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Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

[+] Author Affiliations
Feng Zhou, Jianxin (Roger) Jiao

Georgia Institute of Technology, Atlanta, GA

Paper No. DETC2013-13693, pp. V03BT03A023; 9 pages
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3B: 39th Design Automation Conference
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5589-8
  • Copyright © 2013 by ASME


Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

Copyright © 2013 by ASME



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