Unsupervised concept learning and value systematicity: A complex whole aids learning the parts
Unsupervised concept learning and value systematicity: A complex whole aids learning the parts
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DOI：
10.1037/0278-7393.22.2.458
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发表时间：
1996-03-01
影响因子：
2.6
通讯作者：
Knutson, J
中科院分区：
文献类型：
--
作者：
Billman, D;Knutson, J
通过同时考虑学习模型和“好”类别系统如何组织的理论，可以最好地理解学习新概念的难易程度。作者测试了价值系统性对学习的影响，价值系统性是一项提出的组织原则：如果一个属性可以预测另一个属性，它应该预测更多。这一原则源自无监督学习或观察学习的内部反馈模型（D. Billman & E. Heir，1988）中的集中采样。在 3 个实验中，作者测试了输入结构的组织（价值系统性）如何影响外来动物类别的无监督学习。在所有实验中，相对于几个低系统性对照，在高系统性值的条件下学习目标规则更容易。作者将结果与几种学习模型的预测进行了比较，并考虑了学习与由此产生的类别结构之间的联系。
Ease of learning new concepts may best be understood by simultaneously considering models of learning and theories of how ''good'' systems of categories are organized. The authors tested the effects on learning of value systematicity, a proposed organizing principle: If 1 attribute is predictive of another, it should predict still more. This principle derives from focused sampling in the internal feedback model (D. Billman & E. Heir, 1988) of unsupervised, or observational, learning. In 3 experiments, the authors tested how the organization of structure in input (value systematicity) affected unsupervised learning of categories about alien animals. Across all experiments, learning a target rule was easier in conditions with high value systematicity, relative to several low systematicity controls. The authors compare results to predictions of several learning models and consider the links between learning and the resulting category structure.