MacKenzie, I. S. (1992). Fitts' law as a research and design tool in human-computer interaction. Human-Computer Interaction, 7, 91-139. [PDF]
According to Fitts' law, human movement can be modeled by analogy to the transmission of information. Fitts' popular model has been widely adopted in numerous research areas, including kinematics, human factors, and (recently) human-computer interaction (HCI). The present study provides a historical and theoretical context for the model, including an analysis of problems that have emerged through the systematic deviation of observations from predictions. Refinements to the model are described, including a formulation for the index of task difficulty that is claimed to be more theoretically sound than Fitts' original formulation. The model's utility in predicting the time to position a cursor and select a target is explored through a review of six Fitts' law studies employing devices such as the mouse, trackball, joystick, touchpad, helmet-mounted sight, and eye tracker. An analysis of the performance measures reveals tremendous inconsistencies, making across-study comparisons difficult. Sources of experimental variation are identified to reconcile these differences.


1. Introduction
2. Summary of Fitts' Law
	2.1 Information Theory Foundation
	2.2 Equation by Parts
	2.3 Physical Interpretation
	2.4 Derivation from a Theory of Movement
3. Detailed Analysis
	3.1 The Original Experiments
	3.2 Problems Emerge
	3.3 Variations on Fitts' Law
	3.4 Effective Target Width
	3.5 Reanalysis of Fitts' Data
	3.6 Effective Target Amplitude
	3.7 Targets and Angles
4. Competing Models
	4.1 The Linear Speed-Accuracy Tradeoff
	4.2 Power Functions
5. Applications of Fitts' Law
	5.1 The Generality of Fitts' Law
	5.2 Review of Six Studies
		Card, English, and Burr (1978)
		Drury (1975)
		Epps (1986)
		Jagacinski and Monk (1985)
		Kantowitz and Elvers (1988)
		Ware and Mikaelian (1987)
	5.3 Across-Study Comparison of Performance Measures
	5.4 Sources of Variation
		Device Differences
		Task Differences
		Selection Technique
		Range of Conditions and Choice of Model
		Approach Angle and Target Width
		Error Handling
		Learning Effects
	5.5 Summary
6. Conclusions