Modeling Prototypical Preference Behavior and Diversity using Rank Score Characteristic Functions

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyze...

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Bibliographic Details
Published in2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCICC) pp. 203 - 207
Main Authors Schweikert, Christina, Shimojo, Shinsuke, Glasser, Hannah, Hendsey, Rebecca, Alsaber, Rami, Hsu, D. Frank
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.12.2022
Subjects
Behavioral sciences
cognitive diversity
Cognitive systems
Computational modeling
Decision making
Faces
information fusion
preference formation
rank score characteristic function
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Summary:When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling.
DOI:10.1109/ICCICC57084.2022.10101519