Rohrmeier Martin A, Cross Ian
Cluster Languages of Emotion, Freie Universität Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germany; Centre for Music and Science, Faculty of Music, University of Cambridge, United Kingdom.
Centre for Music and Science, Faculty of Music, University of Cambridge, United Kingdom.
Conscious Cogn. 2014 Jul;27:155-67. doi: 10.1016/j.concog.2014.03.011. Epub 2014 Jun 3.
Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies.
人类能够在各个领域快速学习复杂结构。在人工语法学习研究中，一些未经训练的对照组表现出高于随机水平的结果，这引发了关于在未经训练、无监督的测试情境中，正确和错误结构的学习能达到何种程度的问题。无监督在线学习效应的合理性通过n元语法、组块和简单循环网络模型进行了建模。应用了一种新颖的评估框架，该框架交替进行强制二元语法性判断以及对相同刺激的后续学习。我们的结果表明，n元语法和组块模型具有很强的在线学习效应，而简单循环网络模型的效应较弱。这些发现表明，在线学习是统计组块学习的一种合理效应，当不符合语法的序列包含很大比例的语法组块时就有可能出现。连续统计学习的这种常见效应可能是统计和内隐学习范式的基础，并对研究设计和测试方法产生影响。
