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Environmental Support for Older and Younger Pilots' Comprehension of Air Traffic Control Information

¹ Institute of Aviation and the Beckman Institute
⁴ Department of Educational Psychology, University of Illinois at Urbana-Champaign.
² Department Psychology, Brandeis University, Waltham, MA.
³ Department of Psychology, University of New Hampshire, Durham.
⁵ Independent consultant, Manchester, NH.

Address correspondence to Daniel Morrow, University of Illinois Institute of Aviation, Aviation Human Factors Division, Willard Airport, #1 Airport Road, Savoy, IL 61874. E-mail: dgm{at}uiuc.edu

	Abstract

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We investigated whether expertise mitigates age differences on pilot communication tasks when experts rely on environmental support. Pilots and nonpilots listened to air traffic control messages describing a route through an airspace, during which they referred to a chart of the airspace. The routes were high (waypoint routes anchored to navigational reference points on the chart) or low (vector routes that were not) in contextual support. Participants read back messages and answered questions about aircraft position (which required integration of message and chart information) or altitude (which did not). Pilots more accurately answered questions. The expertise advantage for position, but not altitude, questions was greater for waypoint routes, showing differential use of environmental support by experts. Age did not moderate these effects.

THE aging population in the United States is confronted by challenges related to adapting to technological changes at home and in the workplace (Morrow & Leirer, 1997; Stern & Carstensen, 2000). Age-related performance issues are particularly relevant to aviation because, like the general population, the population of pilots (Morrow & Leirer, 1997) and air traffic controllers (Becker & Milne, 1998) is aging, prompting a need to identify potential age-related costs and benefits related to complex task performance. Moreover, the principle of universal design (Vanderheiden, 1997) suggests that improving displays, procedures, and other aspects of the aviation environment for older pilots will yield general benefits for the workforce.

We focus on two interrelated factors that may help determine the conditions under which older pilots remain proficient: expertise (knowledge and experience) related to piloting tasks and environmental support provided by these tasks. First, experts excel on domain-relevant tasks for a variety of reasons. Experts possess highly organized knowledge structures (Glaser & Chi, 1988) that enable rapid retrieval from long-term memory of information needed to accomplish the task, reducing working memory constraints on performance (Ericsson & Kintsch, 1995). Such knowledge-based mechanisms may offset age-related declines in working memory that would otherwise constrain performance of complex tasks. However, evidence that expertise mitigates age declines is equivocal (see Hambrick & Engle, 2002; Meinz, 2000). Conflicting evidence for mitigation may reflect variation across studies in task characteristics, such as complexity or domain relevance (Morrow & Leirer, 1997). Domain-relevant tasks are organized around domain goals and constraints (Vicente & Wang, 1998).

A second factor that may influence older pilots' proficiency on complex tasks is the environmental support provided by domain-relevant tasks, which may support experts' use of knowledge to accomplish task goals (Kirlik, 1995). Environmental support may especially benefit older experts and mitigate age declines in cognitive abilities because older experts are adept at using external aspects of the task environment to reduce demands on cognitive resources (e.g., working memory). Although the concept of environmental support is multifaceted (Morrow, 2003), we focused on the extent to which the task externalizes mental processes that would otherwise be required by the task, which addresses age-related problems associated with self-initiating mental processes (Craik & Jennings, 1992). For example, relying on external parts of the cockpit (e.g., displays and charts) can reduce the pilot's need for memory retrieval and other cognitive processes (Hutchins, 1991).

We examined whether a navigational chart (a typical part of pilots' cockpit environment) supports pilots' comprehension of air traffic control (ATC) messages. To understand potential benefits of the chart, we briefly describe comprehension processes in ATC communication. Pilots routinely receive radio messages to change their aircraft's course (among other instructions). Understanding these messages involves word recognition and parsing of syntactic structure, which enables identification of the semantic content of the message. Perhaps what is most important is that the message information must be interpreted in terms of and integrated with information provided by flight instruments and other components of the flight context (both inside and outside the cockpit) in order to create a situation model (or mental model) of the current and projected flight conditions, so that the pilot understands not only what to do but how it will influence the flight situation (see Kintsch, 1998, for a general model of comprehension processes). This representation supports situation awareness, the ability to monitor the current and projected aircraft route and flight conditions (Adams, Tenney, & Pew, 1995). Pilots also read back (repeat) ATC messages, allowing the controller to verify their comprehension of the messages. Understanding ATC messages and updating the situation model should impose heavy demands on working memory (Morrow & Rodvold, 1998) and spatial abilities such as visualization (Adams et al., 1995), which may challenge older pilots. For example, age differences on measures of verbal working memory account for age declines in the accuracy of reading back ATC messages (Morrow et al., 2003; Morrow, Menard, Stine-Morrow, Teller, & Bryant, 2001).

