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Cognition, Technology & Work

, Volume 19, Issue 2–3, pp 363–374 | Cite as

Web page attentional priority model

  • Jeremiah D. StillEmail author
Original Article

Abstract

Designing an interface that is both information rich and easy to search is challenging. Successfully finding a solution depends on understanding an interface’s explicit and implicit influences. A cognitively inspired computational approach is taken to make the implicit influences apparent to designers. A saliency model has already been shown to predict the deployment of attention within web page interfaces. It predicts regions likely to be salient, based on local contrast stemming from the bottom-up channels (e.g., color, orientation). This research replicates these previous findings and extends the work by proposing a web page-specific attentional priority (AP) model. This AP model includes previous interaction experience history, manifested as conventions, within the already valuable saliency model. These sources of influence automatically nudge our attention to regions that usually contain useful visual information. This research shows that, by integrating spatial conventions with a saliency model, designers can better predict the deployment of attention within web page interfaces.

Keywords

Computational model Eye movements Salience Human–computer interaction Design 

References

  1. Albert W (2002) Do web users actually look at ads? A case study of banner ads and eye tracking technology. In: Proceedings of usability professional association conferenceGoogle Scholar
  2. Arnheim R (1954) Art and visual perception: a psychology of the creative eye. University of California Press, BerkeleyGoogle Scholar
  3. Awh E, Belopolsky AV, Theeuwes J (2012) Top-down versus bottom-up attentional control: a failed theoretical dichotomy. Trends Cognit Sci 16:437–443CrossRefGoogle Scholar
  4. Baddeley AD (1992) Working memory. Science 255:556–559CrossRefGoogle Scholar
  5. Bradley S (2015) Design principles: dominance, focal points and hierarchy. Smashing Mag. Retrieved from https://www.smashingmagazine.com/2015/02/design-principles-dominance-focal-points-hierarchy/
  6. Burke M, Hornoff AJ (2001) The effects of animated banner advertisements on a visual search task. Computer and Information Science Report. University of Nantes, NantesCrossRefGoogle Scholar
  7. Buscher G, Cutrell E, Morris MR (2009) What do you see when you’re surfing? Using eye tracking to predict salient regions in web pages. In: Proceedings of the computer-human interaction conference, pp 21–30Google Scholar
  8. Chen M, Anderson JR, Sohn M (2001) What can a mouse cursor tell us more? Correlation of eye/mouse movements on web browsing. In: Proceedings of CHI: extended abstracts on human factors in computing systems, pp 281–282Google Scholar
  9. Chun MM (2000) Contextual cueing of visual attention. Trends Cognit Sci 4:170–177CrossRefGoogle Scholar
  10. Cowan N (1988) Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information processing system. Psychol Bull 104:163–191CrossRefGoogle Scholar
  11. Cowan N (2000) The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav Brain Sci 24:154–176Google Scholar
  12. Cutrell E, Guan Z (2007) What are you looking for? An eye-tracking study of information usage in web search. In: Proceedings of CHI conference on human factors in computing systems, pp 407–416Google Scholar
  13. Desimone R, Duncan J (1995) Neural mechanisms of selective visual attention. Annu Rev Neurosci 18:193–222CrossRefGoogle Scholar
  14. Faraday P (2000) Visually critiquing web pages. In: Proceedings of the 6th conference on human factors and the web, Austin, TXGoogle Scholar
  15. Fernandez-Duque D, Johnson ML (2002) Cause and effect theories of attention: the role of conceptual metaphors. Rev Gen Psychol 6:153–165CrossRefGoogle Scholar
  16. Flieder K, Modritscher F (2006) Foundations of pattern language based on gestalt principles. In: CHI: works-in-process, pp 773–778Google Scholar
  17. Grier R, Kortum P, Miller J (2007) How users view web pages: an exploration of cognitive and perceptual mechanisms. In: Zaphiris P, Kurniawan S (eds) Human computer interaction research in web design and evaluation. Information Science Reference, Hershey, pp 22–41CrossRefGoogle Scholar
  18. Harel J, Koch C, Perona P (2006) Graph-based visual saliency. In: Proceedings of neural information processing systems, pp 1–8Google Scholar
  19. Iqbal ST, Bailey BP (2008) Effects of intelligent notification management on users and their tasks. In: Proceedings of the CHI conference on human factors in computing systems, pp 93–102Google Scholar
  20. Itti L, Koch C (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res 40:1489–1506CrossRefGoogle Scholar
