Ergonomics The classification of vigilance tasks in the ...

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The classification of vigilance tasks in the real world Fiona M. Donald

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School of Human and Community Development , University of the Witwatersrand , Johannesburg, South Africa. Private Bag 3, WITS 2050, South Africa Published online: 24 Oct 2008.

To cite this article: Fiona M. Donald (2008) The classification of vigilance tasks in the real world, Ergonomics, 51:11, 1643-1655, DOI: 10.1080/00140130802327219 To link to this article: http://dx.doi.org/10.1080/00140130802327219

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Ergonomics Vol. 51, No. 11, November 2008, 1643–1655

The classification of vigilance tasks in the real world Fiona M. Donald*

Downloaded by [University of Connecticut] at 12:49 17 December 2014

School of Human and Community Development, University of the Witwatersrand, Johannesburg, South Africa. Private Bag 3, WITS 2050, South Africa

The ability to generalise vigilance research to operational environments has been questioned, largely due to differences between laboratory research and real-world settings. The taxonomy of vigilance tasks proposed by Parasuraman and Davies (1977) represents an attempt to classify vigilance tasks so that tasks with similar informationprocessing demands can be compared and the ability to generalise results enhanced. Although the taxonomy originally included complexity, the term specifically referred to multiple sources of information. Complexity has been overlooked in much of the traditional vigilance literature, although it is included in more recent studies of jobs such as air traffic control. In this paper, the taxonomy is evaluated in relation to two vigilance intensive jobs – closed circuit television surveillance operators and air traffic controllers. In its present form, the existing taxonomy of experimental settings has limited applicability to these operational settings. Therefore, recommendations for expanding the taxonomy to include more aspects of complexity are made. It is argued that the revised taxonomy be used in conjunction with situation awareness, which makes provision for the cognitive processes involved in these jobs. Keywords: vigilance taxonomy; complex monitoring; situation awareness; CCTV surveillance; air traffic controller

1. Introduction Despite the plethora of studies regarding vigilance, their value has been strongly debated (for example, Mackie 1987, Wiener 1987, Pigeau et al. 1995, Sawin and Scerbo 1995, Koelega 1996). Until recently, most vigilance research has been conducted in laboratory settings rather than operational environments (Craig 1984). While some researchers have cited examples of the successful application of the findings (for example, Parasuraman et al. 1987), others have questioned their relevance and predictive value in real-world environments (Pigeau et al. 1995, Koelega 1996). A major reason for this is the predominant use of laboratory experiments and the difficulty in generalising the results to real-world operating environments. This is based on differences between vigilance tasks used in laboratory experiments and those faced by operators in work contexts. One of the key differences between these settings is the degree of complexity involved. This raises questions as to the predictive and external validity of much of the traditional vigilance research (Pigeau et al. 1995, Koelega 1996). The situation is further complicated by evidence of varying levels of vigilance performance and vigilance decrements for different jobs in operational environments (Parasuraman 1984). This contrasts with the

*Email: [email protected] ISSN 0014-0139 print/ISSN 1366-5847 online Ó 2008 Taylor & Francis DOI: 10.1080/00140130802327219 http://www.informaworld.com


