The role of edge-based and surface-based information in natural scene categorization: Evidence from behavior and event-related potentials.
A fundamental question in vision research is whether visual recognition is determined by edge-based information (e.g., edge, line, and conjunction) or surface-based information (e.g., color, brightness, and texture). To investigate this question, we manipulated the stimulus onset asynchrony (SOA) between the scene and the mask in a backward masking task of natural scene categorization. The behavioral results showed that correct classification was higher for line-drawings than for color photographs when the SOA was 13ms, but lower when the SOA was longer. The ERP results revealed that most latencies of early components were shorter for the line-drawings than for the color photographs, and the latencies gradually increased with the SOA for the color photographs but not for the line-drawings. The results provide new evidence that edge-based information is the primary determinant of natural scene categorization, receiving priority processing; by contrast, surface information takes longer to facilitate natural scene categorization.
State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. Electronic address: fuqf@psych.ac.cn.
Qiufang
Q
Fu
Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Yong-Jin
YJ
Liu
Sackler Centre for Consciousness Science and School of Psychology, University of Sussex, BN1 9QH Brighton, United Kingdom.
Zoltan
Z
Dienes
State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Jianhui
J
Wu
State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Wenfeng
W
Chen
State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Xiaolan
X
Fu
D000328:Adult; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010775:Photic Stimulation / Q000379:methods*; D011930:Reaction Time / Q000502:physiology; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult
ERPs; Edge-based theory; Natural scene categorization; SOA; Surface-based theory