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Decision theoretic modeling of affective and cognitive needs for product experience engineering: key issues and a conceptual framework

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Abstract

User experience (UX) design plays a critical role in product experience engineering. To create a theoretical foundation of UX design, it is imperative to develop mathematical and computational models for elicitation, quantification, evaluation and reasoning of affective–cognitive needs that are inherent in the fulfillment of user experience. This paper explores the key research issues for understanding how human users’ subjective experience and affective prediction impact their choice behavior under uncertainty. A conceptual framework is envisioned by extending prospect theory in the field of behavioral economics to the modeling of user experience choice behavior, in which inference of affective influence is enacted through the shape parameters of prospect value functions.

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References

  • Abbas, A. E. (2009). Multiattribute utility copulas. Operations Research, 57(6), 1367–1383.

    Article  Google Scholar 

  • Ahn, H. (2010). Modeling and analysis of affective influences on human experience. Prediction, Decision Making, and Behavior. PhD Thesis, MIT, Boston, MA.

  • Ahn, H., & Picard, R.W. (2005). Affective-cognitive learning and decision making: A motivational reward framework for affective agents. In The 1st international conference on affective computing and intelligent interaction (ACII 2005), Oct 22–24, Beijing, China.

  • Allenby, G. M., & Rossi, P. E. (2006). Hierarchical bayes models: A practitioner’s guide. In R. Grover & M. Vriens (Eds.), The handbook of marketing research (pp. 418–440). Thousand Oaks, CA: Sage.

    Chapter  Google Scholar 

  • ASME (2008). ASME Mechanical Engineering Magazine, February Issue.

  • Baines, T., Lightfoot, H., Evans, S., Neely, A., Greenough, R., Peppard, J., et al. (2007). State-of-the-art in product-service systems. Proceedings of the IMechE, Part B: Journal of Engineering Manufacture, 221(10), 1543–1552.

    Article  Google Scholar 

  • Bazan, J. & Szczuka, M. (2000). RSES and RSESlib—A collection of tools for rough set computations, In Lecture Notes in Artificial Intelligence (pp. 592–601). Berlin, Heidelberg, Germany: Springer-Verlag.

  • Bazan, J., Nguyen, H. S., Nguyen, S. H., Synak, P., & Wróblewski, J. (2000). Rough set algorithms in classification problem. In L. Polkowski, S. Tsumoto, & L. Lin (Eds.), Rough set methods and applications (pp. 49–88). New York: Physica-Verlag.

    Chapter  Google Scholar 

  • Bechara, A., Damasio, H., Tranel, D., & Damasio, A. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275(5304), 1293–1295.

    Article  Google Scholar 

  • Bedford, T., & Cooke, R. M. (2001). Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial Intelligence, 32(1), 245–268.

    Article  Google Scholar 

  • Bell, D. E. (1982). Regret in decision-making under uncertainty. Operations Research, 30(5), 961–981.

    Article  Google Scholar 

  • Bell, D. E. (1985). Disappointment in decision-making under uncertainty. Operations Research, 33(1), 1–27.

    Article  Google Scholar 

  • Brandstätter, E., Gigerenzer, G., & Hertwig, R. (2006). The priority heuristic: Making choices without trade-offs. Psychological Review, 113(2), 409–432.

    Article  Google Scholar 

  • Brown, A. S. (2008). The new point of view: Focus on design for human factors. February Issue: ASME Mechanical Engineering Magazine.

  • Brown, S. D., & Heathcote, A. J. (2003). Averaging learning curves across and within participants. Behaviour Research Methods Instruments and Computers, 35(1), 11–21.

    Article  Google Scholar 

  • Camerer, C. F. (1998). Bounded rationality in individual decision making. Experimental Economics, 1(2), 163–183.

    Article  Google Scholar 

  • Camerer, C. F., Loewenstein, G., & Rabin, M. (2003). Advances in behavioral economics. New York and Princeton: Russell Sage Foundation Press and Princeton University Press.

  • Cho, C.K., Kim, Y.S., & Lee, W.J. (2010). Economical, ecological and experience values for product-service systems. In The 7th design and emotion conference, Oct 4–7, Chicago.

  • Cohen, A. L., Sanborn, A. N., & Shiffrin, R. M. (2008). Model evaluation using grouped or individual data. Psychonomic Bulletin and Review, 15(4), 692–712.

