Cog-C: A Tool for Estimating Cognitive Complexity and the Need for Cognitive Task Analysis

Task analysis is a methodology used during many different phases of system development. However, because many tasks involve complex cognitive processing, there is an increasing need for designers to perform cognitive task analysis. It is generally agreed that cognitive task analysis tends to be costly in terms of time and effort, and many designers ask how they would know when cognitive task analysis should be performed. This demonstration features a computer-based decision aid, Cog-C, to help designers answer this question. The software tool is based on the assumption that cognitive complexity is a major factor in determining when cognitive task analysis must be performed. The tool therefore helps the user determine the relative level of cognitive complexity for a set of tasks. It does this by (1) guiding the user in developing a task/subtask hierarchy, (2) guiding the subject matter expert in estimating the “amount” of various categories of knowledge required for subtask performance (e.g., concepts, rules, patterns, and strategies), and then (3) providing output including the number of steps required for task completion, standardized subscores showing the relative amounts of each knowledge category, an overall cognitive complexity score, and a general recommendation as to whether the task is a potential candidate for cognitive task analysis.

Download Full-text

Cognitive Task Analysis Using Complementary Elicitation Methods

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129503900919 ◽

1995 ◽

Vol 39 (9) ◽

pp. 525-529 ◽

Cited By ~ 2

Author(s):

Sallie E. Gordon

Keyword(s):

Cognitive Complexity ◽

Task Analysis ◽

Cognitive Task ◽

Cognitive Task Analysis ◽

Graph Analysis ◽

Conceptual Graphs ◽

Different Types ◽

Conceptual Graph ◽

High Degree ◽

Domain Independent

Cognitive task analysis is accomplished using a wide variety of methodologies, and we have previously argued that different methods will tend to elicit qualitatively different types of knowledge and skills. Because of this, many practitioners use complementary methods for a given project. We have developed such a complementary package of knowledge elicitation techniques, along with a specific representational method, which together are termed conceptual graph analysis. Conceptual graph analysis is domain-independent and can be used to evaluate complex cognitive tasks or subtasks. It relies on the successive use of document analysis, interviews, task observation, and induction based on review of task performance. The information from these elicitation techniques is represented as a set of interrelated conceptual graphs, but can be represented in other formats also. There are several issues relevant to cognitive task analysis that are currently being faced, including when to perform this type of analysis, and what methods to use. One answer is to perform cognitive task analysis when the task has an inherently high degree of cognitive complexity.

Download Full-text

Estimating Cognitive Complexity and the Need for Cognitive Task Analysis

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129403801607 ◽

1994 ◽

Vol 38 (16) ◽

pp. 1023-1027 ◽

Cited By ~ 1

Author(s):

Michael J. DeVries ◽

Sallie E. Gordon

Keyword(s):

Cognitive Processes ◽

Cognitive Complexity ◽

Task Analysis ◽

Cognitive Task ◽

Cognitive Task Analysis ◽

Types Of Knowledge ◽

Different Types ◽

Computer Based ◽

Multiple Domains ◽

Specific Project

Because an increasing number of systems are being developed to support complex cognitive functioning, task analysis is commonly being augmented with cognitive task analysis, which identifies cognitive processes, knowledge, and mental models relevant to task performance. Cognitive task analysis tends to be lengthy and time-consuming, so designers frequently ask how they might know if it is actually necessary for a specific project. In this paper, we assume that much of the need for cognitive task analysis depends on the inherent “cognitive complexity” of the task. We present a model of cognitive complexity, and show how it was used to develop a computer-based tool for estimating relative cognitive complexity for a set of tasks. The tool, Cog-C, elicits task and subtask hierarchies, then guides the user in making relatively simple estimates on a number of scales. The tool calculates and displays the relative cognitive complexity scores for each task, along with subscores of cognitive complexity for different types of knowledge. Usability and reliability were evaluated in multiple domains, showing that the tool is relatively easy to use, reliable, and well-accepted.

Download Full-text

Using Cognitive Task Analysis to Define Human Interface Requirements for First-of-A-Kind Systems

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129503900918 ◽

1995 ◽

Vol 39 (9) ◽

pp. 520-524 ◽

Cited By ~ 12

Author(s):

Emilie M. Roth ◽

Randall J. Mumaw

Keyword(s):

Cognitive Processing ◽

Task Analysis ◽

Cognitive Task ◽

Cognitive Task Analysis ◽

Expert Performance ◽

Advanced Process Control ◽

Human Interface ◽

Operator Problem ◽

Machine Interface ◽

Data Requirements

Cognitive task analysis (CTA) methods have grown out of the need to explicitly consider cognitive processing requirements of complex tasks. A number of approaches to CTA have been developed that vary in goals, the tools they bring to bear, and their data requirements. We present a particular CTA technique that we are utilizing in the design of new person-machine interfaces for first-of-a-kind advanced process control plants. The methodology has its roots in the formal analytic goal-means decomposition method pioneered by Rasmussen (1986). It contrasts with other approaches in that it is intended: (1) for design of first-of-a-kind systems for which there are no close existing analogues, precluding the use of CTA techniques that rely on empirical analysis of expert performance; (2) to define person-machine interface requirements to support operator problem-solving and decision-making in unanticipated situations; and (3) to be a pragmatic, codified, tool that can be used reliably by person-machine interface designers.

Download Full-text