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Improving Preference Detection with Eye Movement Gaze and Cognitive Diversity

Schweikert, Christina and Shimojo, Shinsuke and Zhang, Zihan and Tato, Jonida and Hendsey, Rebecca and Hsu, D. Frank (2021) Improving Preference Detection with Eye Movement Gaze and Cognitive Diversity. In: 2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE , Piscataway, NJ, pp. 98-102. ISBN 978-1-6654-2119-5.

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Preference choices of a subject when given two human face images is a complex cognitive process. One way to detect a subject’s preference is by recording and examining the subject’s eye movement gaze sequence before his/her decision. Combinatorial fusion analysis / algorithm (CFA) is a new approach for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD) measure. In this paper, we apply CFA to the study of the eye movement gaze sequences for preference detection. In particular, we use the RSC function to characterize each of the attributes and the CD to measure the diversity between attributes. Our results demonstrate that weighted combination of attributes using diversity strength, computed using average CD’s, improves the preference detection.

Item Type:Book Section
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Shimojo, Shinsuke0000-0002-1290-5232
Additional Information:© 2022 IEEE.
Subject Keywords:image preference, cognitive diversity, combinatorial fusion algorithm, information fusion
Record Number:CaltechAUTHORS:20220707-315700000
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115364
Deposited By: George Porter
Deposited On:07 Jul 2022 17:54
Last Modified:07 Jul 2022 17:54

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