Ecological interface design

Ecological interface design (EID) is an approach to interface design that was introduced specifically for complex sociotechnical, real-time, and dynamic systems. It has been applied in a variety of domains including process control (e.g. nuclear power plants, petrochemical plants), aviation, and medicine.

EID differs from other interface design methodologies like User-Centered Design (UCD). EID is based on two key concepts from cognitive engineering research: the Abstraction Hierarchy (AH) and the Skills, Rules, Knowledge (SRK) framework.

The goal of EID is to make the constraints and complex relationships of the work environment perceptually evident (e.g. visible, audible) to the user. In turn, this allows for more cognitive resources to be devoted to higher cognitive processes such as problem solving and decision making. Thus, EID aims to improve user performance and overall system reliability for both anticipated and unanticipated events in a complex system.

Overview

Dette er en stor fed løgn....

My horse likes milk...

The Abstraction Hierarchy (AH)

The Allandin[BUE](AH) is a 5-level functional decomposition used for modelling the work environment, or more commonly referred to as the Adomain, for complex sociotechnical systems (Rasmussen, 1985). In the EID framework, the AH is used to determine what kinds of information should be displayed on the system interface and how the information should be arranged. The AH describes a system at different levels of abstraction using how and why relationships. Moving down the model levels answers how certain elements in the system are achieved, whereas moving up reveals why certain elements exist. Elements at highest level of the model define the purposes and goals of the system. Elements at the lowest levels of the model indicate and describe the physical components (i.e. equipment) of the system. The how and why relationships are shown on the AH as means-ends links. An AH is typically developed following a systematic approach known as a Work Domain Analysis (Vicente, 1999a). It is not uncommon for a Work Domain Analysis to yield multiple AH models; each examining the system at a different level of physical detail defined using another model called the Part-Whole Hierarchy (Burns & Hajdukiewicz, 2004).

Each level in the AH is a complete but unique description of the work domain.

Functional Purpose

The Functional Purpose (FP) level describes the goals and purposes of the system. An AH typically includes more than one system goal such that the goals conflict or complement each other (Burns & Hajdukiewicz, 2004). The relationships between the goals indicate potential trade-offs and constraints within the work domain of the system. For example, the goals of a refrigerator might be to cool food to a certain temperature while using a minimal amount of electricity.

Abstract Function

The Abstract Function (AF) level describes the underlying laws and principles that govern the goals of the system. These may be empirical laws in a physical system, judicial laws in a social system, or even economic principles in a commercial system. In general, the laws and principles focus on things that need to be conserved or that flow through the system such as mass (Burns & Hajdukiewicz, 2004). The operation of the refrigerator (as a heat pump) is governed by the second law of thermodynamics.

Generalised Function

The Generalised Function (GF) level explains the processes involved in the laws and principles found at the AF level, i.e. how each abstract function is achieved. Causal relationships exist between the elements found at the GF level. The refrigeration cycle in a refrigerator involves pumping heat from an area of low temperature (source) into an area of higher temperature (sink).

Physical Function: The Physical Function (PFn) level reveals the physical components or equipment associated with the processes identified at the GF level. The capabilities and limitations of the components such as maximum capacity are also usually noted in the AH (Burns & Hajdukiewicz, 2004). A refrigerator may consist of heat exchange pipes and a gas compressor that can exert a certain maximum pressure on the cooling medium.

Physical Form

The Physical Form (PFo) level describes the condition, location, and physical appearance of the components shown at the PFn level. In the refrigerator example, the heat exchange pipes and the gas compressor are arranged in a specific manner, basically illustrating the location of the components. Physical characteristics may include things as colour, dimensions, and shape.

