The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'.
The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'.
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句子结构的跨语言习得：英语、日语、印地语、希伯来语和基切语成人和儿童的计算建模和语法判断。
DOI：
10.1016/j.cognition.2020.104310
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发表时间：
2020
期刊：
影响因子：
3.4
通讯作者：
Ambridge B
中科院分区：
文献类型：
--
作者：
Ambridge B
这项预先注册的研究测试了关于儿童如何形成富有成效但受限的语言概括的三项理论建议，避免了诸如*小丑笑了人之类的错误，涵盖三个年龄组（5-6岁、9-10岁、成人）和五种语言（英语、日语、印地语、希伯来语和基切语）。参与者按照五分制对描述因果关系事件的正确且不合语法的句子进行评分（例如，*有人笑了那个人；有人让那个人笑了；有人打破了卡车；？有人让卡车坏了）。动词语义假说预测，对于所有语言，可接受性评级中的动词差异将通过引起事件和引起事件（例如，有趣和大笑）在概念上合并为单个事件（由不同的成人参与者组进行评级）的程度来预测。巩固和抢占假设预测，对于所有语言，可接受性评级中的动词差异将分别通过动词的相对整体频率和近同义结构中的频率来预测（例如，X 让 Y 笑了*某人笑了这个人）。使用混合效应模型的分析表明，除了 K'iche' 之外，所有年龄组和所有语言都观察到了巩固/抢占效应（由于共线性而无法区分），K'iche 语言的语料库很薄，仅偶尔出现抢占。所有语言都显示出事件合并语义的影响，但 K'iche' 除外，它仅显示了补充语义预测器的影响。最后，我们提出了一个计算模型，该模型在单一判别学习机制中成功模拟了这种结果模式，实现了与人类判断数据的动词相关性 aroundr=0.75。
This preregistered study tested three theoretical proposals for how children form productive yet restricted linguistic generalizations, avoiding errors such as*The clown laughed the man, across three age groups (5–6 years, 9–10 years, adults) and five languages (English, Japanese, Hindi, Hebrew and K'iche'). Participants rated, on a five-point scale, correct and ungrammatical sentences describing events of causation (e.g.,*Someone laughed the man; Someone made the man laugh;Someone broke the truck;?Someone made the truck break). The verb-semantics hypothesis predicts that, for all languages, by-verb differences in acceptability ratings will be predicted by the extent to which the causing and caused event (e.g., amusing and laughing) merge conceptually into a single event (as rated by separate groups of adult participants). The entrenchment and preemption hypotheses predict, for all languages, that by-verb differences in acceptability ratings will be predicted by, respectively, the verb's relative overall frequency, and frequency in nearly-synonymous constructions (e.g., X made Y laugh for*Someone laughed the man). Analysis using mixed effects models revealed that entrenchment/preemption effects (which could not be distinguished due to collinearity) were observed for all age groups and all languages except K'iche', which suffered from a thin corpus and showed only preemption sporadically. All languages showed effects of event-merge semantics, except K'iche' which showed only effects of supplementary semantic predictors. We end by presenting a computational model which successfully simulates this pattern of results in a single discriminative-learning mechanism, achieving by-verb correlations of aroundr= 0.75 with human judgment data.