Participants in the present study listened to ATC messages describing routes that were either high in contextual support (waypoint routes anchored to navigational reference points on the chart) or low (vector routes that contained headings that were not anchored to the aids) in support. Figure 1 shows that the waypoint routes followed Victor airways (standard routes to and from navigation reference points indicated on the chart), whereas the vector routes deviated from these same airways (e.g., to avoid weather or traffic).

View larger version (76K): [in this window] [in a new window]   Figure 1. Example of waypoint and vector routes, and position and altitude questions

Comprehension of the vector routes is less supported by the chart, which does not directly indicate the route. In this case, pilots receive less environmental support for updating their model (e.g., they must infer the exact aircraft position from chart and message information). This in turn may increase the amount of cognitive resources necessary for updating, which may disadvantage older pilots. Earlier studies found that expertise does not reduce age differences in comprehension of vector routes (Morrow et al., 2003). Thus, the navigation chart should serve as a domain-relevant environmental support for pilots' comprehension of ATC information, primarily in the waypoint condition. On the basis of previous studies, we also expected pilots to more accurately read back ATC messages. However, support from the chart in the waypoint condition is unlikely to improve readback accuracy because this task does not require integration of message and chart information.

We also explored sources of age and expertise differences in performance on the question and readback tasks. As in our earlier studies (Morrow et al., 2001; Morrow, Menard, et al., 2003), we used regression analyses to investigate the extent to which age and expertise effects were explained by individual differences in working memory, speed of mental processing, and spatial ability (see the Methods section for a description of these measures). The analyses also provided an opportunity for us to investigate whether age was moderated by expertise, when age and expertise are measured as continuous variables (see the Methods section for a description of expertise measures). Moderation would be indicated by significant Age x Expertise interaction terms after we controlled for the main effects of age and expertise. We also examined whether expertise mediated age declines because the older pilots had higher levels of experience (more flying hours) than the younger pilots did, which may buffer against age-related declines in cognitive abilities. Mediation would be indicated by finding that age accounts for more variance in performance when expertise is controlled in the regression analysis, suggesting that the older pilots would have been even more impaired if they could not rely on relatively higher levels of experience (Meinz, 2000; Morrow et al., 2001).

	METHODS

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Participants
Participants were 92 pilots medically certified to fly and with high levels of experience (minimum of 700 flying hours) in airline or corporate operations (age range, in years: young or Y = 22–40; middle-aged or M = 50–59; older or O = 60–76), as well as 96 nonpilots (age range, in years: Y = 25–39; M = 50–59; O = 60–73). Pilots were less educated than nonpilots but rated themselves as healthier (see Table 1). The mean age of the young nonpilots was lower than that of the young pilots, F(1, 62) = 9.6, p <.01, but pilots and nonpilots in the middle-aged and older groups did not differ in mean age, F(1, 62) < 1.0, producing a significant Age x Expertise interaction, F(2, 182) = 4.8, p <.01. (The pattern of results from analyses reported in this article was unchanged when we eliminated the youngest nonpilots in order to equate the age range for the nonpilot and pilot groups.)

There was a typical age-related increase for vocabulary and age declines for the verbal working memory, processing speed, and spatial ability measures. Pilots and nonpilots did not differ in vocabulary, span, or block design scores, but nonpilots exhibited higher scores on the comparison tasks. What was most important was that the Age x Expertise interactions were not significant for the fluid ability measures, showing that the pilots experienced typical age-related declines in these abilities (see Table 1).

