Fitts' Law Basics

Fitts’ Law Basics Lucas Rizoli CPSC 544, September 2006

Paul Fitts 1954 Image from http://www.psychology.ru/whoswho/Paul_Fitts.stm

Serial tapping task Image from http://www.tele-actor.net/fitts/fitts_background.html

Fitts’ Law

Fitts’ Law a : Intercept b : Slope A : Amplitude W : Width ID : Index of difficulty

Time Index of difficulty Intercept Slope (ms/bits)

Index of performance Bits/ms Bandwidth Comparable across devices/tasks

Limits of Fitts’ Strange results with small A One-dimensional Pointing only

Fitts’ original ID Multiplied by 2 to avoid negative ID Problematic when A < W/2

Better fit to data than Fitts’ original Possible negative ID Welford’s ID

Claude Shannon 1948 Image from http://www.daviddarling.info/encyclopedia/S/Shannon.html

Info Capacity

Shannon formulation Best fit to data Positive ID Follows from Info Theory

W W Target Area A H A

Fitts’ in two dimensions Image from MacKenzie, I. S. and Buxton, W. 1992. Extending Fitts' law to two-dimensional tasks. What to use as W ? Status-quo ( W ) Smaller-of (min( H , W )) Approach ( W ´ ) Perimeter ( H + W ) Area ( H * W )

Appeal of Smaller-of Significantly better than most Simpler than W ´

W:H v. MT Image from Accot, J. and Zhai, S. 2003. Refining Fitts' law models for bivariate pointing.

Bivariate pointing

Implications of BP Law Third empirical parameter Ideal W:H ratio for rect. areas Directional stability v. “landing”

Accot & Zhai 1997 Image from Accot J. and Zhai S. 1997. Beyond Fitts' law: Models for trajectory-based HCI tasks.

Application of steering Evaluate non-pointing tasks Performance on changing path Image from Accot J. and Zhai S. 1997. Beyond Fitts' law: Models for trajectory-based HCI tasks.

Questions?

Difficult target? Fastest location? Most forgiving target area?

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Fitts' Law Basics

by Lucas Rizoli on Oct 17, 2007

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Fitts’ Law Basics — Presentation Transcript