developments in ecological interface design for visual and auditory displays. *To whom ... Worker competency analysis describes the behaviors and skills that .... (hierarchical task analysis and control task analysis) which result in three sets of.
ADVANCES IN COGNITIVE WORK ANALYSIS AND THE DESIGN OF ECOLOGICAL VISUAL AND AUDITORY DISPLAYS T. Claire Davies Catherine M. Burns* Systems Design Engineering University of Waterloo ABSTRACT Careful consideration must be given to the design of a display to reduce the possibility of information overload and to ensure that the information is presented in an effective manner. In designing an easy to interpret interface, the needs of the user must be paramount. Cognitive work analysis is one possible approach for determining user needs and display requirements. Most of the advances in cognitive work analysis over the last decade have focused on making the method more robust across a range of applications and more feasible in constrained project environments. Similarly ecological interface design has grown from a boutique design method to a credible and feasible way to develop interfaces for complex systems that can reliably improve human performance. In this paper we review the recent advances in cognitive work analysis as well recent developments in ecological interface design for visual and auditory displays. *To whom correspondence should be addressed
INTRODUCTION
For the most part, engineers have worked on the theory that engineers design, people adapt. Peter Meijer, who designed the vOICe system to enable individuals with functional
blindness to visualize their surroundings using sound states that, “a method to understand the learning process is sorely needed to allow for more efficient adaptation” (Jones, 2004). Rather than seeing the need to develop the product to meet the needs of the user, he claims we must better understand how the user learns to use the device to enable improved training. But what about the corollary? For three decades, cognitive systems engineering has attempted to provide frameworks to aid in the analysis and design of complex, human-technology systems to enable effective work environments designing systems that reduce the need for users to learn to adapt to unnecessarily complex designs. These frameworks have led to the development of interactive environments in which individuals, teams, and technology can work effectively and efficiently, adapting to the work itself, not the technology. Cognitive Work Analysis (CWA) and Ecological Interface Design (EID) are two such frameworks that aim to analyze the work environment and present information to a user in a cognitively appropriate way. In this paper we will review CWA and EID focusing on recent developments in these methods.
COGNITIVE WORK ANALYSIS The concept of CWA as described by Vicente (1999), and adapted from earlier descriptions of Rasmussen (Rasmussen, Pejtersen, & Goodstein, 1994) consists of a five phase, iterative analysis focusing successively on constraints inherent in the work environment. One of the motivations behind CWA is the argument that the analysis of user tasks, while necessary, is not sufficient for a full description of a work environment. As a result, and arising out of these five phases of analysis, CWA provides multiple perspectives on how people work -- from tasks, to physical work complexities, to
strategies, social-organizational factors and individual competencies. Work domain analysis (WDA) creates a functional description of the objects and relationships in the work environment at different levels of abstraction with the goal of revealing physical relationships inherent in the work, and also the purposes and priorities of the work system. Control task analysis describes the fundamental tasks which must occur in terms of information observation, ambiguity resolution, procedures, tasks, and the shortcuts that can occur within these tasks. A control task analysis has some similarities to a cognitive task analysis, but is also motivated by finding fundamental or modal tasks and revealing opportunities for more direct approaches. Strategies analysis looks at various different ways that these tasks can be performed and can highlight the need for strategy shifts based on initial information conditions or workload levels. Social and organizational analysis can provide different ways of examining the organizational or macro-cognitive influences on work. Worker competency analysis describes the behaviors and skills that individual workers must have to work within the work domain. In the next sections we will briefly review recent work in these phases of CWA (Table 1 summarizes these phases and provides relevant references to research applying analyses at these phases). CWA Phase Work domain analysis
Control task analysis
Strategies analysis
Definition A description of the objects and relationships in the work environment based on how they function Different levels of abstraction describe objects, processes, priorities and balances, and purposes of the work domain Reveals complexities in physical relationships or balancing of purposes and priorities A description of the fundamental tasks which must occur in terms of information observation, ambiguity resolution, procedures and tasks The decision ladder can show expert and novice differences, shortcuts, communication flows and task handoffs Within tasks, looks at the various ways these tasks can be performed.
