Acquiring and processing verb argument structure: Distributional learning in a miniature language

@article{citeulike:1832240, abstract = {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.}, author = {Wonnacott, Elizabeth and Newport, Elissa L. and Tanenhaus, Michael K. }, citeulike-article-id = {1832240}, doi = {10.1016/j.cogpsych.2007.04.002}, journal = {Cognitive Psychology}, keywords = {artificial-grammars, statistical-learning, syntactic-acquisition}, month = {May}, number = {3}, pages = {165--209}, posted-at = {2007-10-28 14:00:50}, priority = {0}, title = {Acquiring and processing verb argument structure: Distributional learning in a miniature language}, url = {http://dx.doi.org/10.1016/j.cogpsych.2007.04.002}, volume = {56}, year = {2008} }

TY - JOUR ID - citeulike:1832240 L3 - citeulike-article-id:1832240 TI - Acquiring and processing verb argument structure: Distributional learning in a miniature language JF - Cognitive Psychology VL - 56 IS - 3 SP - 165 EP - 209 N2 - 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. KW - artificial-grammars KW - statistical-learning KW - syntactic-acquisition AU - Wonnacott, Elizabeth AU - Newport, Elissa AU - Tanenhaus, Michael PY - 2008/05// UR - http://dx.doi.org/10.1016/j.cogpsych.2007.04.002 ER -