Philip J. Smith
The Ohio State University
Norman D. Geddes
Applied Systems Intelligence, Incorporated
Human Performance on Decision-Making Tasks
Errors and Cognitive Biases
Slips
Mistakes
Cognitive Biases
Designer Error
Systems Approaches to Error
Errors and Cognitive Biases — Implications for Design
Human Expertise
Descriptive Models
Normative Optimal Models
Human Expertise — Implications for Design
Additional Cognitive Engineering Considerations
The Human Operator as Monitor
Complacency
Excessive Mental Workload
Lack of Awareness or Understanding
Lack of Trust and User Acceptance
Active Biasing of the User’s Cognitive Processes
Distributed Work and Alternative Roles
Organizational Failures
Case Studies
Case Study A. Interactive Critiquing as a Form of Decision Support
The Application Area
The Design Solution
Evaluation of the Implemented Solution
Case Study A — Conclusions
Case Study Study B. Software Associates: Decisions in Real-Time, High-Stakes Settings
The Application Area
The Need for Decision Aiding
The Design Solution
Results
Case Study C. Decisions and Problem Representations
The Application Area
The Design Solution
Results
Case Study D. Reducing the Burden of Training
The Application Area
The Design Solution
Results
Conclusions
References
Figure 33.1: (a) Full test panel with intermediate results marked using color-coded markers provided by AIDA (shown here in black and white). For the panel shown above, at this point in the analysis it looks like anti-Fyb is present if the reaction is being caused by a single antibody (Fyb is on all the reacting cells—the 2+ and 3+ cells—and only on those cells). However, it looks like anti-E and anti-K are present if the reaction is being caused by 2 antibodies. AIDA = Antibody Identification Assistant. (b) Sample ABO and Rh panel with feedback provided to the user regarding a possible slip or mistake.