Cognitive Biases, Linguistic Universals, and Constraint‐Based Grammar Learning
Topics in Cognitive Science 5 (3):392-424 (2013)
Abstract
According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology—the distribution of linguistic patterns across the world's languages—and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial grammar experiments on noun-phrase word order (Culbertson, Smolensky, & Legendre, 2012). Our proposal has several novel properties that distinguish it from prior work in the domains of linguistic theory, computational cognitive science, and machine learning. This study illustrates how ideas from these domains can be synthesized into a model of language learning in which biases range in strength from hard (absolute) to soft (statistical), and in which language-specific and domain-general biases combine to account for data from the macro-level scale of typological distribution to the micro-level scale of learning by individualsAuthor's Profile
DOI
10.1111/tops.12027
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Citations of this work
Harmonic biases in child learners: In support of language universals.Jennifer Culbertson & Elissa L. Newport - 2015 - Cognition 139 (C):71-82.
Simplicity and Specificity in Language: Domain-General Biases Have Domain-Specific Effects.Jennifer Culbertson & Simon Kirby - 2015 - Frontiers in Psychology 6.
Bootstrapping language acquisition.Omri Abend, Tom Kwiatkowski, Nathaniel J. Smith, Sharon Goldwater & Mark Steedman - 2017 - Cognition 164 (C):116-143.
A learning bias for word order harmony: Evidence from speakers of non-harmonic languages.Jennifer Culbertson, Julie Franck, Guillaume Braquet, Magda Barrera Navarro & Inbal Arnon - 2020 - Cognition 204 (C):104392.
Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning.Willem Zuidema, Robert M. French, Raquel G. Alhama, Kevin Ellis, Timothy J. O'Donnell, Tim Sainburg & Timothy Q. Gentner - 2020 - Topics in Cognitive Science 12 (3):925-941.
References found in this work
The myth of language universals: Language diversity and its importance for cognitive science.Nicholas Evans & Stephen C. Levinson - 2009 - Behavioral and Brain Sciences 32 (5):429-448.