Journal
TRENDS IN COGNITIVE SCIENCES
Volume 8, Issue 10, Pages 451-456Publisher
CELL PRESS
DOI: 10.1016/j.tics.2004.08.006
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Recent demonstrations of statistical learning in infants have reinvigorated the innateness versus learning debate in language acquisition. This article addresses these issues from both computational and developmental perspectives. First, I argue that statistical learning using transitional probabilities cannot reliably segment words when scaled to a realistic setting (e.g. child-directed English). To be successful, it must be constrained by knowledge of phonological structure. Then, turning to the bona fide theory of innateness - the Principles and Parameters framework - I argue that a full explanation of children's grammar development must abandon the domain-specific learning model of triggering, in favor of probabilistic learning mechanisms that might be domain-general but nevertheless operate in the domain-specific space of syntactic parameters.
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