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...problems associated with design of automation in aviation and other domains (Bainbridge, 1987; Coury & Semmel. 1996: Woods, 1996), most of this research has focused on the effects of automation on an individual worker. Some research has suggested that automation may qualitatively change communication between human team members (Johannesen, Cook, & Woods, 1994; Wiener, 1993). With this in mind, there is a need to ascertain the effects of automation on teamwork in order to promote the design of safe and effective systems.
Prior research assessing the effect of automation on teams has produced contradictory results. We first review this research and offer possible explanations for the variety of results. We then suggest an alternative approach for assessing the effect of automation on teams by classifying automation based on its application to different human-machine system information-processing functions in accordance with existing theories of levels of automation (LOAs). In order to predict the effect of different LOAs on teams, we also review research assessing the effects of LOAs on the performance of individuals and research describing characteristics of high-perforating teams. The purpose of this study was to determine whether differences in the form of complex automation have implications for team coordination and performance, to explain these effects in terms of the functional nature of the automation, and to establish how automation may mediate the potential for coordinated teams to achieve high performance.
Effects of Automation on Teams
Existing research on flight deck automation and teams (Bowers, Deaton, Oser, Prince, & Kolb, 1995; Clothier, 1991; Costley, Johnson, & Lawson, 1989; Wise, Guide, Abbot, & Ryan, 1992) provides support for the notion that automation has some type of effect on team coordination; however, results are mixed. Some studies show increases in communication rates (Wise et al.), some show rate decreases (Costley et al.), some suggest detriments to team coordination (Bowers, Deaton, el al.), and some suggest coordination improvements (Clothier). One consistent result across studies on verbal communication and automation is that significant team coordination differences between systems tend to appear when workload is higher (Clothier; Costley).
A possible reason lot the conflicting findings is that studies generally compare team coordination under a new automated system with teamwork in an earlier model aircraft (either through surveys, field studies, or comparisons in high-fidelity simulators); therefore other differences, such as advanced displays, may be affecting team coordination. A second potential problem is that different forms of automation may influence team coordination in different ways. Bowers, Jentsch, and Salas (1994) offered some solutions to these problems, including (a) studying automation using low-fidelity simulation in a laboratory environment so that differences between conditions can be isolated to only those associated with automation and (b) clearly specifying the form of automation used--for example, through a taxonomy of automation.
Jentsch and Bowers (1996) applied these recommendations using a PC-based flight simulator in which the pilot find copilot performed with a single form of automation (autopilot and navigation computer, respectively) on or off. The autopilot represented automation of psychomotor tasks, whereas the navigation computer represented automation of more cognitive functions. They found that performance improved when both automated systems were in use. They also found decreased rates of communication associated with the use of the navigation computer (copilot automation), possibly because the copilot spent time interacting with the navigation computer rather than coordinating with the pilot.
Levels of Automation
Although Bowers el al. (1994) offered a taxonomy of automation for aviation, more general taxonomies and models have been developed to describe different forms of automation. Scerbo (1996) provided a review of taxonomies and models of LOAs that have evolved from the early work of Sheridan and Verplanck (1978). Sheridan and Verplanck's taxonomy distinguished 10 different LOAs that focused on who has decision authority
in system operations (the human or a computer), what information is provided to the user by the system, and who implements an action.
More recent taxonomies and models consider automation as applied to a wider range of functions (Endsley, 1997; Endsley & Kaber, 1999; Kaber & Endsley, 1997; Parasuraman, Sheridan, & Wickens, 2000; Wickens, Mavor, Parasuraman, & McGee, 1998). The taxonomy developed by Endsley and Kaber demonstrates how automation can be classified in terms of four cognitive and psychomotor aspects of human information processing: monitoring, generating, selecting, and implementing. Based on an analysis of real-world systems, they established 10 specific LOAs depending on whether or not the human or a computer performed each of the four information-processing functions or if the functions were shared. The function allocation scheme also defined the overall level of system autonomy.
Parasuraman et al. (2000) developed their model based on the premise that automation is a continuum and not an "all-or-none concept." Like Endsley and Kaber (1999), they identified four stages of human information processing that can be used as a basis for classifying automation: information acquisition, information analysis, decision selection, and action implementation. They said that automation can vary from low to high along unique continua for each information-processing function. The Parasuraman et al. model is a theoretical one that can be used to describe virtually any human-machine system in terms of the type and level of automation. Approaches to classifying LOAs, such as these, serve to qualitatively define the characteristics of automation systems so that they may be compared and evaluated in research.
Effects of LOAs on Individuals
Researchers have found that, similar to the goals of adaptive automation, providing the right degree of automation for the right function of a task can optimize the use of automation in terms of operator workload and situation awareness (SA; Endsley & Kaber, 1999; Endsley & Kiris, 1995; Kaber & Endsley, 1997; Parasuraman et al., 2000). Comparisons have been made of the effects of various LOAs on individual operator performance. Endsley and Kaber found that levels of automation that combine human generation of options with computer implementation of actions produced better overall performance during normal operations of a laboratory simulation of an air traffic control task. In a visual target identification task, Galster, Belie, and Parasuramen (2002) found performance advantages associated with information automation (cuing) that were not apparent when automation wets applied to decision making.
Kaber, Onal, and Endsley (2000). using a high-fidelity simulation of a telerobot, found that high levels of automation involving computer assistance in information analysis and action implementation, or assistance in these functions plus decision making, enhanced performance and reduced workload during normal operation conditions. Intermediate levels of automation including computer assistance in action implementation promoted higher operator SA and enhanced manual performance during system failure modes, as compared with higher levels of automation. Clamann, Wright, and Kaber (2002) found that operators were better able to adapt to adaptive automation when the automation was applied to information acquisition and action implementation than when automation was applied to information analysis and decision-making tasks. Rovira, Zinni, and Parasuraman (2002) found that automation unreliability had a greater cost for decision automation than for information automation. Sarter and Schroeder (2001) obtained the same results in investigating information and decision automation in pilot assessment of in-flight icing conditions.
In general, this body of work supports automation of psychomotor functions (e.g., automation of information acquisition and action implementation) and information automation as compared with automation of higher-order cognitive functions such as decision selection, particularly in situations in which the automation may be unreliable, adaptive automation is being used, or operators are exposed to high workload.
Characteristics of High-Performing Teams
Studies of team communication and coordination have noted specific types of behavior that are associated with good team performance (Costley et al., 1989; Foushee, Lauber, Baetge, & Acomb, 1986; Orasanu, 1990). It has been observed that teams in which members provide unsolicited information to other team members generally perform better than those that do not (Johannesen et al., 1994: Urban, Bowers, Monday, & Morgan, 1993). High-performing teams tend to be more efficient in their use of questions, asking fewer questions yet still receiving all the necessary information (Urban et al., 1995). High-performing teams also exhibit behaviors such as situation assessment and planning that help to achieve and maintain SA (Orasanu).
Jentsch, Sellin-Wolters, Bowers, and Sales (1995) found that teams that were faster in detecting a problem used more standard communications, made more leadership statements, and vocalized more SA observations than did slow teams. Bolstad and Endsley (1999, 2000) found that...
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