Combinatorial fusion analysis in brain informatics: gender variation in facial attractiveness judgment

Authors:
D. Frank Hsu;Takehito Ito;Christina Schweikert;Tetsuya Matsuda;Shinsuke Shimojo
Affiliations:
Department of Computer and Information Science, Fordham University, New York, NY;Tamagawa University Brain Science Institute, Tokyo, Japan;Department of Computer and Information Science, Fordham University, New York, NY;Tamagawa University Brain Science Institute, Tokyo, Japan;Division of Biology/Computation and Neural Systems, California Institute of Technology, Pasadena, CA
Venue:
BI'11 Proceedings of the 2011 international conference on Brain informatics
Year:
2011

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Abstract

Information processing in the brain or other decision making systems, such as in multimedia, involves fusion of information from multiple sensors, sources, and systems at the data, feature or decision level. Combinatorial Fusion Analysis (CFA), a recently developed information fusion paradigm, uses a combinatorial method to model the decision space and the Rank-Score Characteristic (RSC) function to measure cognitive diversity. In this paper, we first introduce CFA and its practice in a variety of application domains such as computer vision and target tracking, information retrieval and Internet search, and virtual screening and drug discovery. We then apply CFA to investigate gender variation in facial attractiveness judgment on three tasks: liking, beauty and mentalization using RSC function. It is demonstrated that the RSC function is useful in the differentiation of gender variation and task judgment, and hence can be used to complement the notion of correlation which is widely used in statistical decision making. In addition, it is shown that CFA is a viable approach to deal with various issues and problems in brain informatics.