Relevant Articles Rasmussen, 1985; Vicente,& Rasmussen 1992; Burns & Hajdukiewicz 2004; Burns, Bisantz & Roth, 2004; Jamieson, Ho, Miller, & Vicente, 2007; Bisantz & Mazaeva, 2008 Rasmussen & Jenson, 1974; Cummings & Guerlain, 2003; Naikar, Moylan, & Pearce, 2006; Lamoureux & Chalmers, 2008 Rasmussen, 1986; Burns, Enomoto & Momtahan,
Social and organizational analysis Worker competency analysis
Strategies may be different under different workload levels, for experts vs. novices, and under different physical configurations of work. Provides different ways of examining the organizational or macro-cognitive influences on work. Describes the behaviors and skills that individual workers must have to work within the work domain.
2008
Vicente, 1999; Pfautz & Pfautz; 2008 Vicente, 1999; Kilgour, StCyr, & Jamieson, 2008
Work Domain Analysis WDA is one of the most informed and well developed phases of CWA and the phase of CWA that has been most used in computer interface designs (e.g. EID, Burns & Hajdukiewicz, 2004). WDA is an effective tool for describing complex environments where physical constraints can influence user actions, where environments are so complex that users rarely fully understand them, or social constraints must also be considered in decision making. Examples where WDA has been applied include petrochemical plants (Jamieson, Ho, Miller & Vicente, 2007), nuclear plants (Burns, 2000), and healthcare (Sharp & Helmicki, 1998). For example, Burns (2000) examined fault-detection in a simulated power plant with displays based on the spatial and temporal proximity of related information objects. She found that when users have functional information on how to achieve purposes in close proximity to physical object information, they diagnose faults more quickly. Recent developments in WDA have involved the modeling of systems with automation (Bisantz & Mazaeva, 2008), modeling of systems with intentional social constraints that interact with physical constraints such as emergency response systems (e.g. Bisantz & Mazaeva, 2008) or military systems (e.g. Burns, Bryant & Chalmers, 2005). Methodologically, researchers have examined differences between analysts modeling similar systems and shown that analysts are
reasonably consistent in applying the fundamentals of the method (Burns, Bisantz & Roth, 2004, and Burns & Hajdukiewicz, 2004, Chapter 6). Authors have also shown that WDA can be useful for more than just interface design, applying it to the assessment of new technologies (Naikar & Sanderson, 2001) and the design of teams (Naikar, Pearce, Drumm & Sanderson, 2003). For example, Naikar et al. (2003) showed how work patterns and the problems arising in teamwork for a multi-skilled team engaged in a complex decision making task could be better illuminated using these techniques and, in particular, teams for novel “first of a kind” systems could be designed before the technical details of the new system were known. Control Task Analysis Control task analysis, sometimes called decision ladder analysis, has been used by many people to describe complexities of various tasks. Good examples of the use of control task analysis include Lamoureux and Chalmers (2008) and Cummings and Guerlain (2003). Recent work in control task analysis has focused on showing how it connects with other analyses and various practical methods of conducting a control task analysis (Lamoureux & Chalmers, 2008) and how the analysis can been used in the design and planning of new systems (Sanderson & Naikar, 2007). For example, Lamoureux and Chalmers (2008) showed how a control task analysis could be quickly operationalized using common scenarios, and how the data could be annotated, mapped to traditional information processing stages, and used in developing new designs. Strategy Analysis The third phase of CWA is to look at worker strategies. Strategy analysis is a particularly informative phase as it reveals various alternative paths to managing work.
