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Perception of Average Value in Multiclass Scatterplots Michael Gleicher, Michael Correll, Christine Nothelfer, Steven Franconeri Perception of Average Value in Multiclass Scatterplots Michael Gleicher, Michael Correll , Christine Nothelfer,


  1. Perception of Average Value in Multiclass Scatterplots Michael Gleicher, Michael Correll, Christine Nothelfer, Steven Franconeri

  2. Perception of Average Value in Multiclass Scatterplots Michael Gleicher, Michael Correll , Christine Nothelfer, Steven Franconeri

  3. Perception of Average Value in Multiclass Scatterplots Michael Gleicher, Michael Correll , Christine Nothelfer, Steven Franconeri

  4. Which color is higher on average?

  5. Which color is higher on average?

  6. Which color is higher on average?

  7. Which color is higher on average?

  8. Which color is higher on average?

  9. Which color is higher on average?

  10. Summary How well can a general audience do this task? How do design choices affect performance?

  11. Visual Aggregation

  12. Why?

  13. Why?

  14. Why?

  15. Why?

  16. Why?

  17. Why?

  18. Anscombe’s Quartet 15 15 10 10 5 5 0 0 0 5 10 15 0 5 10 15 15 15 10 10 5 5 0 0 0 5 10 15 0 10 20

  19. Premises

  20. Premises

  21. Premises

  22. How Well Can We Aggregate?

  23. Perception of Aggregates

  24. Perception of Aggregates

  25. Methodologies

  26. Methods 11 between-subjects experiments 9 within-subjects experiments 600 participants (17,520 trials)

  27. Results

  28. Results

  29. Visual variables

  30. >

  31. Color is a stronger cue than shape > 79% 72%

  32. Results

  33. Irrelevant Cues

  34. ≈ 77% 80%

  35. Distractor class

  36. ≈ 78% 77%

  37. Distractor class and irrelevant cue

  38. >

  39. > 80% 76%

  40. Averaging is robust to visual complexity

  41. Averaging is robust to visual complexity

  42. Averaging is robust to visual complexity

  43. Averaging is robust to visual complexity

  44. Results

  45. Redundant cues

  46. ≈ 81% 80%

  47. Ongoing Work

  48. Ongoing Work

  49. Ongoing Work 100 100 80 80 60 60 40 40 20 20 0 0

  50. Ongoing Work 100 100 80 80 60 60 40 40 20 20 0 0

  51. Conclusion

  52. Acknowledgments This work was supported in part by NSF awards CMMI-0941013, BCS-1056730, SBE-1041707, DRL-0918409, DRL-1247262, and IIS-1162037 and NIH award R01 AU974787. Visit http://graphics.cs.wisc.edu/Vis/ScatterVis13/ for data tables, stimuli, and sample experiments.

  53. Extra Slides

  54. Eye Tracking Means 16 pixels apart Means 80 pixels apart

  55. Shape is a weaker cue than color

  56. Shape is a weaker cue than color 72.9% accuracy overall 79.4% accuracy overall

  57. Shape is a weaker cue than color 75% accuracy at 26.1 pixels apart 75% accuracy at 19.7 pixels apart

  58. Color vs. Shape

  59. Redundancy

  60. Δ parameter Δ =16 pixels Δ =80 pixels

  61. Δ parameter Δ =16 pixels Δ =80 pixels

  62. Δ parameter Δ =12 pixels Δ =80 pixels

  63. Shape is a weaker cue than color * 1 79.4% 0.8 72.9% Accuracy 0.6 0.4 0.2 0

  64. Averaging is robust to visual complexity

  65. Averaging is robust to visual complexity 1 80.1% 76.9% 0.8 Accuracy 0.6 0.4 0.2 0

  66. Averaging is robust to visual complexity

  67. Averaging is robust to visual complexity 1 78.2% 76.9% 0.8 Accuracy 0.6 0.4 0.2 0

  68. Averaging is robust to visual complexity 1 80.1% 78.2% 76.9% 76.9% 0.8 Accuracy 0.6 0.4 0.2 0

  69. Visual complexity still matters

  70. Visual complexity still matters * 1 79.5% 75.5% 0.8 Accuracy 0.6 0.4 0.2 0

  71. Visual complexity still matters * 1 80.1% 79.5% 78.2% 76.9% 76.9% 75.5% 0.8 Accuracy 0.6 0.4 0.2 0

  72. Redundant encodings don’t help

  73. Redundant encodings don’t (always) help 1 80.7% 79.7% 0.8 Accuracy 0.6 0.4 0.2 0

  74. Redundant encodings don’t (always) help

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