The Influence of Matching Degree of the User’s Inherent Mental Model and the Product’s Embedded Mental Model on the Mobile User Experience

  • Tian LeiEmail author
  • Xu Liu
  • Lei Wu
  • Ziliang Jin
  • Yuhui Wang
  • Shuaili Wei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9732)


A good user experience requires that the feedback generated by gestures is consistent with a user’s existing cognitive habits and his learnt Mental Model. However, it remains unclear that to what extent and in what ways the consistency between a user’s inherent Mental Model(UIMM) and a product’s embedded Mental Model (PEMM) can affect a user’s operating experience. This paper, by making two experiments, has explored the extent and the way in which the consistency between PEMM and UIMM influences the user experience. The results manifest that: (1) there is a high correlation between the two Mental Models’ matching degree and the user experience. When the consistency, the matching degree between the two Mental Models, is high, a user’s perception about the product’s usability is also high; on the contrary, the user will feel a low product usability and a low user experience; (2) there is a significant correlation between the two Mental Models’ matching degree and the task type. It is the tasks of “browsing news” and “adding comments”, especially the former, that have a higher matching degree between UIMM and PEMM, and there is a lower one in the tasks of “viewing the detailed information”, “viewing the comments” and “sharing the news”. It shows that there is a bigger difference between users and designers in these three tasks.


Inherent mental model Embedded mental model Mobile user experience Consistency 



This paper is supported by the HUST high-level international curriculum projects.


  1. 1.
    Craik, K.: The Nature of Explanation. Cambridge University Press, Cambridge (1943)Google Scholar
  2. 2.
    Johnson Laird, P.N.: Mental models: towards a cognitive science of language, inference and consciousness. In: Inference & Consciousness Cognitive Science, pp. 481–500. Harvard University Press (1983)Google Scholar
  3. 3.
    Young, I.: Mental Models: Aligning Design Strategy with Human Behavior. Rosenfeld Media, New York (2008)Google Scholar
  4. 4.
    Miwa, K., Kanzaki, N., Terai, H., Kojima, K., Nakaike, R., Morita, J., Saito, H.: Learning mental models of human cognitive processing by creating cognitive models. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds.) AIED 2015. LNCS, vol. 9112, pp. 287–296. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Potosnak, K.: Mental Model: Helping Users Understand Software. IEEE Software, 6(5), 85–86, 88 (1989)Google Scholar
  6. 6.
    Norman, D.A.: The Design of Everyday Things. Doubleday Business, New York (1990)Google Scholar
  7. 7.
    Cooper, A., Reimann, R.M.: About Face 3.0: The Essentials of Interaction Design. Wiley, New York (2007)Google Scholar
  8. 8.
    Sasse, M.A.: Eliciting and Describing Users’ Models of Computer Systems. University of Birmingham (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tian Lei
    • 1
    Email author
  • Xu Liu
    • 1
  • Lei Wu
    • 1
  • Ziliang Jin
    • 1
  • Yuhui Wang
    • 1
  • Shuaili Wei
    • 2
  1. 1.Department of Industrial DesignHuazhong University of Science and TechnologyWuhanChina
  2. 2.Jingdong ShangKe Information Technology Co., LTDShanghaiChina

Personalised recommendations