Strategy analysis can show the difference between how workers manage different workload levels and between novice and expert workers. Strategy analysis can occur in two different ways. First, in small well understood systems, one may be able to analytically determine strategies (e.g. Vicente, 1999). Second, in larger more complex systems, knowledge elicitation methods can reveal the most common worker strategies. Recent examples of strategy analysis include Roth (2008) for strategies of railroad operations and Burns, Enomoto and Momtahan (2008) for strategies of cardiac care nurses. For example, Roth (2008) found that railroad operators used several strategies for staying aware of what other operators were working on, showing the need for support for team situation awareness in this environment. Strategy analysis can be one of the most effective informants to a design that matches well with worker needs. Social and Organizational Analysis There are several approaches for analyzing the constraints of social-organizational context on cognitive work. One way is to look at social-organizational constraints in terms of their function along the dimensions of a work domain analysis. This approach can be seen in Burns et al. (2005) where it was shown that social-organizational constraints within a military environment could be mapped on to a work domain analysis in the same manner as physical work constraints. Another approach is to look at social organizational influences on control tasks in a control task analysis. A third approach, discussed by Pfautz and Pfautz (2008), applies social network analysis to study cognitive work. As the study of social-organizational factors increases, this will be a stronger and more commonly seen aspect of analyzing cognitive work. Worker Competencies Analysis
Worker competencies analysis is one area of CWA that has received the least amount of attention. One exception includes an analysis by Kilgore and St-Cyr (2006) using Rasmussen’s SRK taxonomy. This work evaluates psychological processes in terms of whether they are skill-based, rule-based and knowledge-based in the context of an air traffic control task. Kilgore and St-Cyr (2006) found that by describing worker competencies using the SRK taxonomy they were able to generate specific design ideas tailored to each type of cognitive competency required of the workers. For example, skill based behavior could be supported by visual cues such as the separation between aircraft; rule based behavior by access to commonly used control rules; and knowledge based behavior by showing complexities and tradeoffs in the environment.
FROM CWA TO EID Over the last decade, the phases of CWA have become more defined and there are many more examples in the literature. The most notable developments though have been in adding a richer set of knowledge elicitation techniques, “how to” techniques, and intermediate modeling artifacts. The overall impact of these new developments has been to shorten the analysis time required when using CWA, improve its learnability, and decrease the gap between analysis and design. While ten years ago, it could be argued that a CWA was lengthy, time consuming and hard to learn, there are multiple examples now of how CWA can be used effectively a in timely manner to generate useful design insights. There is still room for development and exciting applications of CWA, but CWA has matured to the phase where it should be considered an important tool in every cognitive engineer’s toolbox.
While CWA aims to enable a better distribution of work organisation with applications extending successfully to the areas of evaluating design proposals, designing teams, examining training needs, and defining training requirements (Naikar, Moylan and Pearce, 2006; Naikar et al., 2003; Naikar & Sanderson, 2001), one of the primary purposes behind conducting a CWA is to inform the design of new systems. Ecological Interface Design (EID) is a systematic approach to the design of interfaces for complex systems (Burns & Hajdukiewicz, 2004) that aims to understand cognitive work and where possible, exploit perception to make cognitive work easier. There are several ways of doing this – one is to use a CWA or other cognitive systems analysis to develop a deep understanding of the user`s work requirements; the other way is to study the perceptual information and invariants in the environment that could be useful informants to human action. Conceptually these approaches may be different in methodology but the underlying objectives are the same: to aid and improve human performance through the proper display of information. EID emerged primarily from the nuclear power domain as a method to determine and display the complexities of power plant control. The earliest studies on EID focused on showing the work domain analysis as an analytical tool, and testing the performance of resulting displays on small system simulation that provided heated water (the Dual Reservoir System, DURESS). Focusing on how the CWA identified areas of the system needing information scaffolding to increase comprehension, these studies compared the effect of adding additional information with comparable displays that did not contain that information. For the revised displays, the results generally found stronger fault diagnosis
performance and recovery strategies in unanticipated situations (e.g. Pawlak & Vicente, 1996; Vicente, 2002). Recent research in EID has been less focused on demonstrating the fundamentals of the approach, but more attentive to exploring the design aspects of ecological displays, the connection between ecological displays and other cognitive engineering based approaches to design, and extensions into auditory and haptic display. Designing with CWA and Task Analysis Methods CWA can be used in conjunction with cognitive task analysis methods; one approach does not rule out the other, and indeed the best designs will be derived from both perspectives. Evaluations of ecological displays have regularly pointed out that the designs could be further improved by incorporating information on task performance (e.g. Burns et. al., 2008; Burns & Hajdukiewicz, 2004). Jamieson et al. (2007) shows the benefits of using both analyses very effectively. Specifically, WDA enables an awareness of unforeseen circumstances to be evident whereas task analysis provides for foreseen contingencies (Jamieson et al., 2007). Jamieson et al. integrate these two methods of analysing the environment and processing system requirements to provide a more robust interface design for a petrochemical plant. The details of their work discuss the independent examination of both the WDA and two different Task Analysis strategies (hierarchical task analysis and control task analysis) which result in three sets of informational requirements for the system. The authors integrate these requirements in the design of an ideal interface in a supervisory control task. From this, they illustrated how task-based and work domain-based analysis frameworks can be used to uncover unique and complementary information system requirements.
