Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features
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Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features. / Zhou, Xiaoyan; Fu, Qiufang; Rose, Michael; Sun, Yuqi.
in: FRONT PSYCHOL, Jahrgang 10, 2019, S. 183.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features
AU - Zhou, Xiaoyan
AU - Fu, Qiufang
AU - Rose, Michael
AU - Sun, Yuqi
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.3389/fpsyg.2019.00183
DO - 10.3389/fpsyg.2019.00183
M3 - SCORING: Journal article
C2 - 30792675
VL - 10
SP - 183
JO - FRONT PSYCHOL
JF - FRONT PSYCHOL
SN - 1664-1078
ER -