Blair M, Homa D
Department of Psychology, Arizona State University, Tempe 85287-1104, USA.
Mem Cognit. 2001 Dec;29(8):1153-64. doi: 10.3758/bf03206385.
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.
分类的形式模型对线性可分性的理论重要性做出了不同预测。先前的研究大多未能找到支持类别学习中线性可分性约束的证据，这些研究使用的任务涉及学习两个成员数量较少的类别。本实验使用了四个类别，每个类别有三种或九种模式，这些模式要么是线性可分的，要么是非线性可分的。在各类别类型的总体类别结构等效的情况下，发现线性可分的类别比非线性可分的类别更容易学习。对个体参与者数据的分析表明，学习大类别时比学习小类别时有更多参与者在线性可分性约束下进行操作。形式建模表明，范例模型无法解释这些数据中的许多情况。这些结果被视为支持分类中存在多种过程。
