ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2019, Vol. 27 ›› Issue (3): 465-474.doi: 10.3724/SP.J.1042.2019.00465

• Research Method • Previous Articles     Next Articles

Visualization of mental representation: Noise-based reverse correlation image classification technology

HOU Chun-Na1(), LIU Zhi-Jun2   

  1. 1 School of psychology, Northeast Normal University, Changchun 130024, China
    2 Department of Sociology, Changchun University of Science and Technology, Changchun 130022, China
  • Received:2018-07-17 Online:2019-03-15 Published:2019-01-22
  • Contact: Chun-Na HOU


Studies of the mental representation of images in social psychology have encountered difficulty in accurately portraying psychological activity. Over the past decade, reverse correlation image classification has emerged as a new psychophysical method that assumes there is a relationship between an observer’s response and visual noise, and that the response is based on the observer’s social judgment criteria, and are not random. Performing a sufficient number of weight calculations on the corresponding noise patterns of the observer’s reaction allows us to visualize the intrinsic evaluation characteristics of the observer. The use of reverse correlation image classification technology has achieved some results in the areas of trait research, ethnicity, and intergroup bias. In the future, however, it is necessary to solve the problems of excessive experimental trials, separation of mixed noise, and subjects’ performance, in order to achieve more realistic mental representations.

Key words: face, mental representation, reverse correlation image classification technology

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