Participants also completed several measures related to piloting expertise. All pilots had commercial licenses and were instrument rated (i.e., certified to fly under instrument as well as visual flight conditions). Age was associated with more total flying hours (Y = 6,149 mean hr, M = 17,067, O = 19,193), F(2, 89) = 35.8, ² =.45, Y < M = O, but fewer recent hours (Y = 627 mean hr, M = 617, O = 194), F(2, 89) = 26.0, ² =.37, Y = M > O. We assessed domain knowledge by using a questionnaire about aviation navigation and ATC communication concepts with 20 multiple-choice items (adapted from an FAA instrument rating exam; test–retest reliability, r =.79; Morrow et al., 2001). Of course, pilots outscored nonpilots on this measure (see Table 1). Age had a small but reliable influence (² =.05) and did not interact with expertise. Thus, although older pilots experienced declines in general cognitive abilities typical of their cohort, on average they had more flying experience than their younger counterparts, and they experienced only small declines on a measure of aviation-related declarative knowledge.

Materials
Navigation chart
As in the research by Morrow and colleagues (2001), we used a low-altitude en route chart for the northeastern United States airspace with names of navigation reference points changed to make the specific content unfamiliar to pilots. The chart indicated the location of electronic navigational reference points that define standard routes used by commercial aircraft to fly into (approach) or out of (departure) the terminal airspace of surrounding airports. These included radio beacons (VOR, or very-high-frequency omnirange), radials (which radiate off VORs like spokes from a wheel), and Victor airways (part of the low-altitude VOR system that defines standard aircraft routes).

ATC messages
Participants listened to ATC messages that described four routes through this airspace. There were two waypoint and two vector routes, with presentation order counterbalanced across participants. Each route was accompanied by a flight plan (typed on a 3 in. x 5 in., or 7.5 cm x 12.5 cm, card) indicating a series of VORs, intersections, and connecting Victor airways that identified the intended route (see Figure 1). The route was described by six ATC messages, each corresponding to a leg of the route. Each message began with an aircraft position report (identifying the location and direction of the aircraft when the ATC message is received; this information is not typically part of ATC messages but was necessary for our study) followed by three instructions to change the course of the aircraft, presented in the order specified by the ATC Handbook (FAA Order 7110.65): heading, altitude, and speed. As Figure 1 shows, waypoint routes were defined by navigational aids on the chart (on an airway). Instructions were presented in the form of crossing restrictions for position, altitude, and speed instructions (the aircraft was instructed to cross a position in the airspace that was defined relative to a navigational aid such as a VOR or intersection, at a particular altitude and speed). Vector routes, in contrast, were defined by headings that were not anchored to the aids (i.e., off airways). In other words, the vector routes deviated from the original flight plans (to avoid traffic congestion or bad weather). Airline pilots are likely to receive both kinds of route instructions when flying into or out of terminal airspace. All messages were recorded by a retired terminal controller using a speech rate typical of actual ATC operations.

We measured message comprehension by using questions about the aircraft's route. Half of the questions probed the position of the aircraft on the route by asking which of three positions the aircraft would pass closest to if it continued on the assigned course (position questions). Some position questions involved computing a projected position from the current aircraft position, which required time/speed calculations that should also be facilitated by environmental support (the chart) in the waypoint condition. The other half of the questions probed the aircraft's assigned altitude by asking which of three other aircraft on the same flight path but at different altitudes would pose a conflict. In other words, if both the participant's aircraft and each of these three aircraft continued on their present course, the participant's aircraft would potentially collide with one of the three (altitude questions; Figure 1 presents sample position and altitude questions for waypoint and vector routes). Thus, position but not altitude questions required integration of message and chart information.

Procedure
Participants completed the domain knowledge test, followed by training for nonpilots on the aviation tasks (see Morrow et al., 2001, for more detail on training procedure), the aviation tasks, and the domain-general cognitive tasks. For the aviation tasks, participants were first familiarized with the navigation chart. For each route, they reviewed the flight plan for 30 s and then listened to the messages describing the route, with the chart always in view. Participants were not allowed to take notes while listening to the messages. After listening to each message, they read back the instructions and answered a question about aircraft position or altitude (they were instructed to assume that the pilot had responded appropriately to the preceding ATC instructions).

After completing the aviation tasks reported in this article, the participants completed a study that examined the impact of note-taking on readback accuracy (the latter findings are reported as a preliminary study in Morrow, Ridolfo, et al., 2003).

