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Comprehension of indirect answers: Developmental trajectory for preschool- and early elementary school-aged children with typical development.

Timothy HuangLizbeth H Finestack
Published in: Journal of child language (2023)
Indirect answers are a common type of non-literal language that do not provide an explicit "yes" or "no" to a question (e.g., "I have to work late" indirectly answered "Are you going to the party?" with a negative response). In the current study, we examined the developmental trajectory of comprehension of indirect answers among 5- to 10-year-old children with typical development. Forty-eight children, 23 boys and 25 girls, between the ages of 5 years; 0 months and 10 years; 11 months ( M  = 8;2,  SD  = 19.77 months) completed an experimental task to judge whether a verbally presented indirect answer meant yes or no (Comprehension Task) and then explain their choice (Explanation Task). Responses were scored for accuracy and coded for error analysis. On the Comprehension Task, the 5- to 8-year-olds performed with approximately 85% accuracy, while the 9- and 10-year-olds achieved 95% accuracy. On the Explanation Task, the cross-sectional trajectory revealed three stages: the 5- and 6-year-olds adequately explained indirect answers 32% of the time, the 7- and 8-year-olds performed significantly higher at 55%, and the 9- and 10-year-olds made significant gains than the younger children at 66%. Error analysis revealed that when children fail to interpret speaker intentions appropriately, they repeat the speaker's utterance or provide an insufficient explanation 80% of the time. Other responses, such as those irrelevant to the context, indicating "I don't know" or no response, or that were made-up interpretations each accounted for 2%-10% of total inadequate explanations. Study findings indicate discrepancies between task performances and offer two separate sets of baseline data for future comparisons that investigate comprehension or explanation of indirect answers by children with different cultural and linguistic backgrounds and by those with varying cognitive and language profiles.
Keyphrases
  • young adults
  • cross sectional
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  • machine learning
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