Flannagan M J, Fried L S, Holyoak K J
J Exp Psychol Learn Mem Cogn. 1986 Apr;12(2):241-56. doi: 10.1037//0278-7393.12.2.241.
Previous research on how categories are learned from observation of exemplars has largely ignored the possible role of prior expectations concerning how exemplars will be distributed. The experiments reported here explored this issue by presenting subjects with category-learning tasks in which the distributions of exemplars defining the categories were varied. In Experiments 1 and 2 the distributional form of a category was found to affect speed of learning. Learning was faster when a category's distribution was normal than when it was multimodal. Also, subjects in the early stages of learning a multimodal category responded as if it were unimodal. These results suggested that subjects enter category-learning tasks with expectations of unimodal, possibly normal, distributions of exemplars. Experiments 3 and 4 attempted to manipulate subjects' prior expectations by varying the distribution of exemplars in the first of two consecutive category-learning tasks. Learning a multimodal category was influenced by the shape of a previously learned distribution and was facilitated when the earlier distribution was either multimodal or skewed, rather than normal. These results are interpreted as support for a dual-process model of category learning that incorporates the effects of prior expectations concerning exemplar distributions.
以往关于如何通过观察范例来学习类别知识的研究，在很大程度上忽略了有关范例将如何分布的先验期望可能发挥的作用。本文所报告的实验通过向受试者呈现类别学习任务来探究这一问题，在这些任务中，定义类别的范例分布是变化的。在实验1和实验2中，发现类别的分布形式会影响学习速度。当类别的分布呈正态分布时，学习速度比呈多峰分布时更快。此外，在学习多峰类别的早期阶段，受试者的反应就好像它是单峰的一样。这些结果表明，受试者在进入类别学习任务时，对范例的分布期望是单峰的，可能是正态的。实验3和实验4试图通过改变连续两个类别学习任务中第一个任务的范例分布来操纵受试者的先验期望。学习多峰类别受到先前学习的分布形状的影响，当早期分布是多峰或偏态而非正态时，学习会得到促进。这些结果被解释为对类别学习双过程模型的支持，该模型纳入了有关范例分布的先验期望的影响。