  21. Itti L, Koch C, Niebur E (1998) A model of saliency-based fast visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259CrossRefGoogle Scholar
  22. Jana A, Bhattacharya S (2015) Design and validation of an attention model of web page users. In: Advances in human-computer interaction, pp 1–14Google Scholar
  23. Johnson WA, Dark VJ (1986) Selective attention. Annu Rev Psychol 37:43–75CrossRefGoogle Scholar
  24. Jones B (2011) Understanding visual hierarchy in web design. Envato. Retrieved from http://webdesign.tutsplus.com/articles/understanding-visual-hierarchy-in-web-design–webdesign-84
  25. Kim MS, Cave KR (1999) Grouping effects on spatial attention in visual search. Gen Psychol 126:326–352CrossRefGoogle Scholar
  26. Malcolm GL, Henderson JM (2010) Combining top-down processes to guide eye movements during real-world scene search. J Vis 10:1–11CrossRefGoogle Scholar
  27. Masciocchi CM, Still JD (2013) Alternatives to eye tracking for predicting stimulus-driven attentional selection within interfaces. J Hum-Comput Inter 34:285–301Google Scholar
  28. McCarthy JD, Sasse MA, Riegelsberger J (2003) Can I have the menu please? An eyetracking study of design conventions. In: Proceedings of human-computer interaction, pp 401–414Google Scholar
  29. Moraglia G (1989) Display organization and the detection of horizontal line segments. Percept Psychophys 45:265–272CrossRefGoogle Scholar
  30. Navalpakkam V, Itti L (2005) Modeling the influence of task on attention. Vis Res 45:205–231CrossRefGoogle Scholar
  31. Nielsen J (2008) How little do users read? http://www.useit.com/altertbox/percent-text-read.html
  32. Norman DA, Shallice T (1986) Attention to action: Willed and automatic control of behavior. In: Davidson RJ, Schwartz GE, Shapiro D (eds) Consciousness and self-regulation: advances in research and theory, vol 4. Plenum Press, New York, pp 1–18Google Scholar
  33. Parkhurst D, Law K, Niebur E (2002) Modeling the role of salience in the allocation of overt visual attention. Vis Res 42:107–123CrossRefGoogle Scholar
  34. Pashler H (1988) Cross-dimensional interaction and texture segregation. Percept Psychophys 43:307–318CrossRefGoogle Scholar
  35. Rayer K (1998) Eye movements in reading and information processing: 20 years of research. Psychol Bull 124:372–422CrossRefGoogle Scholar
  36. Rensink RA (2002) Internal vs. external information in visual perception. In: Proceedings of the 2nd international symposium on smart graphics, pp 63–70Google Scholar
  37. Rosenholtz R, Dorai A, Freeman R (2011) Do predictions of visual perception aid design? ACM Trans Appl Percept 8:1–20CrossRefGoogle Scholar
  38. Still JD, Dark VJ (2010) Examining working memory load and congruency effects on affordances and conventions. Int J Hum Comput Stud 68:561–571CrossRefGoogle Scholar
  39. Still JD, Dark VJ (2013) Cognitively describing and designing affordances. J Des Stud 13:285–301CrossRefGoogle Scholar
  40. Still JD, Masciocchi CM (2010) A saliency model predicts fixations in web interfaces. In: Proceedings of the 5th international workshop on model-driven development of advanced user interactions, pp 25–18. Atlanta, GAGoogle Scholar
  41. Still JD, Masciocchi CM (2012) Considering the influence of visual saliency during interface searches. In: Alkhalifa EM, Gaid K (eds) Cognitively informed intelligent interfaces: system design and development. Information Science Reference, Hershey, pp 84–97CrossRefGoogle Scholar
  42. Still JD, Still ML, Grgic J (2015) Designing intuitive interactions: exploring performance and reflection measures. Interact Comput 27:271–286CrossRefGoogle Scholar
  43. Tatler BW (2007) The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor bases and image feature distributions. J Vis 14:1–17Google Scholar
  44. Theeuwes J (1992) Perceptual selectivity for color and form. Percept Psychophys 51:599–606CrossRefGoogle Scholar
  45. Theeuwes J (2004) Top-down search strategies cannot override attentional capture. Psychon Bull Rev 11:65–70CrossRefGoogle Scholar
  46. Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12:97–136CrossRefGoogle Scholar
  47. Walther D, Koch C (2006) Modeling attention to salient proto-objects. Neural Netw 19:1395–1407CrossRefzbMATHGoogle Scholar
  48. Wickens CD, McCarley JS (2008) Applied attention theory. CRC Press, Boca RatonGoogle Scholar
  49. Wolfe JM (2007) Guided search 4.0: current progress with a model of visual search. In: Gray W (ed) Integrated models of cognitive systems, Oxford, New York, pp 99–119Google Scholar
  50. Wolfe JM, Horowitz TS (2004) What attributes guide the deployment of visual attention and how do they do it? Nat Rev Neurosci 5:1–7. doi: 10.1038/nrn1411 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Department of PsychologyOld Dominion UniversityNorfolkUSA

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