DISTRIBUTIONAL EXPECTATIONS AND THE INDUCTION OF CATEGORY STRUCTURE
DISTRIBUTIONAL EXPECTATIONS AND THE INDUCTION OF CATEGORY STRUCTURE
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DOI：
10.1037/0278-7393.12.2.241
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发表时间：
1986-04-01
影响因子：
2.6
通讯作者：
HOLYOAK, KJ
中科院分区：
文献类型：
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作者：
FLANNAGAN, MJ;FRIED, LS;HOLYOAK, KJ
先前关于如何从对样本的观察中学习类别的研究在很大程度上忽略了先前关于样本如何分布的预期的可能作用。这里报告的实验通过向受试者提供类别学习任务来探讨这个问题，其中定义类别的范例的分布是不同的。在实验 1 和 2 中，发现类别的分布形式会影响学习速度。当类别分布呈正态分布时，学习速度比多峰分布时更快。此外，处于学习多模态类别早期阶段的受试者的反应就好像它是单模态的一样。这些结果表明，受试者进入类别学习任务时期望样本的单峰分布（可能是正态分布）。实验 3 和 4 试图通过改变两个连续类别学习任务中第一个任务中样本的分布来操纵受试者的先验期望。学习多模态类别受到先前学习的分布形状的影响，并且当早期分布是多模态或偏态分布而不是正态分布时，学习多模态类别会变得更加容易。这些结果被解释为对类别学习双过程模型的支持，该模型结合了有关样本分布的先验期望的影响。
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.