1Seneca College of Applied Arts and Technology
Toronto, Ontario, Canada M2J 2X5
2Dynamic Graphics Project, Computer Systems Research Institute
University of Toronto
Toronto, Ontario Canada M5S 1A4
An experiment is described comparing three devices (a mouse, a trackball, and a stylus with tablet) in the performance of pointing and dragging tasks. During pointing, movement times were shorter and error rates were lower than during dragging. It is shown that Fitts' law can model both tasks, and that within devices the index of performance is higher when pointing than when dragging. Device differences also appeared. The stylus displayed a higher rate of information processing than the mouse during pointing but not during dragging. The trackball ranked third for both tasks.
Input devices, input tasks, performance modeling.
This paper has two main contributions. First, it shows that dragging is a variation of pointing, and consequently, that Fitts' law can be applied to it. Second, it establishes that the performance of input devices in each of these two tasks should be considered in characterizing the human-factors of devices.
We present an experiment comparing three devices (a mouse, a tablet,
and a trackball) in both a pointing and a dragging task. Each is modelled
after Fitts' reciprocal tapping task (Fitts, 1954).
MT = a + b log2(2A / W) (1)where a and b are empirical constants determined through linear regression. A variation proposed by Welford (1968) is also widely used:
MT = a + b log2(A / W + 0.5). (2)The log term is called the index of difficulty (ID) and carries the units "bits" (because the base is "2"). The reciprocal of b is the index of performance (IP) in bits/s. This is purportedly the human rate of information processing for the movement task under investigation. Card, English, and Burr (1978) found IP = 10.4 bits/s for the mouse in a text selection task. This is similar to values obtained by Fitts (1954) but is higher than usual. For example, ten devices were tested in studies by Epps (1986), Jagacinski and Monk (1985), and Kantowitz and Elvers (1988). Performance indices ranged from 1.1 to 5.0 bits/s.
There is recent evidence that the following formulation is more theoretically sound and yields a better fit with empirical data (MacKenzie, 1989):
MT = a + b log2(A / W + 1). (3)In an analysis of data from Fitts' (1954) experiments, Equation 3 was shown to yield higher correlations than those obtained using the Fitts or Welford formulation. Another benefit of Equation 3 is that the index of difficulty cannot be negative, unlike the log term in Equation 1 or 2. Studies by Card et al. (1978), Gillan, Holden, Adam, Rudisill, and Magee (1990), and Ware and Mikaelian (1987), for example, yielded a negative index of difficulty under some conditions. Typically this results when wide, short targets (viz., words) are approached from above or below at close range. Under such conditions, A is small, W is large, and the index of difficulty, computed using Equation 1 or 2, is often negative. A negative index is theoretically unsound and diminishes some of the potential benefits of the model.
Fitts' original experiments used reciprocal tapping tasks where one alternately tapped on two rectangular targets. The controlled variables were target width and the distance between targets; however, the motion was one dimensional (back and forth). Extending the model to two dimensions (which better fits pointing tasks in computer usage) has been discussed by Card et al. (1978) and Jagacinski and Monk (1985), among others.
Using Fitts' law to model dragging is best explained using an example. Consider the case of deleting a file on the Apple Macintosh. First, the user acquires the icon for the file in question. This point/select operation is a classic two-dimensional target acquisition task. Then, while holding the mouse button down, the icon is dragged to the trashcan. This also is a target acquisition task. One is really just acquiring the trashcan icon. In this case, however, the task is performed with the mouse button depressed.
From the perspective of motor performance, the only difference is whether
the tasks are performed with the mouse button released or held down. (In
both cases, the target is an icon of approximately the same size.) These
classes of action are characterized as State 1 and State 2
by Buxton (1990), as illustrated in Figure 1.
Figure 1. Simple 2-state interaction. In State 1, mouse motion
moves the tracking symbol. Pressing and releasing the mouse button over an
icon selects the icon and leaves the user in State 1. Depressing the mouse
button over an icon and moving the mouse drags the icon. This is a State 2
action. Releasing the mouse button returns to the tracking state, State 1
(from Buxton, 1990).
State 2 motion on most input devices requires active maintenance of the state (e.g., by holding down a button), generally restricting the freedom of movement. Given the frequency of State 2 actions in direct manipulation systems, we feel the following are important:
Figure 2. State 1 pointing task. Subjects started at the target
marked by the arrow and alternately selected the targets as quickly and
accurately as possible. The cross tracked the movement of the input device.
Dragging Task: The dragging task was similar except an "object"
(see Figure 3) was acquired by pressing and holding down the button (on
the mouse and trackball) or maintaining pressure on the stylus to "drag"
the object to the other target. The object was dropped by releasing the
button or pressure. The new object to be selected appeared immediately
in the centre of the target in which the old object was just dropped.
Figure 3. State 2 dragging task. By placing the cross over the
object inside the target, the object could be acquired and dragged to the other
target. State 2 was maintained by holding the mouse button down.
The dragging task can be likened to an inside-out pointing task: During pointing, movement occurred with the mouse button up and a down-up action terminated a move (and initiated the next); during dragging, movement occurred with the mouse button down and an up-down action terminated a move (and initiated the next).
Although instructed to move as quickly and accurately as possible, performance feedback was not provided. Subjects were told that an error rate of one miss in every 25 trials was optimal.
The task and device factors were within-subjects -- each subject performed both pointing and dragging on all three devices. Ordering of devices was counterbalanced. Within devices, a random process determined the initial task (dragging or pointing) and tasks alternated for each session thereafter.