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early belief that the vigilance decrement does not occur in complex monitoring situations due to the stimulation provided by such environments (Parasuraman 1987). Jobs requiring vigilance include cockpit monitoring, industrial process and quality control, anaesthesia monitoring, cytological screening, X-ray baggage screening, closed circuit television (CCTV) surveillance and air traffic control (Warm and Dember 1998). The aim of this paper is to evaluate the taxonomy in terms of its applicability to CCTV surveillance operators and air traffic controllers (referred to as operators and controllers respectively) who perform complex monitoring. These jobs require vigilance on a sustained basis during shifts typically varying from 8–12 h. Although rotation of positions occurs at some sites and there are breaks during the shift, personnel have to fulfil their monitoring responsibilities and maintain vigilance over extended periods of time. The environments of operators and controllers differ, with operators dealing with a high degree of routine and few detectable events or incidents, and controllers dealing with much larger amounts of significant data. Both these positions constitute appropriate contexts for the evaluation of Parasuraman and Davies’ (1977) taxonomy. It is noted, however, that reallife jobs are, in a sense, case studies and that the conclusions and recommendations based on these jobs may not apply directly to other jobs. Before analysing the work of operators and controllers, the vigilance taxonomy is discussed. This is followed by an explanation of situation awareness (SA), which provides an alternative approach to complexity. The jobs of operators and controllers are then analysed with a focus on two aspects of complexity, namely, complexity of the data used and of the overall job. Parasuraman and Davies’ (1977) taxonomy was developed specifically for displays used in laboratory experiments and did not take into account the broader job goals, demands and SA that apply in operational settings. However, overall job goals and the artefacts used, such as visual displays, are integrally related and should both be considered. 2. Vigilance taxonomy The taxonomy proposed by Parasuraman and Davies (1977) remains the most widely used classification system of vigilance tasks today (for example, See et al. 1995, Koelega 1996). Its authors emphasised the importance of the relationship between classification categories and specific information-processing transactions involved in vigilance tasks. This was based on intercorrelations between tasks that make similar information-processing demands and that therefore facilitate generalisation (Matthews et al. 1993). The dimensions in the taxonomy are signal discrimination type (successive or simultaneous, sensory or cognitive), event rate (low, high or continuous), sensory modality and source complexity. With simultaneous discrimination tasks, the signal and non-signal are present in the display at the same time and are both available for comparison. On the other hand, with successive discrimination tasks the signal and non-signal are not present at the same time and therefore the signal is distinguished from a non-signal through reference to a representation in the observer’s memory (Matthews et al. 1993). The terms redundant and orthogonal are used interchangeably with simultaneous and successive respectively. The two types of detection tasks differ in their information-processing requirements in that successive tasks require more mental effort, attentional capacity and working memory (Matthews et al. 1993). The second dimension in Parasuraman and Davies’ (1977) taxonomy is event rate. This is defined as the ‘rate of presentation of stimulus events in a vigilance task’


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(Parasuraman et al. 1987, p. 14) and includes background or neutral events that do not require a response. Sensory modality refers to visual or auditory stimuli. Finally, the taxonomy uses the term ‘source complexity’ to describe whether there are single or multiple sources of signals. Thus, complexity is used in a specific sense and does not refer to the nature of the data or interrelationships between them. Historically, starting in the 1960s, numerous studies have been conducted into task complexity and vigilance (for example, Howell et al. 1966, Noonan et al. 1985, Warm et al. 1985). However, these studies did not necessarily define complexity in the same way as Parasuraman and Davies in their taxonomy. The studies yielded conflicting results. This may be due to the different ways in which complexity was conceptualised, including a range of types of information, different processing requirements and varying types of response (Koelega 1996). This is related to the lack of an adequate definition of complexity. Over time, more emphasis has been placed on successive and simultaneous discrimination and high vs. low event rates than on the other categories, to the extent that the remaining aspects appear largely to have been dropped from the taxonomy (for example, See et al. 1995). Research provides general experimental support for the taxonomy, but a review of the literature indicates that it has seldom been applied to realworld jobs. 3. Situation awareness The taxonomy classifies tasks on the basis of characteristics that make different information-processing demands on the person. However, it says little about how data are combined, comprehended and updated in dynamic situations (Matthews et al. 1993). These aspects are incorporated in the concept of SA, which is recognised as a key contributor to performance in many jobs (Endsley 2001). SA has been defined in various ways (Kirlik and Strauss 2006, O’Brien and O’Hare 2007, Sinclair 2007), but Endsley’s (1988) qualitative model is adopted for this paper. SA is defined as: ‘the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future’ (Endsley 2000, p. 3). Three levels of SA have been identified (Endsley 2000). The lowest level is perception of important information, enabling the person to form an accurate picture of the situation. The second level involves comprehending the data and is reliant upon cognitive processes such as combining, pattern matching, interpreting, storing and retaining data. In short, integrating data is crucial to creating meaning and understanding implications. Experts demonstrating the third level of SA are able to predict future events based on current data, enabling decisions to be made on time. Accurate SA is critical in making correct decisions about situations (Wright et al. 2004). The perception of time and space is an important aspect of SA, especially where decisions are time critical (Endsley 2000). Job incumbents need to know how much time is available before an event occurs or a decision or action should be taken. Similarly, they need to know how far away in space an event is occurring. The dynamic rate at which information and situations change is also crucial to SA. In many environments, data and situations constantly change and job incumbents’ knowledge is constantly updated to maintain SA. Correspondingly, cognitive strategies need to be continuously altered for SA to remain current (Nunes and Mogford 2003). SA is derived from a variety of direct, indirect, subtle, geographically distributed or remote sources (Endsley 2000). These sources of information are external in the form of


