Fitts's law

Fitts's law has been shown to apply under a variety of conditions; with many different limbs (hands, feet,[2] the lower lip,[3] head-mounted sights[4]), manipulanda (input devices),[5] physical environments (including underwater[6]), and user populations (young, old,[7] special educational needs,[8] and drugged participants[9]).

The original 1954 paper by Paul Morris Fitts proposed a metric to quantify the difficulty of a target selection task.

The metric combines a task's index of difficulty (ID) with the movement time (MT, in seconds) in selecting the target.

Researchers after Fitts began the practice of building linear regression equations and examining the correlation (r) for goodness of fit.

where: Since shorter movement times are desirable for a given task, the value of the b parameter can be used as a metric when comparing computer pointing devices against one another.

The first human–computer interface application of Fitts's law was by Card, English, and Burr,[11] who used the index of performance (IP), interpreted as 1⁄b, to compare performance of different input devices, with the mouse coming out on top compared to the joystick or directional movement keys.

[11] This early work, according to Stuart Card's biography, "was a major factor leading to the mouse's commercial introduction by Xerox".

[12] Many experiments testing Fitts's law apply the model to a dataset in which either distance or width, but not both, are varied.

During a Fitts's law task the user consciously acquires its target and can actually see it, making these two types of interaction not comparable.

This form was proposed by Scott MacKenzie,[15] professor at York University, and named for its resemblance to the Shannon–Hartley theorem.

In Fitts's law, the distance represents signal strength, while target width is noise.

[17] In 2002 the ISO 9241 was published, providing standards for human–computer interface testing, including the use of the Shannon form of Fitts's law.

The authors note, though, that the error is negligible and only has to be accounted for in comparisons of devices with known entropy or measurements of human information processing capabilities.

We is computed from the standard deviation in the selection coordinates gathered over a sequence of trials for a particular D-W condition.

The main advantage in computing IP as above is that spatial variability, or accuracy, is included in the measurement.

This model has an additional parameter, so its predictive accuracy cannot be directly compared with 1-factor forms of Fitts's law.

However, the original experiments required subjects to move a stylus (in three dimensions) between two metal plates on a table, termed the reciprocal tapping task.

[1] The target width perpendicular to the direction of movement was very wide to avoid it having a significant influence on performance.

A major application for Fitts's law is 2D virtual pointing tasks on computer screens, in which targets have bounded sizes in both dimensions.

For navigating e.g. hierarchical pull-down menus, the user must generate a trajectory with the pointing device that is constrained by the menu geometry; for this application the Accot-Zhai steering law was derived.

[23][24] Multiple Methods have been used to determine the target size :[25] While the W-model is sometimes considered the state-of-the-art measurement, the truly correct representation for non-circular targets is substantially more complex, as it requires computing the angle-specific convolution between the trajectory of the pointing device and the target [26] Since the a and b parameters should capture movement times over a potentially wide range of task geometries, they can serve as a performance metric for a given interface.

The a parameter is typically positive and close to zero, and sometimes ignored in characterizing average performance, as in Fitts' original experiment.

[27][28] An additional issue in characterizing performance is incorporating success rate: an aggressive user can achieve shorter movement times at the cost of experimental trials in which the target is missed.

More specifically, the effective size of the button should be as big as possible, meaning that its form has to be optimized for the direction of the user's movement onto the target.

Placing layout elements on the four edges of the screen allows for infinitely large targets in one dimension and therefore presents ideal scenarios.

Since the pointer will always stop at the edge, the user can move the mouse with the greatest possible speed and still hit the target.

The use of this rule can be seen for example in MacOS, which always places the menu bar on the top left edge of the screen instead of the current program's windowframe.

The user can continue interaction right from the current mouse position and doesn't have to move to a different preset area.

The research suggests that in practical implementations the direction in which a user has to move their mouse has also to be accounted for.

For right-handed users, selecting the left-most menu item was significantly more difficult than the right-most one.