Fitts' law

In ergonomics, Fitts' law (or Fitts's law) is a principle of human movement published in 1954 by Paul Fitts which predicts the time required to move from a starting position to a final target area. The kind of motion it describes is aimed and rapid. The time needed to acquire a target is a function of the distance to the target, and the size of the target. Fitts' law is used to model the act of pointing, both in the real world, for example, with a hand or finger and on computers, for example, with a mouse.
Contents 
The model
Mathematically, Fitts' law has been formulated a few different ways. One common form is the Shannon formulation (proposed by Scott MacKenzie, and named for its resemblance to Shannon's theorem), which, for movement along a single dimension, states:
 <math>T = a + b \log_2 \left(\frac{D}{W}+1\right)<math>
where
 T is the average time taken to complete the movement. (Traditionally, researchers have used the symbol MT for this, to mean movement time.)
 a and b are empirical constants, and can be determined by fitting a straight line to measured data.
 D is the distance from the starting point to the center of the target. (Traditionally, researchers have used the symbol A for this, to mean the amplitude of the movement.)
 W is the width of the target measured along the axis of motion. W can also be thought of as the allowed error tolerance in the final position, since the final point of the motion must fall within +/ W/2 of the target's centre.
From the equation, we see a speedaccuracy tradeoff associated with pointing, whereby targets that are smaller and/or further away require more time to acquire.
Success and implications of Fitts' law
Fitts' law is an unusually successful and wellstudied model. Experiments that reproduce Fitts' results, and/or demonstrate the applicability of Fitts' law in somewhat different situations, are not difficult to perform. The measured data in such experiments often fit a straight line with a correlation coefficient of 0.95 or higher, a sign that the model is very accurate.
Although Fitts only published two articles on his law (Fitts 1954, Fitts and Peterson 1964), hundreds of subsequent studies related to it are in humancomputer interaction (HCI) literature, and quite possibly thousands of studies published in the larger psychomovement literature. Fitts' law's first HCI application was by Card, English, and Burr (1978), who used the index of performance (IP) to compare performance of different input devices, with the mouse coming out on top. (This early work, according to Stuart Card's biography, "was a major factor leading to the mouse's commercial introduction by Xerox" [1] (http://www.bcshci.org.uk/interfaces/interfaces50.pdf).) Fitts' law has been shown to apply under a variety of conditions, with many different limbs (hands, feet, headmounted sights, eye gaze), manipulanda (input devices), physical environments (including underwater!), and user populations (young, old, retarded, and drugged participants). Note that the constants a, b, IP have different values under each of these conditions.
Since the advent of graphical user interfaces, Fitts' law has been applied to tasks where the user must position the mouse cursor over an onscreen target, such as a button or other widget. Fitts' law can model both pointandclick and draganddrop actions. (Note that dragging has a lower IP associated with it, because the increased muscle tension makes pointing more difficult.) Despite the model's appeal, it should be remembered that in its original and strictest form:
 applies only to movement in a single dimension and not to movement in two dimensions (though it is successfully extended to two dimensions in the AccotZhai steering law);
 describes simple motor response of, say, the human hand, failing to account for software acceleration usually implemented for a mouse cursor;
 describes untrained movements, not movements that are executed after months or years of practice (though some argue that Fitts' law models behaviour that is so low level, that extensive training doesn't make much difference).
If, as generally claimed, the law does hold true for pointing with the mouse, some consequences for user interface design:
 Buttons and other widgets to be selected in GUIs should be a reasonable size, it is very difficult to click on small ones.
 Edges (e.g. menubar in Mac OS) and corners of the computer display are particularly easy to acquire because the pointer remains at the screen edge regardless of how much further the mouse is moved, thus can be considered as having infinite width.
 Popup menus can usually be opened faster than pulldown menus, since the user avoids travel.
 Pie menu items typically are selected faster and have a lower error rate than linear menu items, for two reasons: because pie menu items are all the same, small distance from the centre of the menu, and their wedgeshaped target areas (which usually extend out to the screen edge) are very large.
Fitts' law remains one of the only hard, reliable humancomputer interaction predictive models, joined more recently by the AccotZhai steering law, which is derived from Fitts' law.
See also Hick's law, which models user decision making time.
Some mathematical details
The logarithm in Fitts' law is called the index of difficulty ID for the target, and has units of bits. We can rewrite the law as
 <math>ID = \log_2 \left(\frac{D}{W}+1\right)<math>
 <math>T = a + b ID<math>
Thus, the units for b are time/bit, e.g. milliseconds/bit. The constant a can be thought of as incorporating reaction time and/or the time required to click a button.
The values for a and b change as the conditions under which pointing is done are changed. For example, a mouse and stylus may both be used for pointing, but have different constants a and b associated with them.
An index of performance IP (also called throughput TP), in bits/time, can be defined to characterize how quickly pointing can be done, independent of the particular targets involved. There are two conventions for defining IP: one is IP = 1/b (which has the disadvantage of ignoring the effect of a), the other is IP = ID_{average}/MT_{average} (which has the disadvantage of depending on an arbitrarily chosen "average" ID). For a discussion of these two conventions, see Zhai (2002). Whatever definition is used, measuring the IP of different input devices allows the devices to be compared with respect to their pointing capability.