    Article  Google Scholar 

  • Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and human brain. New York, USA: Gosset/Putnam Press.

    Google Scholar 

  • de Campos, C.P., & Ji, Q. (2008). Improving Bayesian network parameter learning using constraints. In The 19th international conference on pattern recognition, Dec 8–11, Tampa.

  • Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting with Computers, 21(1–2), 133–145.

    Article  Google Scholar 

  • Falcioni, J. G. (2008). Make it work. February Issue: ASME Mechanical Engineering Magazine.

  • Fodor, I.K. (2002). A survey of dimension reduction techniques. LLNL Technical Report, UCRL-ID-148494, Lawrence Livermore National Laboratory.

  • Ford (2011). www.ford.com/technology/sync/myfordtouch. Accessed 27 Jan 2011.

  • Frantzidis, C. A., Bratsas, C., Klados, M. A., Konstantinidis, E., Lithari, C. D., Vivas, A. B., et al. (2010). On the classification of emotional biosignals evoked while viewing affective pictures: An integrated data-mining-based approach for healthcare applications. IEEE Transactions on Information Technology in Biomedicine, 14(2), 309–318.

    Article  Google Scholar 

  • Fukuda, S. (2011). Emotional engineering: Service development. London: Springer.

    Book  Google Scholar 

  • Gora, G., & Wojna, A. (2002). RIONA: A new classification system combining rule induction and instance-based learning. Fundamenta Informaticae, 51(4), 369–390.

    Google Scholar 

  • Gould, D. (2010). Product ecosystems and service design. http://www.psfk.com/2010/01/product-ecosystems-and-service-design.html. Accessed 27 Sept 2013.

  • Gratch, J., & Marsella, S. (2004). A domain-independent framework for modeling emotion. Cognitive Systems Research, 5(4), 269–306.

    Article  Google Scholar 

  • Han, B., Chen, X.-W., Wang, X., & Michaelis, E.K. (2009). Integrating multiple microarray data for cancer pathway analysis using bootstrapping K-S test. Journal of Biomedicine and Biotechnology, Article ID 707580.

  • Hassenzahl, M. (2004). The interplay of beauty, goodness and usability in interactive products. Human Computer Interaction, 19(4), 319–349.

    Article  Google Scholar 

  • Hassenzahl, M., & Tractinsky, N. (2006). User experience—A research agenda. Behaviour and Information Technology, 25(2), 91–97.

    Article  Google Scholar 

  • Heckerman, D. (1998). A tutorial on learning with Bayesian networks. In M. Jordan (Ed.), Learning in graphical models (pp. 301–354). Boston, USA: Kluwer Academic.

    Chapter  Google Scholar 

  • Helander, M.G., Khalid, H.M., & Peng, H. (2007). Citarasa engineering for affective design of vehicles. In The IEEE international conference on industrial engineering and engineering management, Dec 2–5, Singapore.

  • Hudlicka, E. (2004). Beyond cognition: Modeling emotion in cognitive architectures. In Proceedings of the international conference on cognitive modeling (pp.118–123), Pittsburgh.

  • Ingarden, R. (1985). Selected papers in aesthetics. Washington, DC: Catholic University of America Press.

    Google Scholar 

  • Ingersoll, J. (2008). Non-monotonicity of the Tversky-Kahneman probability-weighting function: A cautionary note. European Financial Management, 14(3), 385–390.

    Article  Google Scholar 

  • Jiao, R.J. (2011). Prospect of design for mass customization and personalization. In ASME international design engineering technical conferences and computers and information in engineering conference, DETC20011-48919, Aug 28–31, Washington DC.

  • Jiao, J., Xu, Q., Du, J., Zhang, Y., Helander, M. G., Khalid, H. M., et al. (2007). Analytical affective design with ambient intelligence for mass customization and personalization. International Journal of Flexible Manufacturing Systems, 19(4), 570–595.

    Article  Google Scholar 

  • Kahneman, D. (2000). Experienced utility and objective happiness: A moment-based approach. In D. Kahneman (Ed.), Choices, values, and frames (pp. 673–692). New York, USA: Cambridge University Press.

    Google Scholar 

  • Kahneman, D. (2002). Maps of bounded rationality: A perspective on intuitive judgment and choice, Nobel Prize Lecture. Princeton, USA: Princeton University.

    Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 1251–1289.

    Article  Google Scholar 

  • Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives. New York, USA: Wiley.

