Abstract
Several studies on categorization have revealed that the strategy of categorization varies according to the category structures of the stimuli and the procedures used in the experiment. This study examined how categorization could vary by the distribution of learning exemplars, which has not been examined in the previous studies.
The stimuli were star-shaped geometric figures, and they were divided into two categories: fat and thin. Two conditions were set for the learning exemplars. In the dense condition, the exemplars were located slightly far from the boundary of the two categories. In the prominent condition, one exemplar was located far from the boundary (the prominent exemplar) and the others, near the boundary. Participants were told to rate the typicality for each exemplar in the test phase. In the dense condition, the responses of many participants revealed that the typicality ratings tended to be the highest for the learning exemplars located near the center. On the other hand, in the prominent condition, the rating values of many participants increased according to the increase in the distance from the boundary.
These results showed that the difference in the distribution of learning exemplars caused the change of categorization. Additionally, the prominent exemplar facilitated the comparison between the two categories and contributed largely to summarize information in the prominent condition.