Designing for Better Cognition EID emerged as an approach for problem solving in a unique type of problem, unanticipated situations with the earliest studies of EID focusing on this contribution. For EID to be an effective mainstream approach though, we need to understand the effect of EID on cognition in general. While more work remains to be done there is some early work showing that EID can help build useful mental models (St-Cyr & Burns, 2002) and may reduce mental workload in complex tasks (Garabet & Burns, 2004). Next we discuss one of the more thorough investigations of EID, that is, its effect on situation awareness. Situation Awareness and EID Situation awareness is generally viewed as the perception and understanding of elements within ones environment at a given point in time and the projection of that understanding into some future state (e.g., Endsley, 1995). It is argued to be critical to decision making and problem solving in real-world settings when an operator is faced with a multitude of informational cues that must be rapidly and continually processed and updated. For example, situation awareness is discussed in domains ranging from aviation (e.g., Sarter & Woods, 1991) and anesthesiology (e.g., Gaba, Howard, & Small, 1995), to command and control (e.g., Gorman, Cooke, & Winner, 2006), and emergency medical teams (e.g., Blandford & Wong, 2004). Within this overall context, a number of researchers have noted that effective implementation of EID should provide a work environment supporting superior levels of situation awareness (e.g., Roth, Lin, Kerch, Kenney & Sugabayashi, 2001; Li, Sanderson, Memisevic, & Wong, 2007; Burns et. al, 2008).For example, in a study of large screen
displays to support individual and team information processing, Roth et al., (2001) found that ecologically designed overview displays could improve operator SA. Similarly, in their study of performance in hydropower plant control systems, Li et al. (2007) found that ecologically inspired displays improved SA content in operator verbalizations. Burns et al, (2008) examined the ability of EID to improve perception, comprehension and projection of the operator following the SA model of Endsley (1995). The results of this study produced an interesting effect based upon the nature of the sub-task. Specifically, this research showed an improvement in SA with ecological displays in the detection phase of scenarios that had not previously been experienced by the operators, but did not increase SA during normal procedure-based scenarios. The authors indicate that task analysis in combination with WDA within the EID framework might enable development of a display that enhances user response in scenarios that are both procedural and unanticipated.Most systems that use EID have been visual, but EID has also been applied to other modes of perception. In haptic interfaces, Stoner, Weise and Lee (2003) applied EID to the design of a haptic display for driving. Xin, Burns and Zelek (2006) used EID in the design of a haptic display for laparascopic surgery. They found that vibrotactile force feedback cues may be an effective means to reduce excessive use of force during surgery. In the next section we discuss applications of EID to auditory interfaces. Integration of EID and CWA in auditory interfaces EID is typically applied to a visual environment, but can be used in combination with other aspects of cognitive work analysis such as strategy analysis, social organisation analysis, attentional mapping and worker competencies analysis in the design of auditory
environments (Sanderson et al., 2000). Strategy analysis aids in identifying the different methods of doing tasks identified in the earlier analyses. Redundancy in auditory systems can be accomplished by matching visual and auditory techniques to allow for prompt detection. Social organisation analysis involves the determination of work sharing. For instance, Buxton used earcons in identifying user tasks in collaborative environments (Buxton & Moran, 1990). Earcons are “auditory icons” used to signal important events about which an operator should be aware. These enable feedback not only about tasks within the domain of the individuals themselves but about the tasks in the environmental domain. A surgeon can be aware of the patient’s anaesthesiology needs while maintaining his/her concentration on the task at hand (Sanderson & Watson, 2005). This can be achieved by determining the specific tasks requiring auditory and visual focus is important and this can be established with the aid of attentional mapping (Sanderson & Watson, 2005). Finally, the worker competencies analysis enables detection of those tasks which are skill-, rule- or knowledge-based and need to be supplied either visually or through sound to enable a response by the user. Especially important for auditory displays, the environment in which the system will be used, must be given due consideration. Also, the ability to revisit information that has been previously presented and the amount of information that can be reviewed is critical. For example, looking back through visual records or graphs is fairly easy, whereas this is not necessarily a simple task for information transmitted with sound (Sanderson & Watson, 2005). Auditory displays that combine EID and CWA effectively can enable reduction in cognitive load by capitalizing on other channels of sensory information. Davies, Burns and Pinder (2007) designed an auditory interface to allow detection of environmental
