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COGNITIVE MODELINGTraditional approaches to work measurement and methods analysis have been focused upon manual or psychomotor tasks that are visually detectable and operationally definable. Cognitive engineering provides several approaches to task description, measurement, and design that appear to be promising for extending analysis to tasks with significant cognitive content. Psychologists seeking to validate models of behavior have done much of this work.
There are many similarities between the traditional and cognitive engineering approaches to describing and analyzing tasks. Both approaches emphasize the division of tasks into smaller subtasks and the assignment of empirically assessed times to these subtasks. Both approaches also propose methods of recombining elemental tasks into sequences in order to evaluate the tasks as a whole, often with the assumption that subtask performance times are statistically independent.Model Human ProcessorThis model human processor predicts median performance times and expected time ranges for simple cognitive tasks. The model assumes that tasks involve a series of sensory and information-processing steps, followed by motor activity. Each step requires time to be performed.
Total task time is the sum of the times required for the separate activities. The Model Human Processor consists of three different processors and several different types of memory. The perceptual processorplaces a visual or auditory image into a Short-Term Sensory Store (STSS) and the corresponding code into working memory.
The cognitive processor checks for matches between items in working memory, retrieves information from memory, makes decisions, and selects a response. The motor processor executes the physical act decided upon. Each processor has a cycle time.This figure shows parameters for the visual and auditory sensory stores, working memory, and long-term memory.
Images within the two sensory stores decay over time. The median decay time is 200 msec for the visual store and 1,500 msec for the auditory store. The two sensory stores also vary in capacity. The median capacity is 17 items for the visual store, and 5 letters for the auditory store. Working memory has a similar set of limitations. The capacity of working memory is assumed to be 7 chunks if it is augmented by long-term memory. Without augmentation, the capacity drops to 3 chunks.
The decay time for information depends on how many chunks of information are in working memory. If only 1 chunk of information is held in working memory, the decay time drops to 7 s.
Long-term memory is viewed to be unlimited in capacity that is, an unlimited amount of information can be stored, and once it’s stored it stays there forever.GOMS: COGNITIVE WORK MEASUREMENT OF A SKILL-BASED TASKCard et al. (1983) discuss research directed toward the practical analysis of a text-editing task they consider to be representative of skilled cognitive task performance in general.The GOMS model of task performanceGOMS models human performance in terms of four basic components: (1) Goals, (2) Operators, (3) Methods, and (4) Selection rules for choosing among alternative methods. The basic structure of the GOMS model developed by Card et al. Note that the model hierarchically defines the goals of a person performing a text-editing task. More specifically, the goal EDIT-MANUSCRIPT is divided into multiple cases of the subgoal EDIT-UNIT-TASK. The subgoal EDIT-UNIT-TASK is further broken down into ACQUIRE-UNIT-TASK and EXECUTE-UNIT-TASK.
Each of these goals is then further subdivided into subgoals. This hierarchical form of task description is similar to that obtained using the generic terms of PERFORM, GENERATE, and EXECUTE provided by Lehto and Miller (1986).
Within the GOMS model, goals are symbolic structures that guide the sequencing of tasks during performance. Goals are satisfied by applying elementary perceptual, motor, and cognitive operators that relate inputs to outputs and last for fixed durations.NGOMSLKieras (1988) discusses a GOMS methodology for human-computer interface design, which he calls Natural GOMS Language (NGOMSL).
This language is intended to assist in the development of production rules for cognitive simulations. As a first step in the procedure, the analyst specifies a set of toplevel goals the user of the system is expected to have. NGOMS describes these as action-object pairs in the form: 1verb noun, such as activate system, setup system, etc. For each goal, the analyst describes a general method in terms of operators that are as high level (or general) as possible. The operators within this general method are then provided methods. This recursive procedure continues until each method is described in terms of analyst-defined or primitive operators. The set of pre-defined primitive mental operators provided in NGOMSL describe user actions pertaining to:.
The flow of control. Memory storage and retrieval. Primitive perceptual-motor acts.SMESDSMESD developed by Lehto (1985) and extended by Lehto and Miller (1986) emphasizes flow and fault tree diagrams as a mediating method for knowledge acquisition.
It provides for the hierarchical description of sequences of elemental tasks within a task, as networks of connected goals and subgoals. In this respect, SMESD is similar to the GOMS methodology, but it differs in that the task description language is object oriented. The focus of SMESD was on generic ways of developing expert systems capable of analyzing a wide range of safety and reliability related problems. The output of SMESD is a set of rules that can be implemented as a production system.SHAPASHAPA (Sanderson, James & Seidler 1989) is a software tool developed to help analysts perform cognitive task analysis. It attains this goal by providing a sophisticated mechanism for organizing the information within verbal protocols in terms of user-defined predicates. As such, it can be useful in building GOMS or production system models from verbal protocols.
Application of SHAPA requires that the analyst first define a set of predicates and their arguments. For example, if the analyst wished to develop a GOMS model, goal, operator, method, and selection rule predicates would have to be defined; each of which would be given a set of relevant arguments. The verbal protocol data must then be input to SHAPA and analysis can commence. During this analysis, each statement in the protocol is tagged by the analyst with an appropriate predicate and its arguments would be assigned appropriate values. For example, the statement “Will now monitor machine 4 could be—tagged GOAL.
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