rao & Vishton, Rule learning by seven-month-old infants. Science, 183 (1999), 77–80). In three reported experiments, infants exhibited increased responding to auditory strings that violated the pattern of elements they were habituated to. We argue that a perceptual interpretation is more parsimonious, as well as more consistent with a broad array of habituation data, and we report successful neural network simulations that implement this lower-level interpretation. In the discussion, we discuss how our model relates to other habituation research, and how it compares to other neural network models of habituation in general, and models of the Marcus et al. (1999) task specifically. IS - 4 JF - Developmental Science EP - 456 TI - Artificial grammer learning by infants: an auto-associator perspective VL - 3 SP - 442 KW - artificial_grammar KW - connectionist KW - language_acquisition KW - learning KW - statistical_learning KW - syntax AU - Sirois AU - Buckingham, D AU - Shultz, TR PY - 2000/11// UR - http://www.ingentaconnect.com/content/bpl/desc/2000/00000003/00000004/art00138 ER -