Prior to each new device-task condition, subjects were given a practice
block. Breaks were allowed between blocks and sessions, but subjects completed
all ten sessions on each device in a single sitting. Three sittings over
three days, for a total of about three hours, were necessary to complete
Figure 4. Dropping errors. The distribution of X coordinates for
one subject showing deviate responses classified as "dropping errors". Shown
are 50 trials for the trackball during dragging with A= 32 and W = 2.
Because dropping errors are considered a distinct behavior, we adjusted the data by eliminating trials with an X coordinate more than three standard deviations from the mean. Means and standard deviations were calculated separately for each subject, and for each combination of width (W), amplitude (A), device, and task.
We also eliminated trials immediately following deviate trials. The literature on response times for repetitive, self-paced, serial tasks shows that deviate responses are disruptive events and can cause unusually long response times on the following trial (e.g., Rabbitt, 1968)
.A multiple comparisons test indicated a significant drop in movement time after the first session (p < .05), but no significant difference in movement time over the last four sessions. Therefore, the first session for each subject for each device-task condition was also removed. Henceforth, "adjusted" results are those subject to the above modifications.
Figure 6 shows the mean percentage errors by device and task, both adjusted and unadjusted. The unadjusted data showed a significant main effect of task, with the dragging task yielding many more errors than the pointing task (F1,11 = 45.28, p < .001). In addition there was a significant main effect of device (F2,22 = 7.57, p < .001). This effect, however, was entirely due to the dragging task as shown by a significant interaction (F2,22 = 16.04, p < .001). While there was no difference in error rate across devices in the pointing task, error rate in the dragging task was dependent on device, with the trackball yielding the most errors and the mouse the fewest.
Adjusting for errors, not surprisingly, had a profound effect on dragging. By definition, no dropping errors occur in the pointing task; however, the same criterion was applied for consistency. If valid, not as many errors would be eliminated in the pointing task. As evident in Figure 6, this was the case.
Figure 6. Mean percentage errors by device and task
We applied Welford's (1968, p. 147) technique for normalizing response variability based on subjects' error rate. For each A-W condition, target width was transformed into an effective target width (We) -- for a nominal error rate of 4% -- and ID was re-computed. Then, MT was regressed on the "effective" ID. Performance differences emerging from normalized data should be more indicative of inherent device-task properties. Figure 7 shows the results of such an analysis.
There were consistently high correlations (r) between movement time (MT) and the index of task difficulty (ID, computed using Equation 3) for all device-task combinations. The performance indices (IP), obtained through linear regression, were less than those found by Card et al. (1978), but are comparable to those cited earlier. The rank order of devices changed across tasks, with the tablet outperformed the mouse during pointing but not during dragging. The differences, however, were slight. The trackball, third for both tasks, had a particularly low rating of IP = 1.5 bits/s during dragging.
Five of the intercepts were close to the origin (within 135 ms); however, a large, negative intercept appeared for the trackball-dragging combination (-349 ms). With a negative intercept, the possibility of a negative predicted movement time looms. However, the chance of such an erroneous prediction is remote because of the large slope coefficients. For example, under the latter condition, a negative prediction would only occur for ID < 0.5 bits.
==================================================== Regression Coefficients ---------------------------------- Intercept, Slope, b IP Device ra a(ms) (ms/bit> (bits/s)b ---------------------------------------------------- *** Pointing *** Mouse .990 -107 223 4.5 Tablet .988 -55 204 4.9 Trackball .981 75 300 3.3 *** Dragging *** Mouse .992 135 249 4.0 Tablet .992 -27 276 3.6 Trackball .923 -349 688 1.5 ===================================================== a n = 16, p < .001 b IP (index of performance) = 1/b
The experiment showed a clear difference with devices in performing State 1 (pointing) and State 2 (dragging) tasks. For State 2 tasks, movement times are longer and error rates are higher. The degradation between states differs across devices.
The trackball was a poor performer for both tasks, and had a very high error rate during dragging. This can be explained by noting the extent of muscle and limb interaction required to maintain State 2 motion and to execute state transitions. The button on the trackball was operated with the thumb while the ball was rolled with the fingers. It was particularly difficult to hold the ball stationary with the fingers while executing a state transition with the thumb: The interaction between muscle and limb groups was considerable. This was not the case with the mouse or tablet which afford separation of the means to effect action. Motion was realized through the wrist or forearm with state transitions executed via the index finger (mouse) or the application of pressure (tablet). Clearly, in the design of direct manipulation systems employing State 2 actions, the performance of devices in both states should be considered.
The experiment also showed that Fitts' law can model both dragging and pointing tasks; however, performance indices within devices were higher while pointing. Overall, IP ranged from 1.5 to 4.9 bits/s, somewhat less than the values found by Card et al. (1978) but comparable to values in other studies.
Of the devices tested, the highest index of performance was for the tablet during pointing and for the mouse during dragging. It is felt that a stylus, despite the requirement of additional, non-standard hardware, has the potential to perform as well as the mouse in direct manipulation systems, and may out-perform the mouse when user activities include, for example, drawing or gesture recognition.
Clearly, the work is not complete, and issues such as extending Fitts'
law to accommodate approach angle need further investigation.
This research was supported by the Natural Sciences and Engineering
Research Council of Canada, Xerox Palo Alto Research Center, Digital Equipment
Corp., and Apple Computer Inc. We gratefully acknowledge this contribution,
without which, this work would not have been possible.
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