Probabilistically-Cued Patterns Trump Perfect Cues in Statistical Language Learning.
Probabilistically-Cued Patterns Trump Perfect Cues in Statistical Language Learning.
复制标题
概率提示模式胜过统计语言学习中的完美提示。
DOI：
10.1080/15475441.2012.685826
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Gómez,RebeccaL
中科院分区：
文献类型：
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作者：
Lany,Jill;Gómez,RebeccaL
在自然语言中，词类之间的概率性共现关系是常见的，但很难获得。例如，在英语中，限定词-名词和非限定词-动词依赖关系都涉及共现关系，但限定词-名词关系更可靠地由相关的分布和语音线索标记，并且似乎更容易学习。我们测试了是否有经验的同现关系，更可靠的促进学习那些不太可靠的使用人工语言范式。先前的经验与确定性线索的偶然性并没有促进学习不太可靠的线索结构，也没有先前的经验与关系实例化在相同的词汇。相比之下，先前的经验与概率提示同现关系实例化不同的词汇，并提高学习。因此，共现关系共享潜在结构而不是词汇的经验可能是学习语法模式的重要因素。此外，概率提示共现关系的经验，尽管他们很难为天真的学习者，奠定了学习新的概率结构的重要基础。
Probabilistically cued co-occurrence relationships between word categories are common in natural languages but difficult to acquire. For example, in English, determiner-noun and auxiliary-verb dependencies both involve co-occurrence relationships but determiner-noun relationships are more reliably marked by correlated distributional and phonological cues and appear to be learned more readily. We tested whether experience with co-occurrence relationships that are more reliable promotes learning those that are less reliable using an artificial language paradigm. Prior experience with deterministically cued contingencies did not promote learning of less reliably cued structure, nor did prior experience with relationships instantiated in the same vocabulary. In contrast, prior experience with probabilistically cued co-occurrence relationships instantiated in different vocabulary did enhance learning. Thus, experience with co-occurrence relationships sharing underlying structure but not vocabulary may be an important factor in learning grammatical patterns. Furthermore, experience with probabilistically cued co-occurrence relationships, despite their difficultly for naïve learners, lays an important foundation for learning novel probabilistic structure.