类别学习中两种学习模式的比较研究：分类学习与推理学习

A Comparative Study of Two Types of Category Learning：Classification and Inference Learning

采用学习迁移任务范式，使用基于单一特征的类别判断技术，比较了非线性分离结构下，分类学习和推理学习的学习效率、学习过程与策略和学习结果。结果表明：在学习效率上，分类学习比推理学习更好地习得了含有较多样例的类别知识，分类学习的速度上显著快于推理学习。在学习的过程与策略上，推理学习比分类学习更为关注类别内不同特征的相关，但在分类策略的运用上不如分类学习灵活。在学习的结果上，推理学习倾向于原型记忆，分类学习倾向于进行样例记忆，分类学习比推理学习更好地掌握了类别原型。

Introduction In this article， we compare classification learning with inference learning. In the inference task， participants predict the value of a missing feature of an item given its category label and other feature values. In the classification task， participants predict the category label of an item given its feature values. Yamauchi and Markman(1998，2000，2002) showed that these two types of learning did not result in the learning of equivalent knowledge. Categories defined by a family resemblance structure were more easily learned by inference learning than by classification learning， whereas categories defined by a nonlinearly separable structure were more easily learned by classification learning than by inference learning. Chin-Parker and Ross（2002， 2004） found that Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic， prototypical features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic， prototypical features. In the current experiments， we examined the sensitivity of classification and inference learners to another critical type of category information-feature correlation information. We further systematically explored the two learning tasks that might lead to differential learning efficiency， strategy and outcome.