Spontaneous transfer of relational category structures between category learning tasks: A novel approach to measure analogical transfer

Abstract

The ability to spontaneously access knowledge of relational concepts acquired in one domain and apply it to a novel domain has traditionally been explored in the analogy literature via the problem-solving paradigm (cf. Gick & Holyoak, 1980, 1983). In the present work, we propose a novel procedure based on categorization as a complementary approach to assess spontaneous analogical transfer—using one category learning task to enhance learning of the same underlying category structures in another domain. In Experiment 1, we demonstrate larger improvements in classification performance across blocks of training in a Target Category Learning task among participants that underwent a Base Category Learning task relative to a separate group of participants learning the Target category structures for the first time; thus providing evidence for spontaneous transfer of the category structures. In Experiment 2, we demonstrate similar evidence of spontaneous transfer for participants that underwent a comparison-based Base Category Learning task under a more rigorous context-shift between the Base and Target Category Learning tasks. Additional exploratory analyses across both experiments showcase ways in which this paradigm can be used to answer questions regarding the analogical transfer of relational category structures and generate promising paths for future work.