Breaking Fitts' Law

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Breaking Fitts' Law

  1. 1. Breaking Fitts’ Law Abhishek, Sahithya, Keenan, Xiao
  2. 2. Our Question.
  3. 3. Is it faster to click on targets at the edge of the screen?
  4. 4. Bounding line simulates edge of screen
  5. 5. Bounding line simulates edge of screen
  6. 6. Bounding line simulates edge of screen
  7. 7. Theoretical Underpinnings: Targets at the edge of the screen effectively have infinite width
  8. 8. We used the Least-of method of determining target in two-dimensions, which MacKenzie and Buxton (1992) found to be comparable to the W’ Model (actual target depth along the approach vector). MacKenzie, I. S., & Buxton, W. (1992). Extending Fitts' law to two-dimensional tasks. Proceedings of the ACM Conference on Human Factors in Computing Systems - CHI '92, pp. 219-226. New York: ACM.
  9. 9. W
  10. 10. W = ∞
  11. 11. Are movement times lower while selecting targets at the edge of the screen than predicted by Fitts’ law? Objectified Question
  12. 12. Does the magnitude of effect vary based on target size? Additional Questions
  13. 13. Bounded mouse movements will be faster than Fitts’ Law would predict. Hypothesis 1
  14. 14. Bounded mouse movements will be faster than identical unbounded movements. Hypothesis 2
  15. 15. Simulate the edge of the screen with a ‘bounding box.’ Participants perform an identical set of pointing tasks with a bounding box and without one. Design
  16. 16. Independent Variables: Presence of Bounding Box Size of Target Dependent Variable: Observed Movement Time
  17. 17. Addressing Potential Confounds Screen Resolution Consistent at 1680x1050 Subject Distance from Screen Same chair height and distance from monitor Type of Mouse Use of identical Dell optical mouse Fatigue Breaks after 25 trials Order Effects Randomized trials to eliminate order effects Device LCD with identical calibration and constrast Starting Position Always in the center of the screen Potential Confounds What We Controlled
  18. 18. Methodology 1680x1050 Resolution 22” Display 2 Foot distance from Display Targets are 1º and 1.2º of Visual Angle Dell optical mouse Randomized order of trials 10 second break after 25 trials to reduce fatigue Bright green targets on black background Pink bounding box Trial time = Time from start until successful click 0.5s fixation time as cursor is auto-centered. Cursor always starts at center of screen 8 varying target distances Two distinct target sizes Same set of targets 4 participants
  19. 19. Data
  20. 20. t=-5.7272 p<0.05 t=0.1196 p=0.9 t=-7.8984 p<0.05 Condition Average(ObservedMT) Average Observed MT vs. Condition significant difference between bounded MT and unbounded MT. almost 100 ms difference. bounding versus no bounding is not significant for large targets, but, for small targets, the effect is significant, and is close to 100ms
  21. 21. Correlation No Bounding Box Bounding Box 0.9 0.7 0.5 0.3 0.1 Correlation between Observed MT and Predicted MT so, does Fitts law still work? We were trying to break it. It works very well when there is no bounding box (around .93), and it still works fairly well when there is a bounding box (around .83)
  22. 22. Data Observed MT vs. Predicted MT (Large targets with Bounding Box) This is a line representing what Fitts law predicts, and box plots for all of the observed MTs at each index of difficulty. pretty good fit for large targets with bounding box
  23. 23. Data Observed MT vs. Predicted MT (Large Targets with No Bounding Box) also a good fit for large targets with no bounding box
  24. 24. Data Observed MT vs. Predicted MT (Small targets with Bounding Box) interesting: these boxes tend to be a bit lower than the Fitts law trend line
  25. 25. Data Observed MT vs. Predicted MT (Small Targets with No Bounding Box) and here, Fitts law works pretty well again- the bounding box is gone, so it’s just the normal task
  26. 26. Differences of Observed Time and Predicted Time So, there is no significant difference between bounding box and no bounding box across all targets, although we were a bit faster with the bounding box for small targets, there is a highly significant difference between predictions and observed times for small targets with a bounding box, but not with no bounding box. With no
  27. 27. • There is a significant difference in movement time between bounded and unbounded movements. • This effect is only significant for small targets. Findings
  28. 28. • Instruct participants on how to approach the target, in order to control for the effects of strategic differences • careful aiming versus quick movements • We did not remove outliers, and our averages may have been skewed by such points What would we do differently?
  29. 29. ★ Perform test on tablet with physical bounding boxes ★ Add additional target sizes between small (20 pixels) and large (100 pixels) to find out when our effect becomes significant. ★ Test for External Validity: Compare differences in tab switching time between browsers Next Steps
  30. 30. Chrome on Windows Chrome on Mac OS External Validity
  31. 31. Questions?

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