	RESULTS

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Question Accuracy
We analyzed the mean percentage of correctly answered questions by means of an Expertise (pilot vs nonpilot) x Age (Y vs M vs O) x Route (waypoint vs vector) x Question (position vs altitude) analysis of variance, with the latter two variables as repeated measures. Figure 2 shows that expertise benefits were greater for waypoint than for vector routes for the position questions, suggesting that pilots differentially benefited from the environmental support provided by the chart in the waypoint condition, Expertise x Route x Question F(1, 178) = 4.8, p <.05, MSE =.096. For position questions, pilots were more accurate in the waypoint versus vector routes, t(89) = 2.1, p <.05, whereas the opposite held for nonpilots, t(94) = 2.5, p <.05, Expertise x Route F(1, 178) = 10.9, p <.01. Route type did not affect accuracy of altitude questions (t < 1.0 for both pilots and nonpilots).

View larger version (15K): [in this window] [in a new window]   Figure 2. Position and altitude question accuracy, by expertise and route type

Readback Accuracy
Pilots read back the messages more accurately than nonpilots did (80% vs 54% correct instructions repeated), F(1, 180) = 151.6, p <.01, MSE =.043, and younger participants were more accurate (Y = 75%, M = 69%, O = 59%), F(2,180) = 18.3, p <.01. Pilots did not differentially benefit from the chart in the waypoint condition (pilots: waypoint = 78%, vector = 83%; nonpilots: waypoint = 50%, vector = 58%), Expertise x Route F(1, 180) = 2.7, p =.10, MSE =.011. Rather, both groups read back vector routes more accurately than waypoint routes (71% vs 64%), F(1, 180) = 39.9, p <.01. This may reflect the fact that the crossing restriction instructions in the waypoint routes were more conceptually complex than the instructions in the vector routes because participants were required to integrate heading or speed with distance and time (i.e., the aircraft needed to be at a specific heading, altitude, or speed at a certain distance from the navigation aid).

Expertise did not mitigate age declines in readback accuracy for waypoint routes, Expertise x Age x Route F(2, 180) = 1.6, p >.10. The Expertise x Age interaction was also nonsignificant, Expertise x Age F(2, 180) = 1.2, p >.10.

Individual Differences in Aviation Task Performance
We conducted hierarchical regressions to investigate whether performance on the aviation tasks was predicted by individual differences in cognitive ability and expertise, and whether these effects helped explain age differences in performance. Because performance on the question and readback tasks was correlated (r =.64, p <.001), we conducted the regressions on a composite of the two tasks in order to increase reliability of the findings. Table 2 presents correlations among age, cognitive variables, expertise variables, and the composite aviation task performance variable. We conducted a set of four hierarchical regression analyses. Model 1 examined how much variability in performance was explained by age alone. Model 2 entered the cognitive measures (working memory, processing speed, and spatial ability entered as a block) before age in order to examine how much variability was accounted for by cognitive ability and whether age-related effects were partly explained by individual differences in cognition. Model 3 entered the expertise measures (domain knowledge, and total and recent flying hours) before age. We assigned nonpilots a score of zero for the flight hour measures, and we log-transformed these variables to adjust for the skewed distributions. Controlling for expertise may increase the amount of variability accounted for by age because older pilots had more total flight hours than younger pilots did, which would provide evidence that expertise buffered against age declines (Meinz, 2000). Model 4 examined the impact of expertise and age on performance, with differences in cognitive ability controlled.

In summary, the regression analyses showed that aviation performance was predicted by both cognitive ability (working memory and spatial ability) and expertise measures. Age differences in performance on these tasks were explained by individual differences in cognitive ability. In addition, more total flying hours for older compared with younger pilots helped buffer against age-related declines on these tasks.