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salient stimuli (e.g. CCTV monitors, radar displays) and internal (e.g. goals, expectations, mental models, learnt scan patterns, information sampling strategies and previously acquired data) to the job incumbent. Internal and external sources interact so that the process of perceiving information is not passive, but involves selecting relevant data. In some environments, people control the information they receive (for example, by operating a camera), based on environmental, system and interface knowledge. This longterm knowledge is embodied in mental models that are used to guide attention to relevant data, integrate and understand them and make mental abstractions to predict future events and states (Nunes and Mogford 2003). Mental models are integral to SA, which guides both the process of assessing situations and resulting updated SA. In this way, both exogenous and endogenous attentional processes are involved in SA. In the following sections, the jobs of operators and controllers are discussed in relation to the taxonomy and SA. 4.

Closed circuit television surveillance operators

The purpose of the operator’s job is to monitor, detect and respond to incidents or signals that threaten the safety of people or property. This applies to contexts such as transport (e.g. railway stations and trains, buses, airports, highways, pedestrian crossings), commercial (e.g. offices), financial (e.g. banks), industrial (e.g. factories), tourist (e.g. theme parks), residential and town centre environments. The following are discussed in turn: the type of images displayed; the nature of incidents to be detected; the degree of technological automation; the range of activities performed by operators. Comments regarding the taxonomy and SA are interspersed throughout. The primary artefact used by operators is the visual CCTV display. CCTV systems typically consist of numerous cameras, monitoring and recording systems and control room operations. Each camera feeds through to a display. Operators typically observe banks of multiplex displays, with each monitor divided into areas devoted to a particular camera. Operators typically observe anything from three to 30 camera scenes, yielding multiple sources of data. Given the numerous displays, mental models of the interrelationships between different cameras and scenes are needed to anticipate and track movement. This goes beyond the taxonomy but is included in SA. Divided attention, selective attention, effective scan patterns and a large working memory capacity are needed to operate the cameras and process the numerous sources of data. Thus, internal and external attentional guidance is required for effective level 1 SA. Systems vary in the degree of technological advancement and demands they place on operators. More technologically advanced multi-media systems transmit data from a variety of types of sources, such as video cameras, motion sensors or text messages. Thus, the data are not only derived from multiple sources (as in the vigilance taxonomy), but are also heterogeneous in nature. In addition to differences between the technological systems operated in CCTV surveillance and experimental situations, there are significant differences in the nature of the displays observed. The scenes observed by operators include naturalistic settings, such as railway platforms, streets or factory floors. The backgrounds of scenes are seldom empty, as was typically the case with earlier vigilance and visual search research (Wolfe et al. 2002). Backgrounds in operational settings may be cluttered and contain a variety of types of images. This contrasts with laboratory research, where only three to four types of simple stimuli are usually included (Moray 1984). Given the relationship between display background complexity and the length of time taken to accumulate the information


