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预测成人和儿童在推理新词义时对语用线索的整合。

Predicting pragmatic cue integration in adults' and children's inferences about novel word meanings.

机构信息

Department of Comparative Cultural Psychology.

Department of Brain and Cognitive Sciences.

出版信息

J Exp Psychol Gen. 2022 Nov;151(11):2927-2942. doi: 10.1037/xge0001216. Epub 2022 Apr 7.

Abstract

Language is learned in complex social settings where listeners must reconstruct speakers' intended meanings from context. To navigate this challenge, children can use pragmatic reasoning to learn the meaning of unfamiliar words. A critical challenge for pragmatic reasoning is that it requires integrating multiple information sources, which have typically been studied separately. Here we study this integration process. First, we experimentally isolate two sources of pragmatic information: expectations about informative communication and common ground. Next, we use a probabilistic model of conversational reasoning to formalize how these information sources should be combined and how this process might develop. We use this model to generate quantitative predictions, which we test against new experimental data from 3- to 5-year-old children ( = 243) and adults ( = 694). Results show close alignment between model predictions and data. Furthermore, the model provided a better explanation of the data compared with simpler alternative models assuming that participants selectively ignore one information source. This work integrates distinct sets of findings regarding information sources for early language learning and suggests that pragmatic reasoning models can provide a quantitative framework for understanding developmental changes in language learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

摘要

语言是在复杂的社会环境中习得的,在这种环境中,听众必须从语境中重建说话者的意图。为了应对这一挑战,儿童可以运用语用推理来学习陌生词汇的含义。语用推理面临的一个关键挑战是,它需要整合多个信息来源,而这些信息来源通常是分开研究的。在这里,我们研究了这个整合过程。首先,我们通过实验将两种语用信息来源隔离开来:关于信息性交流的期望和共同背景。接下来,我们使用会话推理的概率模型来形式化这些信息来源应该如何组合,以及这个过程可能如何发展。我们使用这个模型生成定量预测,并将其与来自 3 至 5 岁儿童(n = 243)和成人(n = 694)的新实验数据进行比较。结果表明,模型预测与数据高度一致。此外,与假设参与者选择性忽略一个信息来源的简单替代模型相比,该模型对数据的解释更为合理。这项工作整合了关于早期语言学习信息来源的不同研究结果,并表明语用推理模型可以为理解语言学习的发展变化提供一个定量框架。

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