IntroductionIn today's fast-paced world, the efficiency of our computer peripherals plays a crucial role in determining our productivity. One often-debated topic is the battle between trackball mice and conventional optical mice - which of these two reigns supreme in terms of work efficiency? In this informative article, we will delve into the key differences between these two types of mice, evaluate their pros and cons, and attempt to determine which one offers users the highest level of efficiency. Join us as we explore the fascinating world of trackball and optical mice!
MethodsWhen it comes to computer input devices, one cannot ignore the importance of Fitts' Law. Like many psychologists in the 1950s, Fitts was motivated to investigate whether human performance could be quantified using a metaphor from the new and exciting field of information theory. This field emerged from the work of Shannon, Wiener, and other mathematicians in the 1940s. The terms probability, redundancy, bits, noise, and channels entered the vocabulary of experimental psychologists as they explored the latest technique of measuring and modeling human behavior. Two well‐known models in this vein are the Hick-Hyman law for choice reaction time and Fitts’ law for the information capacity of the human motor system. Fitts’ particular interest was rapid‐aimed movements, where a human operator acquires or selects targets of a certain size over a certain distance. Fitts proposed a model—now “law”—that is widely used in fields such as ergonomics, engineering, psychology, and human‐computer interaction. The starting point for Fitts’ law is an equation known as Shannon’s Theorem, which gives the information capacity C (in bits/s) of a communications channel of bandwidth B (in s−1 or Hz) as
Following the first publication of Fitts' law, numerous studies emerged in various forms. While their internal validity isn't disputed, inconsistencies exist, making cross-study comparisons challenging. These inconsistencies are due to inadequate details, different throughput calculation methods, and variations in data collection or usage. Standardizing Fitts' law research methodology is essential, especially in HCI. ISO 9241-9, now ISO 9241-411, provides this standardization by outlining performance testing procedures using Fitts' paradigm in one-dimensional (1D) and two-dimensional (2D) tasks.
This standard has been applied to various studies in the past 15 years, evaluating novel interactions or devices such as trackball game controllers, smartphone touch input, tabletop touch input, and Wiimote gun attachments.
Although ISO 9241‐9 provides the correct formula for Fitts’ throughput, little guidance is offered on the data collection, data aggregation, or in performing the adjustment for accuracy. The latter presents a particular challenge when using the 2D task. In this section we examine the best practice method for calculating Fitts’ throughput. We begin with Figure 17.7 which shows the formula for throughput, expanded to reveal the Shannon formulation for ID and the use of effective values for target amplitude and target width. The figure also highlights the presence of speed (1/MT) and accuracy (SDx ) in the calculation.
Figure 1.5 Formula for throughput showing the Shannon formulation for ID and the adjustment for accuracy. Speed (1/MT) and accuracy (SDx) are featured. Figure 1.6 Geometry for a trial. Whether using the 1D or the 2D task, the calculation of throughput requires Cartesian coordinate data for each trial. Data are required for three points: the starting position (“from”), the target position (“to”), and the trial‐end position (“select”). See Figure 1.4. Although the figure shows a trial with horizontal movement to the right, the calculations described next are valid for movements in any direction or angle. Circular targets are shown to provide a conceptual visualization of the task. Other target shapes are possible, depending on the setup in the experiment. The calculation begins by computing the length of the sides connecting the from, to, and select points in the figure. Using Java syntax:
double a = Math.hypot(x1—x2, y1—y2);
double b = Math.hypot(x—x2, y—y2);
double c = Math.hypot(x1—x, y1—y);
The x‐y coordinates correspond to the from (x1, y1), to (x2, y2), and select (x, y) points in the figure. Given a, b, and c, as above, dx and ae are then calculated:
double dx = (c * c — b * b — a * a)/(2.0 * a);
double ae = a + dx;
Given arrays for the from, to, and select points in a sequence of trials and the computed ae and dx for each trial, Ae is the mean of the ae values and SDx is the standard deviation in the dx values. With these, IDe is computed using Figure.1.5 and throughput （TP） is computed using Eq. 1.3. One final point concerns the unit of analysis for calculating throughput. The correct unit of analysis for throughput is an uninterrupted sequence of trials for a single participant. The premise for this is twofold:
•throughput cannot be calculated on a single trial;
•a sequence of trials is the smallest unit of action for which throughput can be attributed as a measure of performance.
Example Protoarc User StudyNow, we want to integrate the above idea into a user case that explores the impact of trackball mice and conventional optical mice on throughput. It seems that this issue has not been systematically studied, which means that there is currently no consensus on whether trackball mice or conventional optical mice have higher throughput.
ApparatusThe test devices were Protoarc EM03 and Logitech MX Master 3S.
Results and DiscussionThe overall mean throughput of the EM03 is 5.39 bits/second, while the mean throughput of the Master 3s is 4.93 bits/second. These results, in themself, are rather remarkable, as they demonstrate that the performance efficiency of trackball mice is not inferior to that of conventional mice, and may even be superior. Trackball mice not only offer superior performance compared to standard mice, but their unique ergonomic design also promotes a healthier working environment.
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