Fitts' Law

Information capacity of discrete motor responses.

Fitts, Paul M.; Peterson, James R.

Journal of Experimental Psychology. 1964 Feb Vol 67(2) 103-112

The information capacity of the human motor system in controlling the amplitude of movement.

Fitts, Paul M.

Journal of Experimental Psychology. 1954 Jun Vol 47(6) 381-391

Fitts' Law Demo

A Web-based Test of Fitts' Law

VR Fitts' Law study.

1. Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, 381-391.
   
   
2. Welford, A. T. (1968). Fundamentals of Skill. London: Methuen.
   
   
3. MacKenzie, I. S. (1989). A note on the information-theoretic basis for Fitts' law. Journal of Motor Behavior, 21, 323-330.
   
   
4. Mackenzie, I. S. Fitts' Law as a Performance Model in Human-Computer Interaction. PhD thesis, University of Toronto, 1991.
   
   
5. MacKenzie, I. S. and Buxton, W. Extending Fitts' Law to Two-Dimensional Tasks. In CHI '92 Conference of Human Factors in Computing Systems, 1992, pp. 219-226.

   Summary: Fitts' Law is designed for motion in one dimension only. However, most practical applications of human movement involve motion in at least two dimensions. With rectangle shaped targets, D is still the distance to the center of the target, but it is unclear what to use for W. If the approach angle creates a vector parallel to one of the sides, it is simple to choose the value for W. However, if this is not the case, there are four other models to consider: W+H, W*H, SMALLER-OF, and W'. The W+H and W*H models did not have significantly better correlations than the STATUS QUO model. The SMALLER-OF model chooses the smaller of the two dimensions to use as W, indicating the accuracy demands of the task. W' uses the width of the target along an approach vector, retaining the one dimensionality of the model. When comparing these models with the STATUS QUO model, the correlations of the SMALLER-OF and W' models did not differ significantly from eachother, but each had a higher correlation than the STATUS QUO model.

6. MacKenzie IS. Movement time prediction in human-computer interfaces. [Conference Paper] Proceedings. Graphics Interface '92. Canadian Inf. Process. Soc. 1992, pp.140-50. Toronto, Ont., Canada.

MacKenzie, I. S., & Buxton, W. (1994). The prediction of pointing and dragging times in graphical user interfaces. Interacting with Computers, 6, 213-227.

Accot, J., & Zhai, S. (1997). Beyond Fitts' law: Models for trajectory-based HCI tasks. Proceedings of the CHI '97 Conference on Human Factors in Computing Systems, New York: ACM, pp. 295-302.

Peter Oel, Paul Schmidt & Alfred Schmitt(2001). Time Prediction of Mouse-based Cursor movements. AFIHM-BCS Conference on Human-Computer Interaction IHM-HCI'2001. Lille, France, Sept. 10-14, 2001. Volume II, 37-40

Summary: Fitts' Law is widely used as a predictor for movement time of mouse based cursor transfers between objects in a graphical user interface. However, Fitts' Law does not provide an accurate representation for low values of I nor for small target areas. This paper proposes a new power model that describes movement time within classes of different Ws. The best fit model is represented by MT=h(w)D^k(w). h(W) and k(W) are replaced by regression models that best fit the experimental data. The final power law is given by MT=(aW_b)D^c+dlog2(W)

Bibliography of Fitts’ Law Research

3D Games as Motivation in Fitts’ Law Experiments