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Pragmatic, linguistic and cognitive factors in young children's development of quantity, relevance and word learning inferences

Published online by Cambridge University Press:  28 September 2021

Elspeth WILSON*
Affiliation:
Faculty of Education, University of Cambridge, UK
Napoleon KATSOS
Affiliation:
Faculty of Modern and Medieval Languages and Linguistics, University of Cambridge, UK
*
*Corresponding author: Elspeth Wilson E-mail: ep321@cam.ac.uk Faculty of Education, University of Cambridge 184 Hills Road, Cambridge, CB2 8PQ, UK
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Abstract

To better understand the developmental trajectory of children's pragmatic development, studies that examine more than one type of implicature as well as associated linguistic and cognitive factors are required. We investigated three- to five-year-old English-speaking children's (N = 71) performance in ad hoc quantity, scalar quantity and relevance implicatures, as well as word learning by exclusion inferences, using a sentence-to-picture-matching story-based task. Children's pragmatic abilities improved with age, with word learning by exclusion acquired first, followed by relevance and ad hoc quantity implicatures, and finally scalar quantity implicatures. In an exploratory analysis (with a subset of the data N = 58), we found that structural language knowledge was a predictor of pragmatic performance (but no evidence for an association with socioeconomic status or Theory of Mind, controlling for structural language). We discuss reasons why this developmental pattern emerges with reference to linguistic and extra-linguistic properties of these inferences.

Information

Type
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 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press
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Table 1. Review of previous literature on implicature development with studies using a picture-matching task

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Table 2. Information about participants

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Table 3. Information about participants for exploratory analysis of subset of participants

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Table 4. Experiment example items

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Table 5. Examples of visual stimuli for each inference type and condition

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Table 6. Proportion of correct responses by condition, inference type and age group

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Figure 1. Proportion of correct responses for word learning by exclusion (WLE), relevance, ad hoc quantity and scalar quantity inferences. Error bars show bootstrapped 95% confidence intervals for between-subject comparison

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Table 7. Mixed-effects logistic regression model: Response ~ Condition + Type + Age group + (1 + Condition + Age group + Block | Item), using glmer, family = binomial, optimizer = bobyqa, sum coding

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Table 8. Mixed-effects logistic regression model: Response ~ Condition + Type + Age group + (1 + Condition + Age group + Block | Item), using glmer, family = binomial, optimizer = bobyqa, backward difference coding

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Figure 2. Distribution of participant scores by age, inference type and condition

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Table 9. ANOVA model comparison for effect of block order, using glmer, family = binomial, optimizer = bobyqa, sum coding

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Table 10. ANOVA model comparison for effect of block order for scalar trials, using glmer, family = binomial, optimizer = bobyqa, sum coding

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Table 11. ANOVA model comparison for age, gender, structural language, SES and ToM