Breaking Fitts’ LawAbhishek, Sahithya, Keenan, Xiao

Our Question.

Is it faster to click on targets at the edge of

the screen?

Bounding line simulates edge of screen

Bounding line simulates edge of screen

Bounding line simulates edge of screen

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

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.

W

W = ∞

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

Objectified Question

Does the magnitude of effect vary based on target size?

Additional Questions

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

Hypothesis 1

Bounded mouse movements will be faster than identical unbounded movements.

Hypothesis 2

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

Independent Variables:

Presence of Bounding BoxSize of Target

Dependent Variable:

Observed Movement Time

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

Methodology1680x1050 Resolution22” Display2 Foot distance from DisplayTargets are 1º and 1.2º of Visual AngleDell optical mouseRandomized order of trials10 second break after 25 trials to reduce fatigueBright green targets on black backgroundPink bounding boxTrial time = Time from start until successful click0.5s fixation time as cursor is auto-centered.Cursor always starts at center of screen8 varying target distancesTwo distinct target sizesSame set of targets4 participants

Data

t=-5.7272p<0.05

t=0.1196p=0.9

t=-7.8984p<0.05

Condition

Aver

age

(Obs

erve

d M

T)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

Corre

latio

n

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)

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

Data

Observed MT vs. Predicted MT (Large Targets with No Bounding Box)

also a good fit for large targets with no bounding box

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

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

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 bounding box, we think that there may have been outliers that made this average so high, even though there was no significant difference.

Finall, for large targets there were no significant differences between predictions and observations.

• There is a significant difference in movement time between bounded and unbounded movements.

• This effect is only significant for small targets.

Findings

• 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?

★ 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

Chrome on Windows

Chrome on Mac OS

External Validity

Questions?