    Google Scholar 

  • Law, E.L.C., Roto, V., Hassenzahl, M., Vermeeren, A.P.O.S., & Kort, J. (2009). Understanding, scoping and defining user experience: A survey approach. In The 27th international conference on Human factors in computing systems, April 4–9, Boston, MA.

  • Lewis, K., Chen, W., & Schmidt, L. (2006). Decision making in engineering design. New York: ASME Press.

  • Liebscher, E. (2008). Construction of asymetric multivariate copulas. Journal of Multivariate Analysis, 99(10), 2234–2250.

    Article  Google Scholar 

  • Lin, J. & Seepersad, C.C. (2007). Empathic lead users: The effects of extraordinary user experiences on customer needs analysis and product redesign. In ASME international design engineering technical conferences and computers and information in engineering conference, DETC2007-35302, Sept 4–7, Las Vegas, Nevada.

  • Lisetti, C. L. & Nasoz, F. (2002). MAUI: A multimodal affective user interface. In Proceedings of the 10th ACM international conference on multimedia (pp.161–170), Juan les Pins, France.

  • Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. J. Davidson, H. H. Goldsmith, & K. R. Scherer (Eds.), Handbook of affective science (pp. 619–642). New York: Oxford University Press.

    Google Scholar 

  • Louviere, J. J., Hensher, D. A., & Swait, J. (2000). Stated choice methods: Analysis and application. Cambridge: Cambridge University Press.

  • Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). WinBUGS—A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10(4), 325–337.

  • Marinier, R. P, I. I. I., Laird, J. E., & Lewis, R. L. (2009). A computational unification of cognitive behavior and emotion. Cognitive Systems Research, 10(1), 48–69.

    Article  Google Scholar 

  • Martinez, A. M., & Kak, A. C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 228–233.

    Article  Google Scholar 

  • Mellers, B. A. (2000). Choice and relative pleasure of consequences. Psychological Bulletin, 126(6), 910–924.

    Article  Google Scholar 

  • Morillas, P. M. (2005). A method to obtain new copulas from a given one. Metrika, 61(2), 169–184.

    Article  Google Scholar 

  • MRC (2012). The BUGS (Bayesian inference Using Gibbs Sampling) project. http://www.mrc-bsu.cam.ac.uk/bugs/. MRC Biostatistics, Cambridge.

  • Nagamachi, M. (1995). Kansei engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics, 15(1), 3–11.

    Article  Google Scholar 

  • NAP (2007). Human-System integration in the system development process: A new look. Washington, DC: National Research Council of The National Academies, The National Academies Press. http://www.nap.edu/catalog.php?record_id=11893.

  • Neal, R.M. (1993). Probabilistic inference using Markov chain Monte Carlo Methods. Technical Report CRG-TR-93-1, University of Toronto, Toronto, Canada.

  • NSF (2009). NSF Design Workshop on Spanning Design Boundaries. www.segal.northwestern.edu/designworkshop. Accessed 20 Sept 2011.

  • Okhrin, O. (2012). Fitting high-dimensional copulae to data. In J.-C. Duan, J. E. Gentle, & W. Härdle (Eds.), Handbook of computational finance (pp. 469–501). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Palmitesta, P., & Provasi, C. (2005). Aggregation of dependent risks using the Koehler Symanowski copula function. Computational Economics, 25(1–2), 189–205.

    Article  Google Scholar 

  • Papalambros, P. Y. (2010). The human dimension. ASME Journal of Mechanical Engineering, 132(5), 1.

    Google Scholar 

  • Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Berlin: Springer.

    Book  Google Scholar 

  • Picard, R. W. (1997). Affective computing. Cambridge, MA: The MIT Press.

    Book  Google Scholar 

  • Picard, R. W., & Klein, J. (2002). Computers that recognise and respond to user emotion: Theoretical and practical implications. Interacting with Computers, 14(2), 141–169.

    Article  Google Scholar 

  • Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., et al. (2004). Affective learning—A manifesto. BT Technology Journal, 22(4), 253–269.

    Article  Google Scholar 

  • Pine, B. J., & Gilmore, J. H. (1999). The experience economy. Boston, MA: Harvard Business School Press.

    Google Scholar 

  • Quiggin, J. (1982). A theory of anticipated utility. Journal of Economic Behavior and Organization, 3(4), 323–343.

