Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Visualization Psychology for Eye Tracking Evaluation (2204.12860v2)

Published 27 Apr 2022 in cs.HC

Abstract: Technical progress in hardware and software enables us to record gaze data in everyday situations and over long time spans. Among a multitude of research opportunities, this technology enables visualization researchers to catch a glimpse behind performance measures and into the perceptual and cognitive processes of people using visualization techniques. The majority of eye tracking studies performed for visualization research is limited to the analysis of gaze distributions and aggregated statistics, thus only covering a small portion of insights that can be derived from gaze data. We argue that incorporating theories and methodology from psychology and cognitive science will benefit the design and evaluation of eye tracking experiments for visualization. This book chapter provides an overview of how eye tracking can be used in a variety of study designs. Further, we discuss the potential merits of cognitive models for the evaluation of visualizations. We exemplify these concepts on two scenarios, each focusing on a different eye tracking study. Lastly, we identify several call for actions.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. ACT-R: A theory of higher level cognition and its relation to visual attention. Human–Computer Interaction, 12(4):439–462, 1997.
  2. Pair analytics: Capturing reasoning processes in collaborative visual analytics. In 2011 44th Hawaii International Conference on System Sciences, pages 1–10, 2011.
  3. Visual analysis and coding of data-rich user behavior. In 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 141–150, 2016.
  4. State-of-the-art of visualization for eye tracking data. In EuroVis (STARs), 2014.
  5. Visualization of eye tracking data: A taxonomy and survey. Comput. Graph. Forum, 36(8):260–284, 2017.
  6. Beyond memorability: Visualization recognition and recall. IEEE Transactions on Visualization and Computer Graphics, 22(1):519–528, 2016.
  7. The challenges of designing metro maps. In N. Magnenat-Thalmann, P. Richard, L. Linsen, A. C. Telea, S. Battiato, F. H. Imai, and J. Braz, editors, Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pages 197–204. SciTePress, 2016.
  8. Data analysis strategies for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 15(2):195–207, 1993.
  9. The keystroke-level model for user performance time with interactive systems. Communications of the ACM, 23(7):396–410, 1980.
  10. S. Carpendale. Evaluating information visualizations. In A. Kerren, J. T. Stasko, J. Fekete, and C. North, editors, Information Visualization - Human-Centered Issues and Perspectives, volume 4950 of Lecture Notes in Computer Science, pages 19–45. Springer, 2008.
  11. A. T. Duchowski. Eye Tracking Methodology - Theory and Practice, Third Edition. Springer, 2017.
  12. Retrospective think-aloud method: Using eye movements as an extra cue for participants’ verbalizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’11, page 1161–1170, New York, NY, USA, 2011. Association for Computing Machinery.
  13. G. Ellis and A. Dix. An explorative analysis of user evaluation studies in information visualisation. In Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization, BELIV ’06, page 1–7, New York, NY, USA, 2006. Association for Computing Machinery.
  14. Visual analytics as a translational cognitive science. Topics in Cognitive Science, 3(3):609–625, 2011.
  15. Eye tracking for visualization evaluation: Reading values on linear versus radial graphs. Information Visualization, 10(3):182–195, 2011.
  16. Attention and visual memory in visualization and computer graphics. IEEE Transactions on Visualization and Computer Graphics, 18(7):1170–1188, 2012.
  17. M. Hegarty. The cognitive science of visual-spatial displays: Implications for design. Topics in Cognitive Science, 3(3):446–474, 2011.
  18. Thinking about the weather: How display salience and knowledge affect performance in a graphic inference task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(1):37, 2010.
  19. C. Heine. Towards modeling visualization processes as dynamic Bayesian networks. IEEE Transactions on Visualization and Computer Graphics, 27(2):1000–1010, 2021.
  20. A graph reading behavior: Geodesic-path tendency. In 2009 IEEE Pacific Visualization Symposium, pages 137–144, 2009.
  21. E. Hutchins. Distributed cognition. International Encyclopedia of the Social and Behavioral Sciences. Elsevier Science, 138, 2000.
  22. L. Itti and C. Koch. Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3):194–203, 2001.
  23. Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2):112–133, 2007.
  24. K. Koffka. Principles of Gestalt Psychology, volume 44. Routledge, 2013.
  25. Demo of the EyeSAC system for visual synchronization, cleaning, and annotation of eye movement data. In ACM Symposium on Eye Tracking Research and Applications, ETRA ’20 Adjunct, New York, NY, USA, 2020. Association for Computing Machinery.
  26. What we see and what we get from visualization: Eye tracking beyond gaze distributions and scanpaths, 2020.
  27. Eye tracking evaluation of visual analytics. Information Visualization, 15(4):340–358, 2016.
  28. C. Körner. Eye movements reveal distinct search and reasoning processes in comprehension of complex graphs. Applied Cognitive Psychology, 25(6):893–905, 2011.
  29. Eye movements indicate the temporal organisation of information processing in graph comprehension. Applied Cognitive Psychology, 28(3):360–373, 2014.
  30. Empirical studies in information visualization: Seven scenarios. IEEE Transactions on Visualization and Computer Graphics, 18(9):1520–1536, 2012.
  31. Workshop proposal: Visualization and the context of work-qualitative research methods for design deployment evaluation. Technical report, Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2011.
  32. E. Moseholm and M. D. Fetters. Conceptual models to guide integration during analysis in convergent mixed methods studies. Methodological Innovations, 10(2):2059799117703118, 2017.
  33. User performance and reading strategies for metro maps: An eye tracking study. Spatial Cognition & Computation, 17(1-2):39–64, 2017.
  34. Comparative eye-tracking evaluation of scatterplots and parallel coordinates. Visual Informatics, 1(2):118–131, 2017.
  35. D. Peebles. A cognitive architecture-based model of graph comprehension. In 11th International Conference on Cognitive Modeling, Berlin, pages 37–42, 2012.
  36. Cognitive ergonomics in visualization. In A. Ebert, G. C. van der Veer, G. Domik, N. D. Gershon, and I. Scheler, editors, Building Bridges: HCI, Visualization, and Non-formal Modeling, pages 80–94, Berlin, Heidelberg, 2014. Springer.
  37. Evaluation of visual analytics environments: The road to the visual analytics science and technology challenge evaluation methodology. Information Visualization, 13(4):326–335, 2014.
  38. B. Shneiderman and C. Plaisant. Strategies for evaluating information visualization tools: Multi-dimensional in-depth long-term case studies. In Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization, BELIV ’06, page 1–7, New York, NY, USA, 2006. Association for Computing Machinery.
  39. A. Treisman. Preattentive processing in vision. Computer Vision, Graphics, and Image Processing, 31(2):156–177, 1985.
  40. Using multi-dimensional in-depth long-term case studies for information visualization evaluation. In Proceedings of the 2008 Workshop on BEyond Time and Errors: Novel EvaLuation Methods for Information Visualization, BELIV ’08, New York, NY, USA, 2008. Association for Computing Machinery.
  41. S. Vogl. Integrating and consolidating data in mixed methods data analysis: Examples from focus group data with children. Journal of Mixed Methods Research, 13(4):536–554, 2019.
  42. D. Weiskopf. Vis4Vis: Visualization for (Empirical) Visualization Research, pages 209–224. Springer International Publishing, Cham, 2020.
  43. M. Wertheimer. Laws of organization in perceptual forms. A source book of Gestalt Psychology, 1, 1923.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Maurice Koch (7 papers)
  2. Kuno Kurzhals (14 papers)
  3. Michael Burch (6 papers)
  4. Daniel Weiskopf (57 papers)
Citations (5)
View on Semantic Scholar