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      Modeling Human Morphological Competence

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          Abstract

          One of the central debates in the cognitive science of language has revolved around the nature of human linguistic competence. Whether syntactic competence should be characterized by abstract hierarchical structures or reduced to surface linear strings has been actively debated, but the nature of morphological competence has been insufficiently appreciated despite the parallel question in the cognitive science literature. In this paper, in order to investigate whether morphological competence should be characterized by abstract hierarchical structures, we conducted a crowdsourced acceptability judgment experiment on morphologically complex words and evaluated five computational models of morphological competence against human acceptability judgments: Character Markov Models (Character), Syllable Markov Models (Syllable), Morpheme Markov Models (Morpheme), Hidden Markov Models (HMM), and Probabilistic Context-Free Grammars (PCFG). Our psycholinguistic experimentation and computational modeling demonstrated that “morphous” computational models with morpheme units outperformed “amorphous” computational models without morpheme units and, importantly, PCFG with hierarchical structures most accurately explained human acceptability judgments on several evaluation metrics, especially for morphologically complex words with nested morphological structures. Those results strongly suggest that human morphological competence should be characterized by abstract hierarchical structures internally generated by the grammar, not reduced to surface linear strings externally attested in large corpora.

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          Most cited references 49

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                12 November 2020
                2020
                : 11
                Affiliations
                1Faculty of Science & Engineering, Waseda University , Tokyo, Japan
                2Department of Linguistics, New York University , New York, NY, United States
                3Department of Psychology, New York University , New York, NY, United States
                4NYU Abu Dhabi Institute, New York University , Abu Dhabi, United Arab Emirates
                Author notes

                Edited by: Viviane Marie Deprez, Centre National de la Recherche Scientifique (CNRS), France

                Reviewed by: Cristiano Chesi, University Institute of Higher Studies in Pavia, Italy; Naoki Fukui, Sophia University, Japan

                *Correspondence: Yohei Oseki yohei.oseki@ 123456nyu.edu

                This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2020.513740
                7688581
                Copyright © 2020 Oseki and Marantz.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 3, Tables: 2, Equations: 4, References: 49, Pages: 12, Words: 8074
                Funding
                Funded by: Japan Society for the Promotion of Science 10.13039/501100001691
                Award ID: 18H05589
                Award ID: 19H04990
                Funded by: New York University Abu Dhabi 10.13039/100012025
                Award ID: G1001
                Categories
                Psychology
                Original Research

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