Conway Christopher M, Christiansen Morten H
Department of Psychology, 1101 E. 10th St., Indiana University, Bloomington, IN 47405, USA.
Psychol Sci. 2006 Oct;17(10):905-12. doi: 10.1111/j.1467-9280.2006.01801.x.
When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.
当学习者对序列模式进行编码并将其知识推广到新的实例时，他们是依赖抽象表征还是特定于刺激的表征呢？人工语法学习（AGL）的研究表明学习可以从一组刺激迁移到另一组刺激，这样的发现支持了一种观点，即统计学习是由独立于刺激的感觉模态或感知特征的抽象表征介导的。通过对标准AGL范式进行新颖的修改，我们得到了相反的数据。这些实验将抽象加工与特定于刺激的学习进行了对比。研究结果表明，统计学习产生的是特定于刺激而非抽象的知识。此外，研究结果还表明，沿着不同的感知维度或感觉模态，多个输入流的学习可以并行进行。我们得出结论，学习序列结构并推广到新刺激本质上涉及与输入的感知特征紧密相关的学习机制。
