Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning

Several theoretical proposals for the evolution of language have sparked a renewed search for comparative data on human and non-human animal computational capacities. However, conceptual confusions still hinder the field, leading to experimental evidence that fails to test for comparable human compe...

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Main Authors: Jeffrey Watumull, Marc D. Hauser, Robert C. Berwick
Format: Article
Language:English
Published: PsychOpen GOLD/ Leibniz Institute for Psychology 2014-04-01
Series:Biolinguistics
Subjects:
Online Access:https://doi.org/10.5964/bioling.8995
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author Jeffrey Watumull
Marc D. Hauser
Robert C. Berwick
author_facet Jeffrey Watumull
Marc D. Hauser
Robert C. Berwick
author_sort Jeffrey Watumull
collection DOAJ
description Several theoretical proposals for the evolution of language have sparked a renewed search for comparative data on human and non-human animal computational capacities. However, conceptual confusions still hinder the field, leading to experimental evidence that fails to test for comparable human competences. Here we focus on two conceptual and methodological challenges that affect the field generally: 1) properly characterizing the computational features of the faculty of language in the narrow sense; 2) defining and probing for human language-like computations via artificial language learning experiments in non-human animals. Our intent is to be critical in the service of clarity, in what we agree is an important approach to understanding how language evolved.
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spelling doaj.art-2c8f9a7d78004744b6139f944fe007d72024-01-31T09:42:20ZengPsychOpen GOLD/ Leibniz Institute for PsychologyBiolinguistics1450-34172014-04-01812012910.5964/bioling.89958995Conceptual and Methodological Problems with Comparative Work on Artificial Language LearningJeffrey Watumull0Marc D. HauserRobert C. BerwickUniversity of CambridgeSeveral theoretical proposals for the evolution of language have sparked a renewed search for comparative data on human and non-human animal computational capacities. However, conceptual confusions still hinder the field, leading to experimental evidence that fails to test for comparable human competences. Here we focus on two conceptual and methodological challenges that affect the field generally: 1) properly characterizing the computational features of the faculty of language in the narrow sense; 2) defining and probing for human language-like computations via artificial language learning experiments in non-human animals. Our intent is to be critical in the service of clarity, in what we agree is an important approach to understanding how language evolved.https://doi.org/10.5964/bioling.8995artificial language learningfaculty of language in the narrow senserecursion
spellingShingle Jeffrey Watumull
Marc D. Hauser
Robert C. Berwick
Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning
Biolinguistics
artificial language learning
faculty of language in the narrow sense
recursion
title Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning
title_full Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning
title_fullStr Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning
title_full_unstemmed Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning
title_short Conceptual and Methodological Problems with Comparative Work on Artificial Language Learning
title_sort conceptual and methodological problems with comparative work on artificial language learning
topic artificial language learning
faculty of language in the narrow sense
recursion
url https://doi.org/10.5964/bioling.8995
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