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Morphological Convergence as on-Line Lexical Analogy

Published online by Cambridge University Press:  01 January 2026

Péter Rácz ,
Clay Beckner ,
Jennifer B. Hay  and
Janet B. Pierrehumbert
Péter Rácz*
Affiliation:
Central European University and University of Canterbury
Clay Beckner*
Affiliation:
University of Warwick and University of Canterbury
Jennifer B. Hay*
Affiliation:
University of Canterbury
Janet B. Pierrehumbert*
Affiliation:
University of Oxford and University of Canterbury
*
[raczp@ceu.edu]
[clay.beckner@warwick.ac.uk]
[jen.hay@canterbury.ac.nz]
[janet.pierrehumbert@oerc.ox.ac.uk]
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Abstract

The English past tense contains pockets of variation, where regular and irregular forms compete (e.g. learned/learnt, weaved/wove). Individuals vary considerably in the degree to which they prefer irregular forms. This article examines the degree to which individuals may converge on their regularization patterns and preferences. We report on a novel experimental methodology, using a cooperative game involving nonce verbs. Analysis of participants' postgame responses indicates that their behavior shifted in response to an automated co-player's preferences, on two dimensions. First, players regularize more after playing with peers with high regularization rates, and less after playing with peers with low regularization rates. Second, players' overall patterns of regularization are also affected by the particular distribution of (ir)regular forms produced by the peer.

We model the effects of the exposure on participants' morphological preferences, using both a rule-based model and an instance-based analogical model (Nosofsky 1988, Albright & Hayes 2003). Both models contribute separately and significantly to explaining participants' pre-exposure regularization processes. However, only the instance-based model captures the shift in preferences that arises after exposure to the peer. We argue that the results suggest an account of morphological convergence in which new word forms are stored in memory, and on-line generalizations are formed over these instances.

Keywords

morphologyconvergencecomputational modelinglanguage variation and changegeneralized context modelminimal generalization learner

Information

Type
Research Article
Information
Language , Volume 96 , Issue 4 , December 2020 , pp. 735 - 770
Copyright
Copyright © 2020 Linguistic Society of America

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Footnotes

*

This work was supported by a grant from the John Templeton Foundation (Award ID 36617) to JBP and JH, and a Royal Society of New Zealand Rutherford Discovery Fellowship (Grant No. E5909) to JH. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the funding agencies. The authors would like to thank Lisa Dawdy-Hesterberg, Chun Liang Chan, Robert Fromont, Adam Albright, Pat LaShell, Jacqui Nokes, Jacq Jones, Ryan Podlubny, our steadfast associate editors Linda Wheeldon and Megan Crowhurst, Marc Brysbaert, Volya Kapatsinski, and three anonymous referees. All faults remain ours.

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