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`Thus we see that the table models the structure that exists in
`the domain of input . devices .. It hel~s in ~n~ing appropriate
`equivalencies, wh1ch IS useful m dealmg w1th 1ssues of device
`independence (to be discussed below). It also helps us relate
`devices to one another. For example, a tablet is to a mouse what
`a joystick is to a trackball.
`Furthermore, if the taxonomy can suggest new transducers in
`a manner analogous to the way the periodic table of Mendelev
`has predicted new elements, then we can have even more confi(cid:173)
`dence in it. We make this claim and cite the "torque sensing• one(cid:173)
`dimensional pressure-sensitive transducer as an example. To our
`knowledge, no such device exists commercially. Nevertheless it is
`a potentially useful device, an approximation of which has been
`demonstrated by Herot and Weinzapfel (1978).
`Three novel input devices demonstrated on video are the bicycle
`as workstation (Roberts, 1989 video), the pointing stick (Rutledge
`and Selker, 1990 video), and the cue ball (Theil, 1991 video).
`Generality and Extensibility
`Choosing the input technologies to be used with a workstation
`often involves a trade-off between two conflicting demands.
`Every task has specialized needs that can be best addressed by a
`specialized technology, yet each workstation is used for multiple
`tasks. Supplying the optimum device for each task is generally
`impossible, so a trade-off must be made.
`Devices must be chosen to give the best coverage of the
`demands of the range of tasks. An important criterion in compar(cid:173)
`ing devices therefore is how broad their coverage is. Stated dif(cid:173)
`ferently, h;w many s~uares in Figure 7.6 can a particular device
`be used to fill? For example, graphics tablets can emulate many
`other transducers (Evans, Tanner, and Wein, 1981). The tablet is
`what could be called an extensible device. This property of exten(cid:173)
`sibility is an important but seldom considered criterion that
`should be used in device selection.
`Relative versus Absolute Controllers
`Another important characteristic of input devices is whether they sense
`absolute or relative values. This has a very strong effect on the nature
`of the dialogues that the system can support with any degree of flu(cid:173)
`ency. As we have seen a mouse cannot be used to digitize map coor(cid:173)
`dinates o• trace a drawing because it does not sense absolute position.
`An example taken from process control, the nulltng problem, occurs
`when absolute transducers are used in designs where one controller
`must be used for different tasks at different times (Buxton, I986a).
`What Our Taxonomy Doesn't Show
`Perhaps the main weakness of the taxonomy presented above is
`that it considers only the continuous aspect of devices. As the
`sample tasks discussed earlier in this chapter illustrated, ot~er fac(cid:173)
`tors, such as the integration of button devices with continuous
`controllers, has a strong impact on a device's perform~nce. ~n
`example is the case of trying to "pick up" and drag an obJeCt With
`~mouse (where the button is integrated) compared to perform(cid:173)
`tng the
`'tis difficult to
`1 ( h
`same transaction using a trackbal w ere 1
`~ld down the button which is not integrated, with the same
`nd that is controllin~ the dragging motion).
`.
`d .
`An ap
`f devices tS foun
`a
`lfl
`proach to capturing this aspect 0
`Uxton Cl990a). A three-state model is developed that can be
`
`Touch, Gesture, and Marking 473
`
`used to characterize both input devices and tasks. By providing a
`common vocabulary to describe both, a means of arriving at an
`appropriate match between the two is provided
`The reader is also referred to Foley, Wallace, and Chan 098?), an
`important early approach to characterizing input; Card, Ma~y,
`and Robertson (1990 1991) an exceUent taxonomy of input deVIces
`that extends the model ctev'eJoped above; and lipscomb and Pique
`0993), a complementary means of categorizing input devices.
`
`CHUNKING AND PHRASING
`
`Much of the rest of this chapter deals with alternative ways of
`articulating commands to the computer. The reading by Buxton
`(1986b) is intended to lay a theoretical foundation for this. The
`main thesis of the paper is that human-machine dialogues can
`benefit by appropriate phrasing similar to that used in written and
`.
`.
`spoken language, and in music.
`Phrasing not only groups together things that are assoctated m
`meaning or purpose, but also makes clear points of closure, that
`is, points at which one can be interrupted, or take a break. M.ost
`human-machine dialogues are compound, for example, selecting
`and positioning, positioning and scaling, navigating and selecting
`(Buxton, 1982; Buxton, 1984 video). The structure that emerges
`from appropriate phrasing can accelerate the process whereby
`novice computer users "chunk" together concepts, thereby build(cid:173)
`ing cognitive skill. Relevant to this issue is Mantei 0990 video), a
`delightful and thought-provoking collection of short clips docu(cid:173)
`menting a variety of mouse use behaviors.
`The reading discusses the nature of skill acquisition and the
`use of phrasing in its acquisition. In so doing, it lays the founda(cid:173)
`tion for how some of the literature on cognitive modeling can be
`extended to apply to the pragmattcand device levels of the inter(cid:173)
`face. Finally, it prepares the reader for the sections that follow(cid:173)
`those that deal with marking, gesture, and two-handed input.
`Modes and Mode Errors
`As originally defined by Norman (1981), a mode error is the mis(cid:173)
`classification of a situation resulting in actions that are inappro(cid:173)
`priate for the true situation. Whenever a particular action has dif(cid:173)
`ferent consequences depending upon the state of the system,
`mode errors may occur. The classic example of this is in text edi(cid:173)
`tors with command-line interfaces. Here, for example, typing the
`word "add" may be interpreted as a command, indicating that
`you are about to add text, or as just another word that you want
`to enter into the document.
`Reducing mode errors is one of the main attractions of direct
`manipulation interfaces (Tesler, 1981). Yet mode errors still occur.
`The best way to prevent them is to provide continuous and mean(cid:173)
`ingful feedback to the user. For example, pressure and movement
`feedback has been shown to be effective in reducing mode errors
`(Sellen, Kurtenbach, and Buxton, 1992).
`
`MARKING
`
`There is increasing interest in a style of interaction that has been
`variously called "paperlike," "pencentric," "pen based, • •character
`recognition, • or "gesture driven." Yet many of these are not like
`paper, and many do not use a pen. What all have in common is
`that the user's input is in the form of a stream of x,ycoorclinates
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