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required to identify a selected object (Wolfe et al. 2002), the performance of operators and experimental participants may differ. Operators are likely to take longer to detect incidents than contexts using simpler stimuli. Display complexity is increased by the fact that displays are 2-D representations of 3-D scenes and events and objects may be hidden or partially obscured. This is particularly relevant to situations where people deliberately obscure objects and situations from the camera’s view to avoid detection. Consequently, abstraction and prediction are required in interpreting scenes. Thus, levels 2 and 3 SA are needed for optimal performance. The displays monitored by operators are dynamic and typically contain one or more moving objects, representing a continuous event rate. Although continuous event rates are included in the taxonomy, traditional vigilance research has focused more on discrete events that can be measured and classified as being high or low (Moray 1984). This may be due to the difficulty in measuring continuous event rates when several objects change at the same time, at different rates, in different directions, as with CCTV surveillance. This restricts the usefulness of the taxonomy. For operators, continuous display changes require the updating of situational awareness on an ongoing basis (Endsley 1995), introducing additional information-processing demands that are seldom present in traditional vigilance research. The nature of incidents to be detected by operators differs from those traditionally used in vigilance research. Significant events include, for example, the presence or behaviour of people, changes in process, anomalies in expected conditions, verification of standard operating conditions or protocols or the detection of specific threats or circumstances (Aldridge 1994). These events may, for example, involve small, covert, transient actions that take merely a moment to execute, recognising patterns or trends in behaviour or identifying intermittent events that are embedded in the cluttered changing backgrounds of displays. Thus, the perceptual salience of relevant data varies and the incidents themselves consist of complex images. Recognising complex situations that occur in real-life environments requires consideration of a range and diversity of elements and possible combinations of signals, conditions and characteristics (Mackie et al. 1994). Successful operators use non-verbal cues and patterns to comprehend actions and predict incidents in a proactive manner. Thus, operators require levels 2 and 3 SA, where they interpret data, match patterns, create meaning and anticipate future events. This contrasts with traditional vigilance research, where little knowledge other than the form of the signal is required for detection and the emphasis is on level 1 SA. Similarly, the taxonomy makes provision for tasks requiring level 1 SA, but little levels 2 and 3 SA. For operators, the detection task is orthogonal. However, it is based on broad situational prototypes or mental models instead of exact replicas (Endsley 1995) as with most vigilance research. The use of mental models provides sufficient scope for operators to detect events whose precise form is not known before they occur and allows for detection in the face of uncertainty and novelty (Kirlik and Strauss 2006). This contrasts with traditional vigilance research where the exact nature of the critical signal is known prior to the experimental trial (Moray 1984). Automated real-time machine intelligence is increasingly being built into surveillance systems, albeit in differing degrees. At a simple level, motion sensors trigger alarms in the form of text messages or auditory alarms. These are used, amongst other things, to alert operators to the presence of people in restricted areas. Thus, visual and auditory channels are used, placing different information-processing demands on operators. Other systems that use greater machine intelligence are aimed at reducing the operator’s workload and preventing human error. These include, for example, motion-based machine vision


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technology for controlling large crowds (Boghassian and Velastin 1999), identifying congestion in underground railway platforms (Lo and Velastin 2001) and detecting abandoned objects in railway stations (Sacchi and Regazzoni 2000). Systems for automatically identifying problematic behaviours in public places, such as drunkenness, are available (Arsic et al. 2007). Similarly, the movement of people and objects can be tracked automatically in certain environments (Rao et al. 1993). Digital systems have brought the capability for machine intelligence, but much of their potential has yet to be realised. Where machine intelligence is used to provide information on significant events, operators are still required to interpret the information and respond to it. Once again, this contrasts with traditional vigilance research conducted prior to the mid-90s where little interpretation was needed and decision making was based largely on salient external stimuli with limited use of simple mental models. Depending on the system design and degree of automation, technology assists with the perception of data by drawing the operator’s attention to potential events. This assists with perceiving events and represents level 1 SA. However, in addition to data provided by technology, internal attentional processes also guide the perception of information. Despite the assistance of technology, levels 2 and 3 SA are largely dependent on the operator. In addition to controlling and monitoring the CCTV surveillance system, operators perform a range of other tasks. These include, for example, responding to situations and signals they observe, liaison with other parties and services (e.g. emergency services, police, traffic authorities), accessing various databases (e.g. vehicle registrations), following up on incidents and writing reports. Depending on how the tasks are structured, these activities may distract from the monitoring and detection tasks. Unlike experimental participants, operators are part of broader systems and perform other tasks that add to workload and job complexity. Therefore, the taxonomy of vigilance tasks should take into consideration job responsibilities that are performed concurrently with monitoring and that may add to mental workload and influence SA. At a simple level, aspects of the operator’s job can be classified using the taxonomy. Thus, it could be categorised as orthogonal, sensory and cognitive, visual and auditory, complex and with a continuous event rate. However, these descriptors fail to capture the complexity of major aspects of the overall job, visual displays and incidents involved. These place greater and different information-processing demands on operators from those used in traditional vigilance research. Cognitive processes such as selective and divided attention, the use of mental models, scan patterns, abstraction and interpretation are crucial in detecting and responding to events. Thus, internal and external attentional processes are involved in detection. Given that the original purpose of the taxonomy was to create a basis for effective comparison of studies based on tasks requiring different information-processing demands, the taxonomy is limited in its applicability to the operator’s role. 5.