obstacles by audification of ultrasound for individuals who cannot perceive visual information. EID is very effective at defining what information needs to be displayed to the user and the breadth of the design in the auditory domain (Sanderson et al, 2000). This project involved the development of an auditory interface that provides information about the whole field of view in a manner that is easy to interpret and designed with the needs of the user in mind. The work domain analysis of EID enables the designer to understand what critical information is available and must be provided to the user. The social implications of auditory information must then be considered. In this case, earphones were used to reduce societal interaction, as the auditory information was only required for the traveller. Next to be addressed is the phase of attentional mapping. To avoid the problem of masking other environmental sounds, external receivers were used that enabled both ultrasound and auditory signals to pass through allowing the user both signals from the system and auditory signals from the surroundings. Implementing the SRK taxonomy was used in determining how to best balance information with cognitive load. As Sanderson (2000) suggests, the idea is to move toward auditory systems requiring skill- or rule-based behaviour so that human error can be reduced. Semantic mapping of the information to the sound is often difficult, but by attempting to use a skill-based audification technique rather than a knowledge-based sonification one can attempt to reduce cognitive load (Sanderson, 2000). For this system, audification of the ultrasound signal resulted in the detection of echoes off environmental obstacles, thus a skill-based behavioural response. By combining components of both EID and CWA, a system was developed that upon human testing appears to show that
direct perception of environmental obstacles through audified ultrasound is possible (Davies, 2008).
SUMMARY Cognitive work analysis can provide important information in the design of effective human-machine systems and cognitive technologies. Recent work in this area has focused on making these methods easier to use, and more applicable to a wide range of situations. CWA is a useful addition to other cognitive engineering methods. Designs resulting from CWA methods have been shown to enable robust performance in unanticipated situations, but more recently research has shown that these designs promote effective cognition. Applications of ecological displays to auditory and haptic displays present great opportunities to further help people work in complex environments.
REFERENCES Bisantz, A.M. & Mazaeva, N. (2008). Work domain analysis using the abstraction hierarchy: Two contrasting cases. In A.M. Bisantz & C.M. Burns (Eds.) Applications of Cognitive Work Analysis. (pp. 49-68). Boca Raton, Fl: CRC Press. Bisantz, A. M. & Roth, E. M. (2008). Analysis of Cognitive Work. D. Boehm-Davis (Ed.) Volume 3 of the Annual Review of Human Factors and Ergonomics, Human Factors and Ergonomics Society, Santa Monica, CA. Bisantz, A. M., Roth, E., Brickman, B., Gosbee, L. L., Hettinger, L., & McKinney, J. (2003). Integrating cognitive analyses into a large scale system design process. International Journal of Human-Computer Studies, 58, 177-206.
Blandford, A. & Wong, W. (2004). Situation awareness in emergency medical dispatch. International Journal of Human–Computer Studies, 61, 421–452. Burns, C. M. (2000). Putting it all together: Improving display integration in ecological displays. Human Factors, 42, 226-241. Burns, C. M. Bisantz, A.M. & Roth, E.M. (2004). Lessons from a comparison of work models: Representational choices and their implications. Human Factors 46, 711727. Burns, C. M., Bryant, D. B., & Chalmers, B.A. (2005). Boundary, purpose and values in work domain models: Models of naval command and control. IEEE Transactions on Systems, Man and Cybernetics Part A 35, 603-616. Burns, C.M., Enomoto, Y. & Momtahan, K. (2008). A cardiac work analysis of cardiac care nurses performing tele-triage. In A. M. Bisantz, A.M. & C.M. Burns (Eds.) Applications of Cognitive Work Analysis. (pp.149-174). Boca Raton, Fl: CRC Press. Burns, C.M., Garrison, L.,& Dinadis, N., (2003). WDA for the petrochemical industry. Proceedings of the 47th Annual Meeting of the Human Factors and Ergonomics Society, 258-262. Burns, C.M. & Hajdukiewicz, J. (2004). Ecological Interface Design. Boca Raton, Fl: CRC Press. Burns, C.M., Jamieson, G.A., Skraaning, G., Lau, N., & Kwok, J. (2007). Supporting situation awareness through ecological interface design. Proceedings of the 51st Annual Meeting of the Human Factors and Ergonomics Society, 205-209.