    Article  Google Scholar 

  • Rae, J. (2006). Ruthless focus on the customer. Business Weekly, July 28.

  • Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin and Review, 12(4), 573–604.

    Article  Google Scholar 

  • Scheirer, J., Fernandez, R., Klein, J., & Picard, R. W. (2002). Frustrating the user on purpose: A step toward building an affective computer. Interacting with Computers, 14(2), 93–118.

  • Scherer, K., Schorr, A., & Johnstone, T. (2001). Appraisal processes in emotion: Theory, methods, research. New York, USA: Oxford University Press.

  • Schmidhuber, J. (1997). Low-complexity art. Leonardo, Journal of the International Society for the Arts, Sciences, and Technology, 30(2), 97–103.

    Google Scholar 

  • Simon, H. (1967). Motivational and emotional controls of cognition. Psychological Review, 74(1), 29–39.

    Article  Google Scholar 

  • Singh, S., Barto, A.G., & Chentanez, N. (2004). Intrinsically motivated reinforcement learning. In Proceedings of advances in neural information processing systems (pp. 1097–1104), Vancouver, British Columbia, Canada.

  • Smith, C. A., & Kirby, L. A. (2001). Toward delivering on the promise of appraisal theory. In K. Scherer, A. Schorr, & T. Johnstone (Eds.), Appraisal processes in emotion: Theory methods, research (pp. 121–138). New York, USA: Oxford University Press.

    Google Scholar 

  • Steyvers, M., Lee, M. D., & Wagenmakers, E.-J. (2009). A Bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology, 53(3), 168–179.

    Article  Google Scholar 

  • Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine, 16(3), 199–202.

    Google Scholar 

  • Storbeck, J., & Clore, G. L. (2007). On the interdependence of cognition and emotion. Cognition and Emotion, 21(6), 1212–1237.

    Article  Google Scholar 

  • Timmermans, T. (2010). On the (ir)relevance of prospect theory in modeling uncertainty in travel decisions. European Journal of Transport and Infrastructure Research, 10(4), 368–384.

    Google Scholar 

  • Train, K. (2003). Discrete choice methods with simulation. Cambridge: Cambridge University Press.

  • Tukker, A., & Tischner, U. (2006). Product-services as a research field: Past, present and future reflections from a decade of research. Journal of Cleaner Production, 14(17), 1552–1556.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.

    Article  Google Scholar 

  • von Neumann, J., & Morgenstern, O. (1953). Theory of games and economic behavior. Princeton, NJ: Princeton University Press.

  • Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35(2), 151–175.

    Article  Google Scholar 

  • Zhou, F. & Jiao, R.J., (2013). An augmented affective-cognition framework for usability studies of in-vehicle system user interface. In ASME design engineering technical conferences, DETC2013-13694, Portland, OR.

  • Zhou, F., Jiao, R. J., Chen, S., & Zhang, D. (2011b). A Case-Driven ambient intelligence system for elderly in-home assistance applications. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 41(2), 179–189.

    Article  Google Scholar 

  • Zhou, F., Jiao, R. J., Schaefer, D., & Chen, S. (2010). Hybrid association mining and refinement for affective mapping in emotional design. ASME Journal of Computing and Information Science in Engineering, 10(3), 0310101–0310109.

    Article  Google Scholar 

  • Zhou, F., Ji, Y., & Jiao, R. J. (2013). Affective and cognitive design for mass personalization: Status and prospect. Journal of Intelligent Manufacturing, 24(5), 1047–1069.

    Article  Google Scholar 

  • Zhou, F., Ji, Y., & Jiao, R. J. (2014). Prospect theoretic modeling of customer Affective–Cognitive decisions under uncertainty for user experience design. IEEE Transactions on Human-Machine Systems, 44(4), 468–483.

    Article  Google Scholar 

  • Zhou, F., Qu, X., Helander, M. G., & Jiao, J. (2011a). Affect prediction from physiological measures via visual stimuli. International Journal of Human-Computer Studies, 69(12), 801–819.

    Article  Google Scholar 

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  1. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Roger J. Jiao & Feng Zhou

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Chih-Hsing Chu

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  1. Roger J. Jiao
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Jiao, R.J., Zhou, F. & Chu, CH. Decision theoretic modeling of affective and cognitive needs for product experience engineering: key issues and a conceptual framework. J Intell Manuf 28, 1755–1767 (2017). https://doi.org/10.1007/s10845-016-1240-z

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