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Artificial grammer learning by infants: an auto‐associator perspective
Author(s) -
Sirois Sylvian,
Buckingham David,
Shultz Thomas R.
Publication year - 2000
Publication title -
developmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.801
H-Index - 127
eISSN - 1467-7687
pISSN - 1363-755X
DOI - 10.1111/1467-7687.00138
Subject(s) - habituation , psychology , perspective (graphical) , perception , interpretation (philosophy) , connectionism , artificial neural network , task (project management) , cognitive psychology , cognitive science , artificial intelligence , cognition , computer science , neuroscience , management , economics , programming language
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.