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.
%0 Journal Article
%1 Yang2004
%A Yang, Charles D.
%D 2004
%J Trends in Cognitive Sciences
%K acquisition,ling832,linguistics,syntax
%N 10
%P 451--456
%R http://dx.doi.org/10.1016/j.tics.2004.08.006
%T Universal Grammar, statistics or both?
%V 8
%X 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.
@article{Yang2004,
abstract = {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.},
added-at = {2011-03-27T17:20:41.000+0200},
author = {Yang, Charles D.},
biburl = {https://www.bibsonomy.org/bibtex/21993d17dca3f6e63d469e1da97ea426e/yevb0},
doi = {http://dx.doi.org/10.1016/j.tics.2004.08.006},
interhash = {895840313a47e008d494c378f017216e},
intrahash = {1993d17dca3f6e63d469e1da97ea426e},
journal = {Trends in Cognitive Sciences},
keywords = {acquisition,ling832,linguistics,syntax},
mendeley-tags = {acquisition,ling832,linguistics,syntax},
number = 10,
pages = {451--456},
timestamp = {2011-03-27T17:21:14.000+0200},
title = {Universal Grammar, statistics or both?},
type = {Journal article},
volume = 8,
year = 2004
}