Murphy Gregory L, Ross Brian H
Department of Psychology, New York University.
J Mem Lang. 2010 Jul 1;63(1):1-17. doi: 10.1016/j.jml.2009.12.002.
In one form of category-based induction, people make predictions about unknown properties of objects. There is a tension between predictions made based on the object's specific features (e.g., objects above a certain size tend not to fly) and those made by reference to category-level knowledge (e.g., birds fly). Seven experiments with artificial categories investigated these two sources of induction by looking at whether people used information about correlated features within categories, suggesting that they focused on feature-feature relations rather than summary categorical information. The results showed that people relied heavily on such correlations, even when there was no reason to think that the correlations exist in the population. The results suggested that people's use of this strategy is largely unreflective, rather than strategically chosen. These findings have important implications for models of category-based induction, which generally ignore feature-feature relations.
在基于类别的归纳推理的一种形式中，人们会对物体的未知属性进行预测。基于物体的特定特征所做的预测（例如，超过一定大小的物体往往不会飞）与基于类别层面知识所做的预测（例如，鸟会飞）之间存在一种张力。针对人工类别进行的七项实验，通过观察人们是否使用类别内相关特征的信息，对这两种归纳推理来源进行了研究，结果表明人们关注的是特征与特征之间的关系，而非类别层面的概括性信息。结果显示，人们严重依赖此类相关性，即便没有理由认为这些相关性存在于总体之中。结果表明，人们使用这种策略很大程度上是不假思索的，而非经过策略性选择的。这些发现对基于类别的归纳推理模型具有重要意义，因为这些模型通常忽略了特征与特征之间的关系。
