期刊
COGNITIVE PSYCHOLOGY
卷 56, 期 3, 页码 165-209出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cogpsych.2007.04.002
关键词
language acquisition; sentence processing; verb argument structures; eye-tracking; artificial language learning
资金
- NICHD NIH HHS [R01 HD027206, R01 HD027206-16, HD-27206] Funding Source: Medline
- NIDCD NIH HHS [R01 DC000167-26, R01 DC000167, DC-00167] Funding Source: Medline
- Economic and Social Research Council [ES/E003001/1] Funding Source: researchfish
- ESRC [ES/E003001/1] Funding Source: UKRI
Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings. (C) 2007 Elsevier Inc. All rights reserved.
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