Fumio Kanbe
Fumio Kanbe is a professor in the Faculty of Education, Hakuoh University in Oyama City, Tochigi Prefecture, Japan. He obtained a Bachelor of Education, a Master of Letters, and a Ph.D. in Human Sciences from Waseda University, Japan.
He first worked for Hakuoh Women’s Junior College in 1984 as an instructor and then gained the position of an assistant professor and later a professor before transferring to Hakuoh University.
By encountering F. Harary’s “Graph Theory” in his doctoral course period, he wished to use (6, n) figures as stimuli for figure recognition experiments. Here, a (6, n) figure consists of n line segments spanned between n pairs of points located at the vertices of an invisible regular hexagon. He calculated structural properties (i.e., graph invariants) and superficial features (i.e., non-graph invariants) of each of (6, n) figure with n ranging from 1 to 6 and stored the calculated values in a database. After years of trial and error in research methodology, he intended to examine whether the structural property of a closure (i.e., a cycle) or a line terminator (i.e., an endpoint) is critical in figure recognition with the use of the database. The question was experimentally implemented by a search task in 2008, an identity decision task in 2009, and a detection task in 2010. He then hypothesized that the detection of structural properties is prioritized to the detection of superficial features in the recognition of figures. In a same/different decision task of pairs of figures, the results supported the hypothesis when a same pair was defined as the sameness in shape and in orientation (reported in 2013) and when defined as the sameness of shape regardless of its orientation (reported in 2018). He is currently interested in a research question concerning what specific superficial feature should be critical to discriminate figure pairs when there is no difference in structural properties (i.e., mutually isomorphic) between the figures. Relevant results have been reported in 2015 and 2019.