Air traffic controllers

The jobs of operators and controllers share a few broad similarities, the most obvious of which is the interaction with the environment through technological interfaces. Studies into the performance of controllers represent an area where research has embraced the complex nature of the job to a far greater extent than traditional vigilance research. A number of studies have focused on the complex nature of the controller’s job and have identified factors that contribute to complexity, such as traffic density, and traffic and sector complexity (Mogford et al. 1995, Laudeman et al. 1998). However, these studies


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seldom focus on vigilance per se. Rather, they approach performance from different perspectives, such as analyses of complexity and SA. This section focuses on the purpose of the controller’s job, sources of data, nature of targets or signals, interrelatedness of data, cognitive processes involved and range of tasks performed. Once again, reference is made to the taxonomy and SA where applicable. The importance of the interrelationships between different data is emphasised rather than display characteristics, as the former are highly significant in the controller’s job. The overall purpose of the controller’s job is to ensure the safe and efficient management of complex air traffic (Wickens et al. 1997). To avoid collisions with other aircraft or obstacles, controllers are responsible for maintaining the lateral, vertical and longitudinal separation of aircraft. This involves creating a 3-D mental picture based on 2D data, similarly to operators. To maintain separation, controllers predict future flight paths based on their perception and comprehension of data, such as speed, aircraft capability, weather conditions and so on. Thus, all three levels of SA are involved. Planning air paths that keep flying time to a minimum and reduce fuel costs enhances efficiency. Air traffic control systems consist of various artefacts, with radar providing a key source of data. Depending on the technological sophistication of the system, other components include, for example, pre-planned flight databases, information regarding weather conditions, paper flight progress strips, communications from other pilots, other controllers and parties and CCTV displays of data from other control teams (Hirston et al. 2002). As with operators, controllers deal with numerous sources of heterogeneous data that draw on both visual and auditory modalities. Once again, the taxonomy’s inclusion of multiple sources of data seems inadequate in the face of the varied nature of the data and their interrelatedness. One controller is usually responsible for numerous aircraft at the same time. Dense traffic increases the difficulty of maintaining separation of aircraft (Berndtsson and Normark 1999). In vigilance terminology, therefore, the numerous aircraft on a radar display are all ‘targets’ or ‘signals’. This contrasts with traditional vigilance research, where there is a single or only a few targets and the rest of the display consists of noise or distracters (Wolfe et al. 2002). The information-processing demands are likely to differ in these situations. The taxonomy could be expanded to specify multiple targets in addition to multiple sources of data. The interrelationships between aircraft are highly significant to controllers. Thus, the mere existence of an aircraft constitutes only part of a ‘target’ as the proximity of aircraft to each other and other features (e.g. storms, geographical features or buildings) is crucial. Proximity in 3-D space is analysed based on data presented in 2-D space, adding a further dimension to the task. Controllers use a range of cognitive processes in monitoring the interrelationships between aircraft and other relevant factors. Key to this is the formation of a ‘picture’ of the air traffic situation, based on external (radar screen and flight progress strips) and internal (mental model) sources (Nunes and Mogford 2003). The picture is a comprehensive mental representation that is used to perceive and understand the air traffic scenario and project future aircraft trajectories. In creating the picture, data are analysed, integrated and interpreted. They are used to create mental abstractions and project trajectories into the future, forming predictions and plans regarding aircraft positions (Bentley et al. 1992). Various cognitive strategies are used to keep the picture up to date and ensure that aircraft are adequately separated, such as predicting trajectories, comparing times,


