Introduction to cognitive engineering, its types, application

1. COGNITIVE ENGINEERING
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SUBMITTED BY
Pulak Pattanaik
M190614ME

2. Contents
• What is cognitive engineering
• History
• Objective of cognitive engineering
• Cognitive system approaches to new system design.
• Methods in cognitive engineering
• Cognitive task analysis
• Cognitive work analysis
• Areas of cognitive engineering
• Applications of cognitive engineering
• Future scope of cognitive engineering with help of artifical
intellilegience.
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3. What is Cognitive Engineering?
• Cognitive engineering is commonly viewed as the analysis,
modelling, design, and evaluation of complex sociotechnical
systems.
• It is further evolved in the branch of engineering called as,
Cognitive Ergonomics which concentrates on the analysis of
the cognitive process regarding diagnosis, workload, situation
awareness, decision-making, and planning.
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4. History
• The field of cognitive ergonomics emerged predominantly in
the 70's with the advent of the personal computer and new
developments in the fields of cognitive psychology and
artificial intelligence.
• The term cognitive engineering was first used by Norman
(1980, 1981) and cognitive systems engineering by Hollnagel
and Woods (1983).
• It is an applied science, and has rapidly developed over the last
30 years.
• Cognitive ergonomics is the application of psychology to work
to achieve the optimization between people and their work.
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5. Objective of cognitive engineering
 To improve the performance of
cognitive tasks in dynamic,
technology-intensive environments
through designing effective support,
including understanding the
fundamental principles behind
human action and performance
associated with engineering design
development principles, and
developing user-friendly systems.
 Studies on cognitive engineering
helps to address the balance between
the human cognitive abilities and
limitations and machine, task, and
environment.
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6. COGNITIVE ENGINEERING APPROACHES TO NEW
SYSTEM DEISGN
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Cognitive engineering and health informatics: Applications and
intersections (Journal of Biomedical Informatics)

7. Generic METHODS IN COGNITIVE ENGINEERING
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8. GENERIC METHODS IN COGNITIVE ENGINEERING(CONTD)
• The methods of Cognitive Engineering are placed into five
categories, based on the focus and purpose of each method.
The five categories, illustrated along with several
subcategories in previous figure are as follows:
1. Modelling Cognitive Processes
2. Modelling Behavioural Processes
3. Describing Cognitive and Behavioural Processes,
4. Modelling Erroneous Actions, and
5. Modelling Human-Machine Systems
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9. COGNITIVE TASK ANALYSIS
• Cognitive task analysis (CTA) is a type of Task analysis aimed
at understanding tasks that require a lot of cognitive activity
from the user, such as decision-making, problem-solving,
memory, attention and judgement.
• Example-Cognitive Task Analysis can be used to assess the
potential for human errors in information processing, and
thereby serve as a basis for designing decision support
systems.
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10. Types of COGNITIVE TASK ANALYSIS
• There is a collection of methods available for conducting a cognitive task
analysis which includes:-
 Applied Cognitive Task Analysis (ACTA)-help interviewer to extract
information by help of 3 methods i.e. Task diagram interview, knowledge
audit, simulation interview.
 Critical Decision Method (CDM)-probing an interview about a particular
incident to extract information related to strategies & decisions.(example
fire fighting military command & control).
 Task-Knowledge Structures (TKS) - generates description of tasks & then
re-express it in terms of knowledge. It consist of action/object pairs that
when combined represent the knowledge content of tasks.
 Cognitive Function Model (CFM)-It describes four products:
 an overview of all of the tasks and functions associated with the operators.
 a clear indication of which of these tasks/functions are cognitively
complex.
 a detailed cognitive analysis of these cognitively complex tasks/functions,
 recommendations and cautions regarding designing for these specific tasks
and functions.
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11. COGNITIVE WORK ANALYSIS
• The Cognitive work Analysis framework analyses the work
people do, the tasks they perform, the decision they make.
Their information behaviour and the context in which they
perform their work.
• Its application does in design of support systems for a variety
of modern work domains, such as process plants,
manufacturing, hospitals and libraries .
• It is used in design of control interfaces that can support
anticipated as well as procedure oriented work.
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12. Cognitive work Analysis (contd)
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13. AREAS OF COGNITIVE ENGINERING
• NEUROERGONOMICS- Neuro-ergonomics, asubfield of
cognitive ergonomics, aims to enhance human-computer
interaction by using neural correlates to better understand
situational task demands.
• HUMAN COMPUTER INTERACTION-HCI involves the study,
planning, and design of the interaction between people (users)
and computers.
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14. Application of Cognitive Ergonomics
• Designing a software interface to be "easy to use“.
• Designing a sign so that the majority of people will understand
and act in the intended manner.
• Designing an airplane cockpit or nuclear power plant control
system so that the operators will not make catastrophic errors.
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15. Examples of Cognitive Engineering in Air Traffic Control
• It is a complex enterprise comprising many agents with various
goals and concerns.
• Here focus is on designing efforts to increase capacity, i.e., to
increase the number of aircraft able to take off and land in a given
period of time.
• The Systems Engineering challenges here involve Performance,
Workload, and Training Estimation (How will the changes impact
performance?),Task Design (How will tasks be performed in the
future configuration?), and Requirements Review (Will controller
performance be adequate to meet the new demands?)
• To address these challenges, a suite of Cognitive Engineering
methods that includes Cognitive Task Analysis,
Computational Cognitive Modelling and Critical Incident
Analysis.
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16. Artificial Intelligence in Cognitive Engineering
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17. Artificial Intelligence in Cognitive Engineering
• Artificial intelligence is a conceptual framework or tool for
how a system can be implemented to process information and
behave in a manner considered intelligent.
• Artificial intelligence, from the perspective of cognitive
engineering, provides a means for developing and testing
computer models of human cognitive processes.
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18. CONCLUSION
• Cognitive Engineering play a central role in the practice of
Systems Engineering.
• Survey of the Cognitive Engineering methods in the form of a
Method Matrix, and summarized some specific applications to
Systems Engineering problems in many other domains.
• It describes, models and simulates to solve Reducing the
footprint, Improving coordination between people and
automation and Reducing the time line as the three basic
challenge in work environment
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19. REFERENCES
• Xiaoguang Sun, Rémy Houssin, Jean Renaud & Mickaël Gardoni,. “A review of
methodologies for integrating human factors and ergonomics in engineering
design”,. International Journal of Production Research,2018.(Vol. 57, Nos. 15–16,
4961–4976).
• April Savoya & et all, “A cognitive systems engineering design approach to
improve the usability of electronic order forms for medical consultation”,.
Journal of Biomedical Informatics, 2018(vol-85, 138–148).
• Bonaceto, C. A. (2003). A survey of cognitive engineering methods and systems
engineering uses (MITRE Product No. MP 03B0000061). Bedford, MA: The MITRE
Corporation.
• Hollnagel, E. 2003. “Handbook of Cognitive Task Design”. London: CRC
Press.
• Wei, J., and G. Salvendy. 2004. “The Cognitive Task Analysis Methods for job and
Task Design: Review and Reappraisal.” Behaviour &Information Technology 23 (4):
273–299.
• Vicente, K. J. 1999. “Cognitive Work Analysis: Toward Safe, Productive, and
Healthy Computer-based Work. Mahwah, NJ: Lawrence Erlbaum
• A. Zachary Hettinger, Emilie M. Roth , Ann M. Bisantz,. “Cognitive engineering
and health informatics: Applications and intersections”, Journal of Biomedical
Informatics, 2017(vol-67, 21-33).
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