AUTHOR=Zhou Xiaoyan , Fu Qiufang , Rose Michael , Sun Yuqi TITLE=Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features JOURNAL=Frontiers in Psychology VOLUME=10 YEAR=2019 URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.00183 DOI=10.3389/fpsyg.2019.00183 ISSN=1664-1078 ABSTRACT=

Although many researches have shown that edge-based information is more important than surface-based information in object recognition, it remains unclear whether edge-based features play a more crucial role than surface-based features in category learning. To address this issue, a modified prototype distortion task was adopted in the present study, in which each category was defined by a rule or a similarity about either the edge-based features (i.e., contours or shapes) or the corresponding surface-based features (i.e., color and textures). The results of Experiments 1 and 2 showed that when the category was defined by a rule, the performance was significantly better in the edge-based condition than in the surface-based condition in the testing phase, and increasing the defined dimensions enhanced rather than reduced performance in the edge-based condition but not in the surface-based condition. The results of Experiment 3 showed that when each category was defined by a similarity, there was also a larger learning effect when the category was defined by edge-based dimensions than by surface-based dimensions in the testing phase. The current study is the first to provide convergent evidence that the edge-based information matters more than surface-based information in incidental category learning.