Slightly different from the Shannon formulation is the original formulation by Fitts
 <math>ID = \log_2 \left(\frac{2D}{W}\right)<math>
The factor of 2 here is not particularly important; this form of the ID can be rewritten with the factor of 2 absorbed as changes in the constants a, b. The "+1" in the Shannon form, however, does make it different from Fitts' original form, especially for low values of the ratio D/W. The Shannon form has the advantage that the ID is always nonnegative, and has been shown to better fit measured data.
A derivation of Fitts' law
Fitts' law can be derived from various models of motion. A very simple model, involving discrete, deterministic responses, is considered here. Although this model is overly simplistic, it provides some intuition for Fitts' law.
Assume that the user moves toward the target in a sequence of submovements. Each submovement requires a constant time t to execute, and moves a constant fraction 1r of the remaining distance to the centre of the target, where 0 < r < 1. Thus, if the user is initially at a distance D from the target, the remaining distance after the first submovement is rD, and the remaining distance after the nth submovement is r^{n}D. (In other words, the distance left to the target's centre is a function that decays exponentially over time.) Let N be the (possibly fractional) number of submovements required to fall within the target. Then,
 <math>r^N D = \frac{W}{2}<math>
Solving for N:
N  <math>= \log_r \frac{W}{2D}<math>  
<math>= \frac{1}{log_2 r} \log_2 \frac{W}{2D}<math>  (because log_{x}y = (log_{z}y)/(log_{z}x))  
<math>= \frac{1}{log_2 1/r} \log_2 \frac{2D}{W}<math>  (because log_{x}y =  log_{x}1/y) 
The time required for all submovements is:
 <math>T = Nt = \frac{t}{log_2 1/r} \log_2 \frac{2D}{W}<math>
By defining appropriate constants a and b, this can be rewritten as
 <math>T = a + b \log_2 \frac{D}{W}<math>
The above derivation is similar to one given in Card, Moran, and Newell (1983). For a critique of the deterministic iterativecorrections model, see Meyer et al. (1990).
References
 Original work
 Paul M. Fitts (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, volume 47, number 6, June 1954, pp. 381391. (Reprinted in Journal of Experimental Psychology: General, 121(3):262269, 1992).
 Paul M. Fitts and James R. Peterson (1964). Information capacity of discrete motor responses. Journal of Experimental Psychology, 67(2):103112, February 1964.
 Selected subsequent work
 The first application of Fitts' law to HCI
 Stuart K. Card, William K. English, and Betty J. Burr (1978). Evaluation of mouse, ratecontrolled isometric joystick, step keys, and text keys for text selection on a CRT. Ergonomics, 21(8):601613, 1978.
 Extending Fitts' law to 2 dimensions (bivariate targets)
 I. Scott MacKenzie and William A. S. Buxton (1992). Extending Fitts' law to twodimensional tasks. Proceedings of ACM CHI 1992 Conference on Human Factors in Computing Systems, pp. 219226. http://doi.acm.org/10.1145/142750.142794
 A. Murata. Extending effective target width in Fitts' law to a twodimensional pointing task. International Journal of HumanComputer Interaction, 11(2):137152, 1999.
 Johnny Accot and Shumin Zhai (2003). Refining Fitts' law models for bivariate pointing. Proceedings of ACM CHI 2003 Conference on Human Factors in Computing Systems, pp. 193200. http://doi.acm.org/10.1145/642611.642646
 Extending Fitts' law to goal passing and crossing
 Johnny Accot and Shumin Zhai (2002). More than dotting the i's  foundations for crossingbased interfaces. Proceedings of ACM CHI 2002 Conference on Human Factors in Computing Systems, pp. 7380. http://doi.acm.org/10.1145/503376.503390
 The first application of Fitts' law to HCI
 Overviews
 Stuart K. Card, Thomas P. Moran, Allen Newell (1983). The Psychology of HumanComputer Interaction.
 I. Scott MacKenzie (1992). Fitts' law as a research and design tool in humancomputer interaction. HumanComputer Interaction, volume 7, 1992, pp. 91139.
 Meyer, D. E., Smith, J. E. K., Kornblum, S., Abrams, R. A., & Wright, C. E. (1990). Speedaccuracy tradeoffs in aimed movements: Toward a theory of rapid voluntary action. In M. Jeannerod (Ed.), Attention and performance XIII (pp. 173226). Hillsdale, NJ: Lawrence Erlbaum. http://www.umich.edu/~bcalab/Meyer_Bibliography.html
 A. T. Welford (1968). Fundamentals of Skill. Methuen, 1968.
 Regarding two conventions on the definition of index of performance IP
 Shumin Zhai (2002). On the Validity of Throughput as a Characteristic of Computer Input, IBM Research Report RJ 10253, 2002, Almaden Research Center, San Jose, California. http://www.almaden.ibm.com/u/zhai/papers/ZhaiIBMReporRJ10253.pdf
External links
 First Principles of Interaction Design (http://www.asktog.com/basics/firstPrinciples.html) by Bruce Tognazzini
 A Quiz Designed to Give You Fitts (http://www.asktog.com/columns/022DesignedToGiveFitts.html) by Bruce Tognazzini
 Fitts' Law (http://ei.cs.vt.edu/~cs5724/g1/)
 Fitts’ Law: Modeling Movement Time in HCI (http://www.cs.umd.edu/class/fall2002/cmsc838s/tichi/fitts.html)
 Bibliography of Fitts' Law Research (http://www.yorku.ca/mack/RNFitts_bib.htm) compiled by I. Scott MacKenzie