Input Techniques Jeffrey Heer · 14 May 2009

Pointing Device Evaluation Experimental task: target acquisition abstract, elementary Real task: interacting with GUIs pointing is fundamental W D

Fitts’ Law [Paul Fitts, 1954] MT = a + b log2 (D/W + 1) Index of Difficulty (ID ) a, b = constants (empirically derived) D = distance W = size Index of Performance (IP ) = 1/b (bits/s) ID is the number of bits of information transmitted IP is the rate of transmission Fitt’s thesis was that IP is constant across a range of values for ID How do you think this law was determined? Derived by analogy from Information Theory Tested empirically Models well-rehearsed selection task MT increases as the distance to the target increases MT decreases as the size of the target increases

Experimental Data Is there any Questions at this point?

Considers Distance and Target Size MT = a + b log2 (D/W + 1) Target 1 Target 2 Same ID → Same Difficulty

Considers Distance and Target Size MT = a + b log2 (D/W + 1) Target 1 Target 2 Smaller ID → Easier

Considers Distance and Target Size MT = a + b log2 (D/W + 1) Target 1 Target 2 Larger ID → Harder

What does Fitts’ law really model? Target Width Velocity (c) (a) Ballistic movement (b) Distance

Comparing device performance Device Study IP (bits/s) Hand Fitts (1954) 10.6 Mouse Card, English, & Burr (1978) 10.4 Joystick Card, English, & Burr (1978) 5.0 Trackball Epps (1986) 2.9 Touchpad Epps (1986) 1.6 Eyetracker Ware & Mikaelian (1987) 13.7 Reference: MacKenzie, I. Fitts’ Law as a research and design tool in human computer interaction. Human Computer Interaction, 1992, vol. 7, pp. 91-139

Using laws to predict performance Which will be faster on average? Pie menu (bigger targets & less distance)? Today Sunday Monday Tuesday Wednesday Thursday Friday Saturday Pop-up Linear Menu Pop-up Pie Menu

The Macintosh menu bar and taskbar and the Windows XP Taskbar have “infinite height” improving their Fitts Law performance …as does the back button in the Firefox browser

Fitts’ Law [Paul Fitts, 1954] MT = a + b log2 (D/W + 1) Index of Difficulty (ID ) a, b = constants (empirically derived) D = distance W = size Index of Performance (IP ) = 1/b (bits/s) What are the limitations of this model? Assumes 1D approach Limited to pointing tasks Predicts movement time but not error rate Models well-rehearsed selection task MT increases as the distance to the target increases MT decreases as the size of the target increases

Beyond pointing: trajectories Steering Law Accot & Zhai

EdgeWrite Corner-based text input Uses physical constraints Hard edges and corners Can help offset motor impairments

Crossing UIs [Apitz & Guimbretière 04] From the CrossY paper conclusion: We presented the first exploration of crossing as the primary building block of a graphic user interface. We found that crossing is as expressive as the more traditional point-and-click interface and provides designers with more flexibility than the latter because it takes into account the shape and direction of the strokes. We also found that a crossing-based interface can encourage the fluid composition of commands in one stroke.

From Yves Guiard’s Handwriting Experiment. The left half of the image shows an entire sheet of paper as filled out by the subject on dictation. The right half of the image shows the impression left on a blotter which was on a desk underneath the sheet of paper. The experiment demonstrates that movement of the dominant hand occurred not with respect to the sheet of paper itself, but rather with respect to the postures defined by the non-dominant hand moving and holding the sheet of paper over time. In a related experiment, Athenes [7], working with Guiard, had subjects repeatedly write a memorized one-line phrase at several heights on individual sheets of paper, with the nonpreferred hand excluded (no contact permitted with the sheet of paper) during half the trials. Athenes's study included 48 subjects, including a group of 16 right-handers and two groups of left-handers,2 each again with 16 subjects. Athenes's results show that when the nonpreferred hand was excluded, subjects wrote between 15% and 27% slower, depending on the height of the line on the page, with an overall deficit of approximately 20%. This result clearly shows that handwriting is a two-handed behavior.

Yves Guiard: Kinematic Chain Asymmetry in bimanual activities “Under standard conditions, the spontaneous writing speed of adults is reduced by some 20% when instructions prevent the non-preferred hand from manipulating the page” Non-dominant hand (NDH) provides a frame of reference for the dominant hand (DH) NDH operates at a coarse temporal and spatial scale; DH operates at a finer scales From Ken Hinckley’s Thesis: Guiard's analysis of human skilled bimanual action [67] provides an insightful theoretical framework for classifying and understanding the roles of the hands. The vast majority of human manual acts involve two hands acting in complementary roles. Guiard classifies these as the bimanual asymmetric class of manual actions. Guiard has proposed the Kinematic Chain as a general model of skilled asymmetric bimanual action, where a kinematic chain is a serial linkage of abstract motors. For example, the shoulder, elbow, wrist, and fingers form a kinematic chain representing the arm. For each link (e.g. the forearm), there is a proximal element (the elbow) and a distal element (the wrist). The distal wrist must organize its movement relative to the output of the proximal elbow, since the two are physically attached. The Kinematic Chain model hypothesizes that the preferred and nonpreferred hands make up a functional kinematic chain: for right-handers, the distal right hand moves relative to the output of the proximal left hand. Based on this theory and observations of people performing manual tasks, Guiard proposes three high-order principles governing the asymmetry of human bimanual gestures, which can be summarized as follows: (1) Motion of the preferred hand typically finds its spatial references in the results of motion of the nonpreferred hand. The preferred hand articulates its motion relative to the reference frame defined by the nonpreferred hand. For example, when writing, the nonpreferred hand controls the position and orientation of the page, while the preferred hand performs the actual writing by moving the pen relative to the nonpreferred hand (fig. 2.15). This means that the hands do not work independently and in parallel, as has often been assumed by the interface design community, but rather that the hands specify a hierarchy of reference frames, with the preferred hand moving relative to the nonpreferred hand. (2) The preferred and nonpreferred hands are involved in asymmetric temporal-spatial scales of motion. The movements of the preferred hand are more frequent and more precise than those of the nonpreferred hand. During handwriting, for example, the movements of the nonpreferred hand adjusting the page are infrequent and coarse in comparison to the high-frequency, detailed work done by the preferred hand. (3) The contribution of the nonpreferred hand starts earlier than that of the preferred. The nonpreferred hand precedes the preferred hand: the nonpreferred hand first positions the paper, then the preferred hand begins to write. This is obvious for handwriting, but also applies to tasks such as swinging a golf club [67].