AN ADAPTIVE APPROACH TO HUMAN DECISION-MAKING - LEARNING-THEORY, DECISION-THEORY, AND HUMAN-PERFORMANCE
AN ADAPTIVE APPROACH TO HUMAN DECISION-MAKING - LEARNING-THEORY, DECISION-THEORY, AND HUMAN-PERFORMANCE
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DOI：
10.1037/0096-3445.121.2.177
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发表时间：
1992-06-01
影响因子：
4.1
通讯作者：
MYUNG, IJ
中科院分区：
文献类型：
--
作者：
BUSEMEYER, JR;MYUNG, IJ
本文描述了决策规则学习的一般模型，规则竞争模型，由两部分组成：一个自适应网络模型，描述了个体如何学习预测在任何给定情况下应用每个决策规则产生的收益;一个爬山模型，描述了个体如何学习通过调整其参数来微调每个规则。在3个概率分类实验中对该模型进行了测试，并与其他模型进行了比较。第一个实验的目的是测试的自适应网络模型使用概率学习任务，第二个设计测试的参数搜索过程中使用的标准学习任务，第三个设计测试的两个部分的模型同时使用的任务，需要学习的类别规则和截止标准。
This article describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that describes how individuals learn to fine tune each rule by adjusting its parameters. The model was tested and compared with other models in 3 experiments on probabilistic categorization. The first experiment was designed to test the adaptive network model using a probability learning task, the second was designed to test the parameter search process using a criterion learning task, and the third was designed to test both parts of the model simultaneously by using a task that required learning both category rules and cutoff criteria.