The Influence Of Configural And Featural Information On Facial Expression Recognition

Facial expressions are an important source of social information. However, there is not a consistent finding in facial expression processing mechanism which is a key aspect of facial expression recognition area. There are three representative theories, including configural model, featural model and holistic model. Meanwhile, analyzing spatial frequency component is a good way to study facial expression. Therefore, in the present study we study the influence of different spatial frequency on facial expression recognition and explore the impact of configural and featural information on facial expression recognition using high and low spatial frequency pictures.The present study includes three experiments:Experiment 1 aimed to investigate the influence of spatial frequency on facial expression recognition, in which participants were showed normal, low pass filter and high pass filter pictures, and were required to judge what expression they saw. The second experiment was to explore the impact of configural information on facial expression recognition by using the pictures of low pass filter, low pass filter+eye, low pass filter+mouth. The last experiment was further to study the influence of featural information on facial expression recognition through showing the pictures of low pass filter+eye, low pass filter+mouth, and eye and mouth only.The main findings of the present study are as below:(1) Facial expressions differ in processing. The process of happy and sad face is consistent with holistic model; the process of angry and fear face is identical with featural model.(2) There is "happy face advantage", and this effect is caused by holistic structure. (3) The processing of low-spatial-frequency information is more effective than the one of high-spatial-frequency information.(4) Mouth is important for identifying happy face, and eyes are crucial to recognizing anger, fear and sad face.To sum up, there is different processing mechanism in recognizing facial expressions. The importance of feature information differs in recognizing facial expressions.