Expanding the search for a linear separability constraint on category learning
Expanding the search for a linear separability constraint on category learning
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DOI：
10.3758/bf03206385
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发表时间：
2001-12-01
影响因子：
2.4
通讯作者：
Homa, D
中科院分区：
文献类型：
--
作者：
Blair, M;Homa, D
分类的形式模型对线性可分性的理论重要性做出了不同的预测。以前的研究，其中大部分都没有找到支持的线性可分性约束的类别学习，已进行了使用任务，涉及学习两个类别的成员数量很少。本实验使用了四个类别，每个类别有三个或九个模式，这些模式要么是线性可分的，要么不是线性可分的。在类别结构上，线性可分类别比非线性可分类别更容易学习。对个体参与者数据的分析表明，在学习大类别时，比学习小类别时，有更多的参与者在线性可分性约束下操作。正式建模表明，一个范例模型不能解释这些数据中的许多。这些结果被用来支持多个过程的存在，在分类。
Formal models of categorization make different predictions about the theoretical importance of linear separability. Prior research, most of which has failed to find support for a linear separability constraint on category learning, has been conducted using tasks that involve learning two categories with a small number of members. The present experiment used four categories with three or nine patterns per category that were either linearly separable or not linearly separable. With overall category structure equivalent across category types, the linearly separable categories were found to be easier to learn than the not linearly separable categories. An analysis of individual participants' data showed that there were more participants operating under a linear separability constraint when learning large categories than when learning small ones. Formal modeling showed that an exemplar model could not account for many of these data. These results are taken to support the existence of multiple processes in categorization.