Turk-Browne Nicholas B, Isola Phillip J, Scholl Brian J, Treat Teresa A
Department of Psychology, Yale University, New Haven, CT 06520-8205, USA.
J Exp Psychol Learn Mem Cogn. 2008 Mar;34(2):399-407. doi: 10.1037/0278-7393.34.2.399.
Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.
近期关于视觉统计学习（VSL）的研究表明，即使没有意图或意识，视觉刺激序列中的统计规律也能被自动提取。然而，尽管在这个主题上已经做了很多工作，但关于VSL的本质仍有几个基本问题。特别是，以前的实验尚未探索VSL所作用的潜在单元。例如，在一系列彩色形状中，VSL是独立作用于每个特征维度，还是作用于颜色和形状绑定在一起的多维物体？这里报道的研究表明，基于不同特征维度的协变方式，VSL可以系统地同时基于物体和基于特征。例如，当每个形状与特定颜色完美协变时，VSL是基于物体的：观察者在测试时对彩色形状子序列表现出强烈的VSL，但当测试项目由单色形状或颜色块组成时则失败。然而，当形状和颜色对在学习过程中部分解耦时，VSL作用于特征：当分别测试特征维度时，观察者表现出强烈的VSL。这些结果表明VSL是基于物体的，但对多维序列中特征相关性的敏感性（可能是VSL的另一种形式）反过来可能有助于定义什么算作一个物体。