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distance and altitudes (Nunes and Mogford 2003). These cognitive processes are incorporated in levels 2 and 3 SA and differ from those used in traditional vigilance research, which relies largely upon perceptual detection or level 1 SA. However, for the controller, cognitive processes and SA are integral to the achievement of job goals and therefore need to be accounted for in the taxonomy. This is consistent with the increased recognition that the perceptual stage of information processing is intimately related to the subsequent stages of decision and action (Flach 1995). The continuous movement of aircraft and the fact that they cannot stop during flight create the time-critical nature of the job (Berndtsson and Normark 1999). Controllers are required to perceive and comprehend data and make predictions at a glance (Bentley et al. 1992). They work under time pressure and with data and situations that constantly change. Thus, their SA has to be updated constantly. Their actions need to be proactive rather than reactive. This type of continuous change differs from the continuous event rate in the taxonomy, which does not include the information-processing demands of continuously updated and time-critical SA. In order to reduce the mental workload of controllers and increase safety, numerous conventions, procedures and tools are used (Berndtsson and Normark 1999). For example, rules govern the distances allowed between aircraft and conventions regarding communication between controllers and pilots. While these aspects are static and therefore not considered part of SA, which is dynamic, they form part of long-term memory and are used in the creation of mental models (Endsley 2001). Certain air traffic control systems assist in implementing procedures, such as issuing warnings when aircraft are too close to each other. However, the degree of automation varies. Some systems automatically generate advisories to en-route and terminal area controllers regarding aspects such as runway allocation, sequences for arrival and departure, fuel-efficient and conflict-free paths, aircraft crossing times, headings and speeds, and take weather conditions into account (Erzberger et al. 1993). Despite automation, human controllers have not been replaced by machines and still perform crucial functions based on SA. In addition to coordinating aircraft within their own sector, controllers liaise with other sectors. Air space is divided into 3-D blocks or sectors, with teams of controllers responsible for each sector. Terminal control centres manage airspace at and around airports, including ground traffic, take-offs and landings. These centres consist of various teams, such as apron tower controllers responsible for aircraft on the ground, other teams managing runways, take-offs and landings and others coordinating aircraft approaching the airport. Finally, area centres control aircraft that are en route at higher altitudes. The aircraft’s location determines which controller controls its path, with different controllers taking over at different points in the flight. When aircraft move to the next sector, controllers hand them over to the relevant controllers and pilots are informed of this. Thus, movement within and between sectors requires coordination with other controllers, the pilot and systems. To do this effectively, controllers require information about surrounding sectors and a database system so that they can plan their work, taking into account aircraft that will be entering or leaving their sectors. Examples of the type of information used are navigational waypoints and estimated arrival times at these points (Berndtsson and Normark 1999). Thus, the integration of multiple sources and types of information is used to formulate plans that ensure effective coordination within and between sectors. In addition to planning and maintaining traffic movements, controllers provide instructions, advisories and information to pilots, deal with requests and irregular events. Thus, they perform a range of tasks at the same time, using a variety of cognitive processes

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and SA. As with operators, the responsibilities related to monitoring activities are integral to the job and cannot be ignored. Using the existing taxonomy, the controller’s job could be described as orthogonal, sensory and cognitive, visual and auditory, with continuous event rates and numerous sources of data. However, these categories do not adequately capture the essence of the goals and tasks that create different information-processing demands on controllers. These are summarised as the extrapolation of 3-D data from 2-D data, heterogeneous and interrelated data, multiple, continuously changing and interrelated targets, time-critical events, multi-tasking and reliance upon SA. The incorporation of these aspects into the taxonomy is discussed in the next section. 6. Towards an expanded taxonomy Based on the above discussion of the operator and controller’s jobs, it is evident that the taxonomy applies to certain aspects of these jobs but does not capture all aspects of the tasks that impact on information processing. At a superficial level, both jobs may be described as orthogonal, sensory and cognitive, visual and auditory, with continuous event rates and multiple sources of data. However, these descriptors fail to capture the complexity of the task and the corresponding cognitive processes. Therefore, three key recommendations are made. First, it is suggested that additional dimensions be added to the taxonomy with the aim of increasing its applicability. Second, the overall job rather than merely a narrowly defined monitoring task should be considered. Third, levels of SA required for successful performance should be included. These are elaborated upon below. Recommended expansion of the taxonomy is presented in Table 1. The categories of signal discrimination type, event rate and sensory modality merely require minor expansion. Thus, the degree of signal uncertainty varies with orthogonal tasks and the sensory modality may be both (not necessarily either) visual and auditory. Continuous event rates need to include multiple objects changing at different speeds in different directions and the time critical nature of the data and goals. Source complexity needs to go beyond the number of sources of data because the data themselves are complex in nature. Complexity should include heterogeneous data, perceptual salience, 2-D representations of 3-D space, background clutter, object occlusion, stand alone vs. embedded data and the extent to which meaning can be obtained from individual datum or interrelated data. These aspects generally form continua. They capture the perceptual complexity of displays and other artefacts that are involved mainly in level 1 SA but that provide a springboard for levels 2 and 3 SA. The alternative to expanding the taxonomy is to cease using it, but this has the disadvantage of decreasing the ability to compare research findings across different operational contexts. The second recommendation is that the overall job be considered rather than merely a narrowly defined monitoring task. This is due to the interrelated nature of tasks, goals and data. Traditional vigilance research tends to focus on monitoring and detection to the exclusion of related tasks. However, operators and controllers perform monitoring in conjunction with other, but often related, duties. The influence between monitoring and other tasks is bi-directional, with each influencing performance in the other area. Although related duties cannot easily be accommodated in the taxonomy, they should at least be specified and their relationship to monitoring considered. The third recommendation is that levels of SA and associated cognitive processes be specified when describing jobs involving vigilance. While the vigilance taxonomy acknowledges the multiple sources of information encountered in operational contexts,

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Table 1.

