Fitts' Law as a Research and Design Tool in Human-Computer Interaction

TLDR

Fitts' law models human movement as information transmission and has been widely adopted across kinematics, human factors, and human‑computer interaction. The study offers a historical and theoretical context for Fitts' law, analyzes systematic deviations from predictions, and proposes a more theoretically sound index of task difficulty. The authors refine Fitts' law by proposing a theoretically sound index of task difficulty and evaluate its predictive utility across six studies using devices such as mouse, trackball, joystick, touchpad, helmet‑mounted sight, and eye tracker. The analysis reveals large inconsistencies across studies, and the authors identify experimental variations that explain these differences.

Abstract

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.