Burns, C. M., Kuo, J., & Ng, S. (2003). Ecological interface design: A new approach for visualizing network management. Computer Networks, 43, 369-388. Buxton, W. & Moran, T. P. (1990). EuroPARC's Integrated Interactive Intermedia Facility (IIIF): Early Experiences. In S. Gibbs & A.A. Verrijn-Stuart (Eds.). Multiuser interfaces and applications, Proceedings of the IFIP WG 8.4 Conference on Multi-user Interfaces and Applications, 11-34. Cummings, M.L. & Guerlain, S. (2003). The tactical Tomahawk conundrum: Designing decision support systems for revolutionary domains. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics,1583-1588. Davies, T.C. (2008). Audification of ultra-sound for human echolocation. Unpublished Ph.D. dissertation, Systems Design Engineering, University of Waterloo, August, 2008. Davies, T.C., Burns, C.M., & Pinder, S. (2007). Testing a novel auditory interface display to enable visually impaired travelers to use sonar mobility devices effectively. Proceedings of the 51st Annual Meeting of the Human Factors and Ergonomics Society, 278-282. Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64. Enomoto, Y., Burns, C. M., Momtahan, K., & Caves, W. (2006). Effects of visualization tools on cardiac telephone consultation processes. 50th Annual Meeting of the Human Factors and Ergonomics Society, 1044-1048.
Gaba, D. M., Howard, S. K., & Small, S. D. (1995). Situation awareness in anesthesiology. Human Factors, 37, 20-31. Garabet, A. & Burns, C.M. (2004). Collaboration with ecological interface design. Proceedings of the 48th Annual Meeting of the Human Factors and Ergonomics Society, 543-546. Gorman, J. C. Cooke, N. J., & Winner, J. L. (2006). Measuring team situation awareness in decentralized command and control environments. Ergonomics, 49, (12-13), 1312–1325. Jamieson, G. A. (2007). Ecological interface design for petrochemical process control: An empirical assessment. IEEE Transactions on Systems, Man & Cybernetics, 37, 906–920. Jamieson, G. A., & Vicente, K. J. (2001). Ecological interface design for petrochemical applications: Supporting operator adaptation, continuous learning, and distributed, collaborative work. Computers and Chemical Engineering, 1055-1074. Jamieson, G. A., Ho, W. H., Miller, C. M., & Vicente, K. J. (2007). Integrating task- and work domain–based work analyses in ecological interface design: A process control case study. IEEE Transactions on Systems, Man, and Cybernetics, 37, 887–905. Jones, W. G. (2004, February). Sight for sore ears. IEEE Spectrum, 13-14. Kilgore, R. and St-Cyr, O., and Jamieson, G.A. (2008). From work domains to worker competencies: A five phase CWA. In A. M. Bisantz, A.M. & C.M. Burns (Eds.) Applications of Cognitive Work Analysis. (pp.15-48). Boca Raton, Fl: CRC Press.
Lamoureux, T. M. and Chalmers, B. A. (2008). Control task analysis: Methodologies for eliciting and applying decision ladder models in command and control. In A. M. Bisantz, A.M. & C.M. Burns (Eds.) Applications of Cognitive Work Analysis. (pp.96-128). Boca Raton, Fl: CRC Press. Li, X., Sanderson, P., Memisevic, R., & Wong, W. (2007). Adapting situational awareness measures for hydropower display evaluations. Proceedings of the Human Factors and Ergonomics Society 51st Annual Meeting, 210–214. Mazaeva, N. & Bisantz, A. M. (2007). On the representation of automation using a work domain analysis. Theoretical Issues in Ergonomics Science 8 (6), 1-22. Naikar, N., Moylan, A., & Pearce, B. (2006). Analysing activity in complex systems with cognitive work analysis: Concepts, guidelines, and case study for control task analysis. Theoretical Issues in Ergonomics Science, 7(4), 371-394. Naikar, N., Pearce, B., Drumm, D., & Sanderson, P. M. (2003). Designing teams for firstof-a-kind, complex systems using the initial phases of cognitive work analysis: Case study. Human Factors, 45, 202-217. Naikar, N., & Sanderson, P. M. (2001). Evaluating design proposals for complex systems with work domain analysis. Human Factors, 43, 529-542. Pawlak, W. S., and Vicente, K. J. (1996) .Inducing effective operator control through ecological interface design. International Journal of Human-Computer Studies, 44, 653688.
