心理学
话语
理解力
背景(考古学)
任务(项目管理)
发展心理学
语言发展
儿童发展
认知心理学
语言学
古生物学
哲学
管理
经济
生物
作者
Timothy Huang,Lizbeth H. Finestack
标识
DOI:10.1017/s0305000923000442
摘要
Abstract 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI