RULE-PLUS-EXCEPTION MODEL OF CLASSIFICATION LEARNING
RULE-PLUS-EXCEPTION MODEL OF CLASSIFICATION LEARNING
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DOI：
10.1037/0033-295x.101.1.53
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发表时间：
1994-01-01
影响因子：
5.4
通讯作者：
MCKINLEY, SC
中科院分区：
文献类型：
--
作者：
NOSOFSKY, RM;PALMERI, TJ;MCKINLEY, SC
提出了一种规则加例外的分类学习模型（RULEX）。根据RULEX，人们通过形成简单的逻辑规则并记住这些规则的偶尔例外来学习分类对象。因为RULEX的学习过程是随机的。该模型预测，各个S在形成的特定规则和存储的例外方面会有很大的不同。平均分类数据被假定为代表这些高度特质的规则和例外的混合物。RULEX解释了许多基本的分类现象，包括原型和特定的范例效应，对相关信息的敏感性，学习线性可分与非线性可分类别的困难，选择性注意效应，以及学习具有不同复杂性规则的概念的困难。RULEX还预测在个体受试者水平上观察到的泛化模式的分布。
The authors propose a rule-plus-exception model (RULEX) of classification learning. According to RULEX, people learn to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. Because the learning process in RULEX is stochastic. the model predicts that individual Ss will vary greatly in the particular rules that are formed and the exceptions that are stored. Averaged classification data are presumed to represent mixtures of these highly idiosyncratic rules and exceptions. RULEX accounts for numerous fundamental classification phenomena, including prototype and specific exemplar effects, sensitivity to correlational information, difficulty of learning linearly separable versus nonlinearly separable categories, selective attention effects, and difficulty of learning concepts with rules of differing complexity. RULEX also predicts distributions of generalization patterns observed at the individual subject level.