Content deleted Content added
The law presently known as 'steering law' in the context of human-computer interaction was originally discovered (theoretically and empirically) by famous HF & E practitioner C.G. Drury, back in 1971 for linear paths and later on to curved paths. |
Magioladitis (talk | contribs) m refpunct / other fixes using AWB |
||
Line 39:
==== Pointing ====
Pointing at stationary targets such as buttons, windows, images, menu items, and controls on computer displays is commonplace and has a well-established modeling tool for analysis - [[Fitts's law|Fitt's law]] (Fitts, 1954) - which states that the time to make an aimed movement (MT) is a linear function of the index of difficulty of the movement: '''''MT = a + bID'''''. The index of difficulty (ID) for any given movement is a function of the ratio of distance to the target (D) and width of the target (W): '''''ID =''''' '''log<sub>2</sub>''(2D/W) -''''' a relationship derivable from [[information theory]].<ref name=":1" /> Fitt's law is actually responsible for the ubiquity of the computer [[Mouse (computing)|mouse]], due to the research of Card, English, and Burr (1978). Extensions of Fitt's law also apply to pointing at spatially moving targets, via the ''[[steering law]]'' , originally discovered by C.G. Drury in 1971<ref>{{Cite journal|last=DRURY|first=C. G.|date=1971-03-01|title=Movements with Lateral Constraint|url=http://dx.doi.org/10.1080/00140137108931246|journal=Ergonomics|volume=14|issue=2|pages=293–305|doi=10.1080/00140137108931246|issn=0014-0139|pmid=5093722}}</ref>
==== [[Control theory|Manual Control Theory]] ====
Line 131:
When a modeler builds a network model of a task, the first step is to construct a flow chart decomposing the task into discrete sub-tasks - each sub-task as a node, the serial and parallel paths connecting them, and the gating logic that governs the sequential flow through the resulting network. When modeling human-system performance, some nodes represent human decision processes and.or human task execution, some represent system execution sub-tasks, and some aggregate human/machine performance into a single node. Each node is represented by a statistically specified completion time distribution and a probability of completion. When all these specifications are programmed into a computer, the network is exercised repeatedly in Monte Carlo fashion to build up distributions of the aggregate performance measures that are of concern to the analyst. The art in this is in the modeler's selection of the right level of abstraction at which to represent nodes and paths and in estimating the statistically defined parameters for each node. Sometimes, human-in-the-loop simulations are conducted to provide support and validation for the estimates.. Detail regarding this, related, and alternative approaches may be found in Laughery, Lebiere, and Archer (2006) and in the work of Schwieckert and colleagues, such as Schweickert, Fisher, and Proctor (2003).<ref name=":1" />
Historically, Task Network Modeling stems from queuing theory and modeling of engineering reliability and quality control. Art Siegel, a psychologist, first though of extending reliability methods into a Monte Carlo simulation model of human-machine performance (Siegel & Wolf, 1969). In the early 1970s, the U.S. Air Force sponsored the development of '''SAINT''' (Systems Analysis of Integrated Networks of Tasks), a high-level programming language specifically designed to support the programming of Monte Carlo simulations of human-machine task networks (Wortman, Pritsker, Seum, Seifert, & Chubb, 1974). A modern version of this software is Micro Saint Sharp (Archer, Headley, & Allender, 2003). This family of software spawned a tree of special-purpose programs with varying degrees of commonality and specificity with Micro Saint. The most prominent of these is the [[IMPRINT (Improved Performance Research Integration Tool)
The network approach to modeling using these programs is popular due to its technical accessibility to individual with general knowledge of computer simulation techniques and human performance analysis. The flowcharts that result from task analysis lead naturally to formal network models. The models can be developed to serve specific purposes - from simulation of an individual using a human-computer interface to analyzing potential traffic flow in a hospital emergency center. Their weakness is the great difficulty required to derive performance times and success probabilities from previous data or from theory or first principles. These data provide the model's principle content.
| |||