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Agreeing when to disagree: A corpus analysis of variable agreement in caregiver and child English

Published online by Cambridge University Press:  02 May 2023

Cynthia Lukyanenko*
Affiliation:
George Mason University, USA
Karen Miller
Affiliation:
Penn State University, USA
*
*Corresponding author: Cynthia Lukyanenko. Email: clukyane@gmu.edu
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Abstract

We characterized the patterns of agreement variation and consistency in three corpora of child and child-directed US English to better understand preschoolers’ input and to compare preschoolers’ own agreement production. We examined sentences with third-person subjects and tensed forms of be in two large single-family corpora and one cross-sectional corpus collected during a Search-and-Find activity. Caregivers’ agreement variation consistently reflected patterns previously found in adult-to-adult speech. Children's variation was conditioned by many of the same factors (e.g., sentence type, pronoun subject, and order of subject and verb) and clearly demonstrated acquisition of the categorical-variable split. However, some children showed substantially higher rates of nonagreeing forms (There's the cherries) than their caregivers and differed in their ranking of conditioning factors. We suggest that this reflects children's developing production processing abilities: shorter sentence-planning spans may make nonagreement a useful strategy for avoiding early number commitments in verb-first sentences.

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Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press
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Table 1. Sample sentences with plural subjects, divided by sentence type, order, and subject type

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Figure 1. Percentage plural verb forms in sentences with plural subjects, split by corpus, speaker, and order of subject and verb on the horizontal axis, and by subject type and sentence type on the vertical axis. Number of plural verb tokens and total tokens are shown below percentages (plural/total). The darker the cell, the lower the rate of plural agreement. Blank cells indicate combinations of factors that did not occur.

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Table 2. Results from the generalized linear models of verb form in sentences with plural subjects for the Nina (n = 2632) and Sarah (n = 615) corpora

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Figure 2. Best conditional inference tree for Nina's corpus. Node labels indicate the splitting factor and the edge labels indicate the levels of that factor in each branch. Each terminal node shows the number of tokens it contains (n) and the proportion of those tokens that have plural (light gray) and singular (dark gray) verb forms.

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Figure 3. Best conditional inference tree for Sarah's corpus. Node labels indicate the splitting factor, and the edge labels indicate the levels of that factor in each branch. Each terminal node shows the number of tokens it contains (n) and the proportion of those tokens that have plural (light gray) and singular (dark gray) verb forms.

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Figure 4. The relationship between contractedness and agreement variation in sentences with singular verbs in the Sarah and Nina corpora. Panel A shows the overall rate of plural subjects with singular verb forms, split by contractedness, corpus, and speaker. Panel B shows rates of plural subjects with singular verb forms for cells with highest rates of contractedness, split by corpus, speaker, verb form, sentence type, subject type, and order. In both panels, point size indicates the number of contributing tokens.

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Figure 5. Sample pages from the Search-and-Find task.

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Figure 6. Percentage plural verb forms in sentences with plural subjects, split on the horizontal axis by speaker and order and on the vertical axis by subject type and sentence type. Number of plural verb tokens and total tokens (i.e., plural + singular verb forms) are shown below percentages (plural/total). The darker the cell, the lower the rate of plural agreement. Blank cells indicate combinations of factors that did not occur.

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Table 3. Results from the generalized linear models of verb form in sentences with plural subjects for Search-and-Find corpus (n = 1383)

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Figure 7. Best conditional inference tree for the Search-and-Find corpus. Node labels indicate the splitting factor, and the edge labels indicate the levels of that factor in each branch. Each terminal node shows the number of tokens it contains (n) and the proportion of those tokens that have plural (light gray) and singular (dark gray) verb forms.

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Figure 8. The relationship between contractedness and agreement variation in sentences with singular verbs in the Search-and-Find corpus. Panel A shows the overall rate of plural subjects with singular verb forms, split by contractedness and speaker. Panel B shows rates of plural subjects with singular verb forms for cells with highest rates of contractedness, split by speaker, verb form, sentence type, subject type, and order. In both panels, point size indicates the number of contributing tokens.

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Figure 9. Patterns of variability across caregivers in the Search-and-Find task. Panel A shows the relationship between the number of likely contexts for variation (there, where, and here VS sentences with plural nonpronoun subjects) and caregivers’ production of at least one instance of nonagreement, with a logistic fit. Panel B shows the relationship between the number of likely contexts for variation and the proportion of plural verb forms each caregiver produced, with a linear fit.

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