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Learning categories by making predictions: An investigation of indirect category learning

机译：通过预测来学习类别：间接类别学习的调查

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摘要

Categories are learned in many ways, but studies of category learning have generally focused on classification learning. This focus may limit the understanding of categorization processes. Two experiments were conducted in which participants learned categories of animals by predicting how much food each animal would eat. We refer to this as indirect category learning, because the task and the feedback were not directly related to category membership, yet category learning was necessary for good performance in the task. In the first experiment, we compared the performance of participants who learned the categories indirectly with the performance of participants who first learned to classify the objects. In the second experiment, we replicated the basic findings and examined attention to different features during the learning task. In both experiments, participants who learned in the prediction-only condition displayed a broader distribution of attention than participants who learned in the classification-and-prediction condition did. Some participants in the prediction-only group learned the family resemblance structure of the categories, even when a perfect criterial attribute was present. In contrast, participants who first learned to classify the objects tended to learn the criterial attribute.
机译：类别的学习方法很多，但是类别学习的研究通常集中在分类学习上。此重点可能会限制对分类过程的理解。进行了两个实验，参与者通过预测每只动物要吃多少食物来学习动物的种类。我们将此称为间接类别学习，因为任务和反馈与类别成员不直接相关，但是类别学习对于在任务中取得良好的表现是必需的。在第一个实验中，我们将间接学习类别的参与者的表现与首次学习对对象进行分类的参与者的表现进行了比较。在第二个实验中，我们复制了基本发现，并检查了学习任务期间对不同功能的注意。在这两个实验中，在仅预测条件下学习的参与者比在分类和预测条件下学习的参与者表现出更广泛的注意力分布。仅存在预测的小组中的一些参与者即使存在完美的标准属性，也了解了类别的家庭相似结构。相反，最先学会对对象进行分类的参与者倾向于学习标准属性。

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