The proper treatment of language acquisition and change in a population setting

Language acquisition maps linguistic experience, primary linguistic data (PLD), onto linguistic knowledge, a grammar. Classically, computational models of language acquisition assume a single target grammar and one PLD source, the central question being whether the target grammar can be acquired fro...

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Main Authors: Niyogi, Partha, Berwick, Robert C.
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Language:en_US
Published: National Academy of Sciences 2010
Online Access:http://hdl.handle.net/1721.1/52389
https://orcid.org/0000-0002-1061-1871
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author Niyogi, Partha
Berwick, Robert C.
author2 Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
author_facet Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Niyogi, Partha
Berwick, Robert C.
author_sort Niyogi, Partha
collection MIT
description Language acquisition maps linguistic experience, primary linguistic data (PLD), onto linguistic knowledge, a grammar. Classically, computational models of language acquisition assume a single target grammar and one PLD source, the central question being whether the target grammar can be acquired from the PLD. However, real-world learners confront populations with variation, i.e., multiple target grammars and PLDs. Removing this idealization has inspired a new class of population-based language acquisition models. This paper contrasts 2 such models. In the first, iterated learning (IL), each learner receives PLD from one target grammar but different learners can have different targets. In the second, social learning (SL), each learner receives PLD from possibly multiple targets, e.g., from 2 parents. We demonstrate that these 2 models have radically different evolutionary consequences. The IL model is dynamically deficient in 2 key respects. First, the IL model admits only linear dynamics and so cannot describe phase transitions, attested rapid changes in languages over time. Second, the IL model cannot properly describe the stability of languages over time. In contrast, the SL model leads to nonlinear dynamics, bifurcations, and possibly multiple equilibria and so suffices to model both the case of stable language populations, mixtures of more than 1 language, as well as rapid language change. The 2 models also make distinct, empirically testable predictions about language change. Using historical data, we show that the SL model more faithfully replicates the dynamics of the evolution of Middle English.
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spelling mit-1721.1/523892022-09-28T18:33:01Z The proper treatment of language acquisition and change in a population setting Niyogi, Partha Berwick, Robert C. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Berwick, Robert C. Niyogi, Partha Berwick, Robert C. Language acquisition maps linguistic experience, primary linguistic data (PLD), onto linguistic knowledge, a grammar. Classically, computational models of language acquisition assume a single target grammar and one PLD source, the central question being whether the target grammar can be acquired from the PLD. However, real-world learners confront populations with variation, i.e., multiple target grammars and PLDs. Removing this idealization has inspired a new class of population-based language acquisition models. This paper contrasts 2 such models. In the first, iterated learning (IL), each learner receives PLD from one target grammar but different learners can have different targets. In the second, social learning (SL), each learner receives PLD from possibly multiple targets, e.g., from 2 parents. We demonstrate that these 2 models have radically different evolutionary consequences. The IL model is dynamically deficient in 2 key respects. First, the IL model admits only linear dynamics and so cannot describe phase transitions, attested rapid changes in languages over time. Second, the IL model cannot properly describe the stability of languages over time. In contrast, the SL model leads to nonlinear dynamics, bifurcations, and possibly multiple equilibria and so suffices to model both the case of stable language populations, mixtures of more than 1 language, as well as rapid language change. The 2 models also make distinct, empirically testable predictions about language change. Using historical data, we show that the SL model more faithfully replicates the dynamics of the evolution of Middle English. 2010-03-08T19:01:58Z 2010-03-08T19:01:58Z 2009-06 2008-12 Article http://purl.org/eprint/type/JournalArticle 0027-8424 http://hdl.handle.net/1721.1/52389 Niyogi, Partha, and Robert C. Berwick. “The proper treatment of language acquisition and change in a population setting.” Proceedings of the National Academy of Sciences 106.25 (2009): 10124-10129. ©2009 by the National Academy of Sciences 19497883 https://orcid.org/0000-0002-1061-1871 en_US http://dx.doi.org/10.1073/pnas.0903993106 Proceedings of the National Academy of Sciences of the United States of America Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences PNAS
spellingShingle Niyogi, Partha
Berwick, Robert C.
The proper treatment of language acquisition and change in a population setting
title The proper treatment of language acquisition and change in a population setting
title_full The proper treatment of language acquisition and change in a population setting
title_fullStr The proper treatment of language acquisition and change in a population setting
title_full_unstemmed The proper treatment of language acquisition and change in a population setting
title_short The proper treatment of language acquisition and change in a population setting
title_sort proper treatment of language acquisition and change in a population setting
url http://hdl.handle.net/1721.1/52389
https://orcid.org/0000-0002-1061-1871
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