Fitts’ Law Rob Diaz-Marino

Overview  The Basics Who invented it? Who invented it? What does it model? What does it model? How is it used in HCI? How is it used in HCI?  Fitts’ Adaptation The Formula The Formula Drawbacks Drawbacks A Standardized Test A Standardized Test  An Example  Synthesis  Conclusion

Who invented it?  Paul Fitts (in 1954)  Psychologist at Ohio State University  US Air Force Aviation safety Aviation safety

What does it Model?  Info Capacity of Human Motor system  Ability to guide pointing device to target Coarse movement Coarse movement Overshoot / Undershoot Overshoot / Undershoot Adjustment Adjustment  Based on Shannon’s Theorem 17

How is it used in HCI?  Predictive Formula  Rapid aimed motion with a Pointing Device Mouse, trackball, stylus, finger (on touch screen) etc. Mouse, trackball, stylus, finger (on touch screen) etc.  Estimates Time to acquire target Time to acquire target Difficulty of acquiring target Difficulty of acquiring target

Shannon’s Theorem 17  Formula (From CPSC 441)  Maximum effective transmission capacity  Applied in Networking Capacity = Maximum effective transmission capacity for a medium Bandwidth = Physical transmission capacity Signal Power = Strength of the signal being carried Noise Power = Strength of noise interference over the medium Log base 2 because transmitting binary (2 signal levels: 0 and 1)

Fitts’ Adaptation  Human Motor System = Medium  Human Perception = Channel  Target Distance = Signal  Target Width = Noise  Bandwidth = Index of Performance  Log base 2?

The Formula Movement Time (s) (constant) Minimum reaction time (s) Index of Difficulty (bits) AmplitudeWidth (constant) Index of Performance (Time/bit)

Variations Welford (1960; 1968, p. 147) MacKenzie (1989, p. 324)

Amplitude and Width A W

A W

A W

Drawbacks  Formula doesn’t always work Ex. For easy tasks, sometimes MT < 0 Ex. For easy tasks, sometimes MT < 0  Not always accurate Different people, different “bandwidths” Different people, different “bandwidths” Learning effects Learning effects

Drawbacks (2)  Different pointing devices, different times Mouse vs. Trackball Mouse vs. Trackball  Solution: Adjust with constants a & b Adjust with constants a & b

A Standardized Test ISO : Picture from Gutwin (2002) Regression line used on empirical results to determine a and b.

An Example  Which interface is more efficient for browsing?

An Example  Which interface is more efficient for browsing?

An Example (2) A = 25cm W = 2cm From previous experiments with a mouse: a = 230ms, b = 166 ms/bit

An Example (3) A = 5cm W = 2cm From previous experiments with a mouse: a = 230ms, b = 166 ms/bit

An Example (3)  Difference of ms  Worst case – Delete every 2 nd photo  sec / 100 photos  5 min 57 sec / 1000 photos  Is redesign worth it?

Synthesis  To reduce target acquisition time: Decrease target distance Decrease target distance Increase target size Increase target size Consider direction of approach Consider direction of approach

Conclusion  Cheap quantitative metric  Judges GUI design & efficiency  Not exact – rough estimate  Applicable to pointing devices  Models “hand-to-eye” coordination