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.
25. 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
27. 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
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
31. 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)
32. 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
33. Data
Observed MT vs. Predicted MT (Large Targets with No Bounding Box)
also a good fit for large targets with no bounding box
34. 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
35. 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
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. • There is a significant difference in movement time
between bounded and unbounded movements.
• This effect is only significant for small targets.
Findings
38. • 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?
39. ★ 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
