The goal of EID is to make constraints and complex relationships in the work environment perceptually evident (e.g. visible, audible) to the user.
EID borrows from ecological psychology in that the constraints and relationships of the work environment in a complex system are reflected perceptually (through an interface) in order to shape user behaviour.
To date, EID has been applied in a variety of complex systems including computer network management, anaesthesiology, military command and control, and aircraft (Vicente, 2002; Burns & Hajdukiewicz, 2004).
Rapid advances in technologies along with economic demands have led to a noticeable increase in the complexity of engineering systems (Vicente, 1999a).
A complex sociotechnical system designed based solely on known scenarios frequently loses the flexibility to support unforeseen events.
Ecological interface design attempts to provide the operators with the necessary tools and information to become active problem solvers as opposed to passive monitors, particularly during the development of unforeseen events.
Interfaces designed following the EID framework aim to lessen mental workload when dealing with unfamiliar and unanticipated events, which are attributed to increased psychological pressure (Vicente, 1999b).
In addition to providing operators with the means to successfully manage unanticipated events, EID is also proposed for systems that require users to become experts (Burns & Hajdukiewicz, 2004).
Through the use of the Abstraction Hierarchy (AH) and the Skills, Rules, Knowledge (SRK) framework, EID enables novice users to more easily acquire advanced mental models that generally take many years of experience and training to develop.
In the pre-UCD era, interface design was almost an afterthought to a program and was completely dependent on the programmers while totally neglecting the end user.
EID focuses on understanding the complex system – its build, its architecture, and its original intent and then relaying this information to the end user thereby reducing their learning curve and helping them achieve higher level of expertise.
The abstract function (AF) level describes the underlying laws and principles that govern the goals of the 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).
A refrigerator may consist of heat exchange pipes and a gas compressor that can exert a certain maximum pressure on the cooling medium.
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.
The two representations are closely related but are usually developed separately because doing so results in a clearer model which captures most of the system constraints.
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).
Performance is smooth, automated, and consists of highly integrated patterns of behaviour in most skill-based control (Rasmussen, 1990).
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).
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.