Problem structure and the use of base-rate information from experience.
Problem structure and the use of base-rate information from experience.
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DOI：
10.1037//0096-3445.117.1.68
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发表时间：
1988-03
期刊：
影响因子：
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通讯作者：
Douglas L. Medin;S. Edelson
中科院分区：
文献类型：
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作者：
Douglas L. Medin;S. Edelson
这篇文章关注的是使用基本率信息，这是来自经验的分类例子的一个类别。基本任务包括模拟医疗决策，参与者学习根据症状信息诊断假设疾病。替代疾病的相对发生频率或基本发生率各不相同。在五个实验中，初始学习之后是一系列旨在索引基本速率信息使用的迁移测试。在这些测试中，所呈现的症状模式表明不止一种疾病，因此是模糊的。这些测试中的替代或候选疾病在学习期间的相对发生频率可能不同。例如，一种相对常见和相对罕见的疾病都可能出现的症状。如果参与者正确地使用了基本比率信息（根据贝叶斯定理），那么他们应该更有可能预测常见疾病的存在，而不是罕见疾病的存在。当前的分类模型在关于使用基本速率信息的预测方面有所不同。例如，大多数原型模型暗示对基本速率信息不敏感，而许多基于样本的分类模型预测适当使用基本速率信息。结果揭示了一个一致但复杂的模式。根据类别结构和模糊测试的性质，参与者适当地使用基本率信息，忽略基本率信息，或不适当地使用基本率信息（预测罕见疾病更有可能存在）。据我们所知，目前没有任何分类模型预测这种结果模式。为了解释这些结果，一个新的模型描述将财产或症状的竞争和上下文敏感的检索的想法。
This article is concerned with the use of base-rate information that is derived from experience in classifying examples of a category. The basic task involved simulated medical decision making in which participants learned to diagnose hypothetical diseases on the basis of symptom information. Alternative diseases differed in their relative frequency or base rates of occurrence. In five experiments initial learning was followed by a series of transfer tests designed to index the use of base-rate information. On these tests, patterns of symptoms were presented that suggested more than one disease and were therefore ambiguous. The alternative or candidate diseases on such tests could differ in their relative frequency of occurrence during learning. For example, a symptom might be presented that had appeared with both a relatively common and a relatively rare disease. If participants are using base-rate information appropriately (according to Bayes' theorem), then they should be more likely to predict that the common disease is present than that the rare disease is present on such ambiguous tests. Current classification models differ in their predictions concerning the use of base-rate information. For example, most prototype models imply an insensitivity to base-rate information, whereas many exemplar-based classification models predict appropriate use of base-rate information. The results reveal a consistent but complex pattern. Depending on the category structure and the nature of the ambiguous tests, participants use base-rate information appropriately, ignore base-rate information, or use base-rate information inappropriately (predict that the rare disease is more likely to be present). To our knowledge, no current categorization model predicts this pattern of results. To account for these results, a new model is described incorporating the ideas of property or symptom competition and context-sensitive retrieval.