Study on Fitts Law by Human Computer Interaction Masters students in Georgia Tech

1. Breaking Fitts’ Law
Abhishek, Sahithya, Keenan, Xiao

2. Our Question.

3. Is it faster to click on
targets at the edge of
the screen?

8. Bounding line simulates edge of screen

9. Bounding line simulates edge of screen

10. Bounding line simulates edge of screen

17. Theoretical Underpinnings:
Targets at the edge of the screen
effectively have infinite width

18. 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.

19. W

20. W=∞

21. Objectified Question
Are movement times lower while
selecting targets at the edge of the screen
than predicted by Fitts’ law?

22. Additional Questions
Does the magnitude of effect vary based
on target size?

23. Hypothesis 1
Bounded mouse movements will be
faster than Fitts’ Law would predict.

24. Hypothesis 2
Bounded mouse movements will be
faster than identical unbounded
movements.

25. Design
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.

26. Independent Variables:
Presence of Bounding Box
Size of Target
Dependent Variable:
Observed Movement Time

27. Addressing Potential Confounds
Potential Confounds What We Controlled
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

28. 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

29. Data

30. Average Observed MT vs. Condition
t=-7.8984
p<0.05
t=-5.7272
p<0.05
Average (Observed MT)
t=0.1196
p=0.9
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

31. Correlation between Observed MT and Predicted MT
0.9
Correlation
0.7
0.5
0.3
0.1
No Bounding Box Bounding Box
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)

32. Observed MT vs. Predicted MT (Large targets with Bounding Box)
Data
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

33. Observed MT vs. Predicted MT (Large Targets with No Bounding Box)
Data
also a good fit for large targets with no bounding box

34. Observed MT vs. Predicted MT (Small targets with Bounding Box)
Data
interesting: these boxes tend to be a bit lower than the Fitts law trend line

35. Observed MT vs. Predicted MT (Small Targets with No Bounding Box)
Data
and here, Fitts law works pretty well again- the bounding box is gone, so it’s just the normal
task

36. 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

37. Findings
• There is a significant difference in movement time
between bounded and unbounded movements.
• This effect is only significant for small targets.

38. What would we do differently?
• 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

39. Next Steps
★ 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

40. External Validity
Chrome on Windows
Chrome on Mac OS

41. Questions?