Pfautz, J. & Pfautz, S. (2008). Methods for the analysis of social organizational aspects of the work domain. In A. M. Bisantz, A.M. & C.M. Burns (Eds.) Applications of Cognitive Work Analysis. (pp.175-228). Boca Raton, Fl: CRC Press. 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. (1986). Information processing and human-machine interaction: An approach to cognitive engineering. New York: North Holland. Rasmussen, J. and Jensen, A. (1974). Mental Procedures in Real-Life Tasks: A Case Study of Electronic Troubleshooting. Ergonomics, 17, 293-307. Rasmussen, J., Pejtersen, A. M., & Goodstein, L. P. (1994). Cognitive Systems Engineering. New York: Wiley and Sons. Roth, E.M. (2008). Understanding cognitive strategies for shared situation awareness across a distributed system: An example of strategies analysis. In A. M. Bisantz, A.M. & C.M. Burns (Eds.) Applications of Cognitive Work Analysis. (pp.129148). Boca Raton, Fl: CRC Press. Roth, E. M., Lin, L., Kerch, S., Kenney, S., & Sugibayashi, N. (2001). Designing a firstof-a-kind group view display for team decisionmaking: A case study. In E. Salas & G. Klein (Eds.), Linking expertise and naturalistic decision making (pp. 113– 135). Mahwah, NJ: Erlbaum. Sanderson, P., Anderson, J., & Watson, M. (2000). Extending ecological interface design to auditory displays. Proceedings of the 2000 Annual Conference of the Computer
Human Interaction Special Interest Group of the Ergonomics Society of Australia (OZCHI2000),259-256. Sanderson, P. & Watson (2005). From information content to auditory display with ecological interface design:Prospects and challenges. Proceedings of the 49th Annual Meeting of the Human Factors and Ergonomics Society, 259-263. Sanderson, P. M. & Naikar, N. (2007). Temporal coordination control task analysis for analysing human-system integration. Proceedings of the 51st Annual Meeting of the Human Factors and Ergonomics Society, 206-209. Sarter, N. B. & Woods, D. D. (1991). Situation awareness: A critical but ill-defined phenomenon. International Journal of Aviation Psychology, 1, 45-57. Sharp, T. D., & Helmicki, A. J. (1998). The application of the ecological interface design approach to neonatal intensive care medicine. Proceedings of the 42nd Annual Meeting of the Human Factors and Ergonomics Society, 350-354. St-Cyr, O. & Burns, C. M. (2002). Mental models and ecological interface design: An experimental investigation. Proceedings of the 46th Annual Meeting of the Human Factors and Ergonomics Society, 270-274. Stoner, H. A., Wiese, E. E., & Lee, J. D. (2003). Applying ecological interface design to the driving domain: The results of an abstraction hierarchy analysis. Proceedings of the 47th Annual Meeting of the Human Factors and Ergonomics Society, 444448. Vicente, K. J. (1999). Cognitive Work Analysis. Mahwah, NJ: Erlbaum. Vicente, K. J. (2002). Ecological interface design: Progress and challenges. Human Factors, 44, 62-78.
Vicente, K., and Rasmussen, J. (1992). Ecological interface design: Theoretical foundations. IEEE Transactions on Systems, Man and Cybernetics 22, 1-18. Woods, D. (1991). The cognitive engineering of problem representations. In G. R. S.Weir and J. L . Alty (Eds), Human -computer interaction and complex systems. Academic Press, London. Xin, H., Burns, C.M. & Zelek, J.S. (2006). Non-situated vibrotactile force feedback and laparascopy performance. HAVE 2006. The IEEE International workshop on Haptic Audio Visual Environments. Ottawa, Canada. Nov 4-5.