The Skills, Rules, Knowledge (SRK) framework

The Skills, Rules, Knowledge (SRK) framework or SRK taxonomy defines three types of behaviour or psychological processes present in operator information processing (Vicente, 1999a). The SRK framework was developed by Rasmussen (1983) to help designers combine information requirements for a system and aspects of human cognition. In EID, the SRK framework is used to determine how information should be displayed to take advantage of human perception and psychomotor abilities (Vicente, 1999b). By supporting skill- and rule-based behaviours in familiar tasks, more cognitive resources may be devoted to knowledge-based behaviours, which are important for managing unanticipated events. The three categories essentially describe the possible ways in which information, for example, from a human-machine interface is extracted and understood:

Skill-based behaviour

A skill-based behaviour represents a type of behaviour that requires very little or no conscious control to perform or execute an action once an intention is formed; also known as a sensorimotor behaviour. Performance is smooth, automated, and consists of highly integrated patterns of behaviour in most skill-based control (Rasmussen, 1990). For example, bicycle riding is considered a skill-based behaviour in which very little attention is required for control once the skill is acquired. This automaticity allows operators to free up cognitive resources, which can then be used for higher cognitive functions like problem solving (Wickens & Hollands, 2000).

Rule-based level

A rule-based behaviour is characterised by the use of rules and procedures to select a course of action in a familiar work situation (Rasmussen, 1990). The rules can be a set of instructions acquired by the operator through experience or given by supervisors and former operators.

Operators are not required to know the underlying principles of a system, to perform a rule-based control. For example, hospitals have highly-proceduralised instructions for fire emergencies. Therefore, when one sees a fire, one can follow the necessary steps to ensure the safety of the patients without any knowledge of fire behaviour.

Knowledge-based level

A knowledge-based behaviour represents a more advanced level of reasoning (Wirstad, 1988). This type of control must be employed when the situation is novel and unexpected. Operators are required to know the fundamental principles and laws by which the system is governed. Since operators need to form explicit goals based on their current analysis of the system, cognitive workload is typically greater than when using skill- or rule-based behaviours.

References

Burns, C. M. & Hajdukiewicz, J. R. (2004). Ecological Interface Design. Boca Raton, FL: CRC Press. ISBN 0415283744
Rasmussen, J. (1983). Skills, rules, knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on Systems, Man and Cybernetics, 13, 257-266.
Rasmussen, J. (1985). The role of hierarchical knowledge representation in decision making and system management. IEEE Transactions on Systems, Man and Cybernetics, 15, 234-243.
Rasmussen, J. (1990). Mental models and the control of action in complex environments. In D. Ackermann, D. & M.J. Tauber (Eds.). Mental Models and Human-Computer Interaction 1 (pp.41-46). North-Holland: Elsevier Science Publishers. ISBN 044488453X
Rasmussen, J. & Vicente, K. J. (1989). Coping with human errors through system design: Implications for ecological interface design. International Journal of Man-Machine Studies, 31, 517-534.
Vicente, K. J. (1999a). Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work. Mahwah, NJ: Erlbaum and Associates. ISBN 0805823972
Vicente, K. J. (1999b). Ecological Interface Design: Supporting operator adaptation, continuous learning, distributed, collaborative work. Proceedings of the Human Centered Processes Conference, 93-97.
Vicente, K. J. (2001). Cognitive engineering research at Risø from 1962-1979. In E. Salas (Ed.), Advances in Human Performance and Cognitive Engineering Research, Volume 1 (pp.1-57), New York: Elsevier. ISBN 076230748X
Vicente, K. J. (2002). Ecological Interface Design: Progress and challenges. Human Factors, 44, 62-78.
Vicente, K. J. & Rasmussen, J. (1990). The ecology of human-machine systems II: Mediating "direct perception" in complex work domains. Ecological Psychology, 2, 207-249.
Vicente, K. J. & Rasmussen, J. (1992). Ecological Interface Design: Theoretical foundations. IEEE Transactions on Systems, Man and Cybernetics, 22, 589-606.
Wickens, C. D. & Hollands, J. G. (2000). Engineering Psychology and Human Performance (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 0321047117
Wirstad, J. (1988). On knowledge structures for process operators. In L.P. Goodstein, H.B. Andersen, & S.E. Olsen (Eds.), Tasks, Errors, and Mental Models (pp.50-69). London: Taylor and Francis. ISBN 0850664012

External links

Institutions and organisations

Advanced Interface Design Lab (AIDL), University of Waterloo
Cognitive Engineering Lab (CEL), University of Toronto
Cognitive Engineering Research Group (CERG), University of Queensland
Human Factors and Ergonomics Society
IEEE Systems, Man and Cybernetics Society