Abstract:

Categorization is a process in which human beings learn to classify different categories. The representation of categorical information and the implication of classification strategy are hot topics in the field of categorization. Category learning includes both supervised and unsupervised category learning. Whereas previous literature has had a lot of introductions on supervised category learning, this article mainly introduced unsupervised category learning in which how human beings represent information and apply strategy by direct vs. indirect ways. In the direct unsupervised category learning (constrained categorization tasks, unconstrained categorization tasks), individuals has a tendency to classify the strategy in a "one-dimension" way. Moreover, both within-category variances and between-category distance can influence category representation. Indirect categories of unsupervised learning are more likely to form a similar representation. By contrast, the representation of direct unsupervised category learning is rule-based. The current theories of unsupervised category learning can’t completely explain category strategy and representation. The researches on category transfer and knowledge effect in different learning tasks are not yet sufficient. Further studies can test some issues such as the influence of knowledge effect on the cognitive process of unsupervised category learning and explore some factors that influence category representation formation.

Key words: unsupervised category learning, constrained categorization tasks, unconstrained categorization tasks, representation