Developing Quantitative Metrics to Predict Users' Perceptions of Interface Design

Abstract

Users' perceptions of an interface play an important role in influencing their acceptance of a product. Currently, however, designers lack an objective method to predict users' perceptions and to guide decisions during the design process. We propose a statistical image analysis method that takes the user interface as an input and computes image features that can be used to inform design. Based on these metrics, we then implemented a supervised learning algorithm to predict users' perceptions. We tested this method with a case study involving car infotainment systems. The results showed that the algorithm can capture users' perceptual ratings quite well, in particular for the perceptions of clutter vs. organized, and aggressive vs. calm. We discuss the implications of this computational method for UI design.

Get full access to this article

View all access options for this article.

References

Chuang

M. C.

Chang

C. C.

Hsu

S. H.

(2001). Perceptual factors underlying user preferences toward product form of mobile phones. International Journal of Industrial Ergonomics, 27, 247–258.

Fogg

B. J.

Soohoo

Danielson

D. R.

Marable

Stanford

Tauber

E. R.

(2003). How do users evaluate the credibility of Web sites?: A study with over 2,500 participants Proceedings of the 2003 conference on Designing for user experiences.

Freund

Iyer

Schapire

Singer

(2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933–969.

Itti

Koch

Niebur

(1998). A model of saliency-based visual attention for rapid scene analysis IEEE Transaction of Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.

Leung

Malik

(2001). Representing and recognizing the visual appearance of materials using three- dimensional textons. International Journal of Computer Vision, 43(1), 29–44.

Liu

(2003). Engineering aesthetics and aesthetic Ergonomics: Theoretical foundations and a dual-process research methodology. Ergonomics, 46, 1273–1292.

Nagamachi

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

Norman

(2004). Emotional design: Why we love (or hate) everyday things. New York: Basic Books.

Oliva

Torralba

(2006). Building the gist of a scene: The role of global image features in recognition. Progress in Brain Research, 155, 23–36.

10.

Osgood

C. E.

Suci

G.J.

Tannenbaum

P.H.

(1957). The Measurement of Meaning. Urbana, IL: Illinois Press.

11.

Reinagel

Zador

(1999). Natural scene statistics at the center of gaze. Network, 10, 341–350.

12.

Renninger

L. W.

Coughlan

J.M.

Verghese

Malik

(2005). An information maximization model of eye movements. Advances in Neural Information Processing Systems, 17, 1121–1128.

13.

Rosenholtz

Mansfield

Jin

(2005). Feature congestion: A measure of display clutter Proceedings of the SIGCHI conference on Human factors in computing systems 761–770.

14.

Shannon

C. E.

(1948). A mathematical theory of communication. Bell System Tech. Journal, 27, 379–423.

15.

Thorpe

Fize

Marlot

(1996). Speed of processing in the human visual system. Nature, 381, 520-522.

16.

Tidwell

(2005). Designing interfaces: Patterns for effective interaction design: O'Reilly.

17.

Torralba

Oliva

(2003). Statistics of natural image categories. Network: computation in neural systems, 14, 391–412.

18.

Zheng

Lin

Zapf

Knapheide

(2007). Visualizing user experience through “perceptual maps”: Concurrent assessment of perceived usability and subjective appearance in car infotainment systems. In Duffy

V. G.

(Ed.), Digital Human Modeling (pp. 536–545). Verlag Berlin Heidelberg: Springer.