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Continuous measure of word learning supports associative model

机译：单词学习的连续量度支持联想模型

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Cross-situational learning, the ability to learn word meanings across multiple scenes consisting of multiple words and referents, is thought to be an important tool for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult to uncover the learning mechanics in part because the standard experimental paradigm, which presents a few words and objects on each of a series of training trials, measures learning only at the end of training after several occurrences of each word-object pair. Thus, the exact learning moment-and its current and historical context-cannot be investigated directly. This paper offers a version of the cross-situational learning task in which a response is made each time a word is heard, as well as in a final test. We compare this to the typical cross-situational learning task, and examine how well the response distributions match two recent computational models of word learning.
机译：跨情境学习是一种在多个场景中学习由多个单词和所指对象组成的单词含义的能力，被认为是获取语言的重要工具。已经在婴儿，儿童和成人中研究了这种能力，但是关于跨情境单词学习过程中起作用的基本存储和检索机制仍有很多争议。很难发现学习机制的部分原因是，标准的实验范式在一系列训练试验的每一个中都显示几个单词和对象，并且仅在每个单词-对象对多次出现后才在训练结束时测量学习。因此，不能直接研究确切的学习时刻及其当前和历史背景。本文提供了跨境学习任务的一种版本，其中每次听到单词以及在最终测试中都会做出响应。我们将此与典型的跨情境学习任务进行比较，并检查响应分布与两个最近的单词学习计算模型的匹配程度。

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