F.M. Donald Expanded vigilance taxonomy.

Dimension

Category

Signal discrimination type

Redundant

Orthogonal (with continuum of certainty)

Sensory

Cognitive

Signal/target characteristics

Homogeneous

Heterogeneous

Event rate

Low

Continuous (Multiple objects 6 Speeds 6 Directions) Time critical

Sensory modality

Visual and/or

Auditory

Source complexity

Single Homogeneous High perceptual salience 2-D

Multiple Heterogeneous Low perceptual salience 3-D abstraction Cluttered background Occluded objects Embedded Interrelated data

Medium

Empty background Complete objects Stand alone Meaning in individual datum Note: Additions to taxonomy are shown in bold.

it does not go far enough in explaining how job incumbents perceive, understand and use data. As Endsley (2000, p. 7) states: Simply describing the many sources of SA information does little to convey the intricate complexities of how people pick and choose information, weave it together and interpret it in an ongoing and ever-changing fashion as both situations change and operator goal states change.

Consequently cognitive processes such as data selection, integration, abstraction, prediction and the use of mental models are highly relevant to job performance. Analysis of these processes allows for the inclusion of endogenous and exogenous attentional guidance and a deeper understanding of the job. Similarly to overall job responsibilities, SA is not included in the vigilance taxonomy but forms part of the larger vigilance context. In summary, the expanded taxonomy only captures certain aspects of the operator and controller’s role and should therefore be used in conjunction with consideration of the overall job and SA. Other processes and concepts such as working memory capacity and mental workload could also provide insight into these roles. 7. Conclusion The traditional approach of studying vigilance by isolating and manipulating simple variables is not possible in complex monitoring due to the very nature of complex data, where multiple facets are interrelated. Because detection involves both perceptual and cognitive processes based on interrelated aspects of the display, the composite impact of


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the overall display is more important than the effects of discrete characteristics or stimuli themselves. Future research aimed at specifying different levels of complexity and examining their effects on vigilance performance would assist in bridging the gap between traditional research and operational environments. The original vigilance taxonomy was useful in classifying traditional vigilance research that was conducted in laboratories. Indeed, this was the purpose for which it was designed. Its aim was to categorise tasks in order to distinguish different information-processing requirements rather than to specify the information-processing requirements themselves (Matthews et al. 1993). The dimensions and categories in the taxonomy were appropriate to the relatively simple stimuli and tasks generally used in traditional vigilance research, where the emphasis is largely on the perceptual level of SA. The jobs of operators and controllers are significantly more complex than the tasks performed in traditional vigilance research. Data themselves are not only numerous, but complex, and their interrelationships are highly significant. There is far greater emphasis on all three levels of SA and reliance upon the first level of perception is inadequate for successful job performance. The cognitive processes involved extend beyond those that were presumably considered in the original taxonomy and related experiments. SA is derived from numerous sources, both internal and external to the job incumbent. Although the vigilance taxonomy acknowledges multiple sources of data, it does not include the heterogeneous nature of the data. With its focus on classifying tasks, the taxonomy is restricted to external sources of data presented in displays and places insufficient emphasis on the numerous internal processes that guide attention and perception. The taxonomy appears inadequate in the face of the interrelated externally and internally directed attentional processes that are involved in jobs where people interact with environments through technological interfaces. The original taxonomy cannot be applied directly to the jobs of operators and controllers. However, the expanded taxonomy that includes additional relevant aspects could be useful. In order to capture the information-processing demands of the jobs, the expanded taxonomy needs to be used in conjunction with analysis of overall job goals, related tasks and SA. The latter has the advantage of providing a platform for specifying the cognitive processes involved in selecting and using data to achieve job goals. It is an integral part of monitoring and detection and embraces both exogenous and endogenous attentional processes.

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