Hunt Ruskin H, Aslin Richard N
University of Rochester.
J Mem Lang. 2010 Feb 1;62(2):98-112. doi: 10.1016/j.jml.2009.10.002.
Category formation lies at the heart of a number of higher-order behaviors, including language. We assessed the ability of human adults to learn, from distributional information alone, categories embedded in a sequence of input stimuli using a serial reaction time task. Artificial grammars generated corpora of input strings containing a predetermined and constrained set of sequential statistics. After training, learners were presented with novel input strings, some of which contained violations of the category membership defined by distributional context. Category induction was assessed by comparing performance on novel and familiar strings. Results indicate that learners develop increasing sensitivity to the category structure present in the input, and become sensitive to fine-grained differences in the pre- and post-element contexts that define category membership. Results suggest that distributional analysis plays a significant role in the development of visuomotor categories, and may play a similar role in the induction of linguistic form-class categories.
类别形成是包括语言在内的许多高阶行为的核心。我们使用序列反应时任务评估了成年人仅从分布信息中学习嵌入在一系列输入刺激中的类别的能力。人工语法生成了包含预定且受限的序列统计集的输入字符串语料库。训练后，向学习者呈现新的输入字符串，其中一些包含违反由分布上下文定义的类别成员资格的情况。通过比较对新字符串和熟悉字符串的表现来评估类别归纳。结果表明，学习者对输入中存在的类别结构的敏感度不断提高，并对定义类别成员资格的前后元素上下文中的细微差异变得敏感。结果表明，分布分析在视觉运动类别的发展中起着重要作用，并且可能在语言形式类别归纳中发挥类似作用。