	DISCUSSION

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Pilots were more accurate than nonpilots for both the question and readback measures, demonstrating the domain relevance of these laboratory tasks (also see Morrow et al., 2001; Morrow, Ridolfo, et al., 2003). What was more important was that the expertise advantage for the accuracy of position questions was greater in the waypoint than in the vector route condition. Only the waypoint routes were perceptually indicated on the chart (by navigation reference points or aids). Therefore, the chart in this condition provided environmental support for updating a situation model of the route, including the aircraft's current position and projected route. Consistent with contextual approaches to expertise (Kirlik, 1995), pilots derived more benefit than nonpilots did from the chart when they were answering position questions, reflecting their training and experience related to using external aids such as charts to support cognitive activities when flying. Differential expertise benefits did not occur in the waypoint condition for altitude questions, or for readback accuracy in either route condition. This presumably reflects the fact that neither task required pilots to integrate information from the chart and the message, so the support provided by the chart was less relevant. It is less clear why nonpilots were more accurate in answering position questions for vector rather than waypoint routes. This may reflect an attempt to take advantage of relatively unfamiliar navigational aids on the chart, which could have reduced performance in the waypoint condition. Position questions for the waypoint routes also required projecting a more precise location than in the vector condition, perhaps putting nonpilots at a disadvantage.

Environmental support from the chart did not differentially benefit older pilots for position questions in the waypoint condition, as shown by both the analysis of variance and the regression analysis. This may reflect several age-related cognitive limitations on performance that this form of environmental support did not address. First, because older pilots remembered less information than younger pilots did from the waypoint messages, they had less information to answer the questions. Note-taking, which has been found to eliminate age differences among pilots for message memory (Morrow, Ridolfo et al., 2003), may address this limitation. Second, older pilots may have had trouble identifying relevant chart information such as navigational aids (and thus integrating message and chart information) while listening to the messages because of age-related declines in selective visual attention (McDowd & Shaw, 2000). Although expertise can mitigate age declines for domain-relevant visual search (Hoyer & Ingolfsdottir, 2003), the working memory demands of the complex task in the present study may have offset potential expertise benefits, even in the waypoint condition. Morrow, Ridolfo, and colleagues (2003) found that note-taking reduced age differences among pilots for readback accuracy but not for question accuracy (using similar questions to the present study). In both studies, the environmental support (perceptual support from the chart in the present study or note-taking in Morrow, Ridolfo, et al., 2003) did not reduce the cognitive resources needed to integrate message and chart information and update their mental model of the flight. Thus, an important issue raised by both studies is what types of environmental support will reduce the cognitive cost of information integration in complex tasks. For example, in the present study, perceptual enhancement of the relevant waypoints on the chart by means of highlighting (a capability of electronic charts) may reduce the attention and working memory resources required to integrate the chart and message information, especially if highlighting reduces clutter on the navigational display. Such forms of environmental support may improve older pilots' situation awareness, which is necessary for communication, decision making, and other components of piloting (Endsley, 1995).

Findings from the analysis of individual differences in performance also have implications for improving pilot performance. Age differences in pilot communication were predicted by individual differences in cognitive ability, especially working memory, which is consistent with evidence that working memory plays an increasingly important role in explaining age differences in more complex tasks involving verbal memory and comprehension (Van der Linden et al., 1999). Working memory was critical for understanding and remembering the verbally presented ATC information and for updating a model of the flight from this message. Spatial ability also predicted performance, perhaps because it is important for updating, which requires integrating message and chart information. Thus, the present study suggests the importance of basic cognitive abilities for pilot communication (Table 3, Model 2), even as it shows the importance of expertise above and beyond these abilities (Table 3, Model 4; also see Morrow et al., 2001; Morrow, Menard, et al., 2003). The findings also reinforce the conclusion from earlier studies (e.g., Braune & Wickens, 1985) that functional age is more important than chronological age in predicting pilot performance. That is, it is essential to assess the impact of age on the cognitive processes involved in piloting in order to understand how age influences this complex task. Finally, the present study also underscores the importance of environmental supports such as navigational aids and note-taking (Morrow, Ridolfo, et al., 2003) that help externalize task components and reduce the role of these basic cognitive constraints on older pilots' performance, as well as the need for aids that reduce the extent of mental integration required by piloting tasks.

	Acknowledgments

 
Support for this research was provided by Grant R01 AG13936 from the National Institute on Aging. The data for this study were collected at the University of New Hampshire. We thank Milovan S. Brenlove for recording the ATC messages. A preliminary version of this article was presented at the Cognitive Aging Conference, April 2002, Atlanta.

	Footnotes

 
Decision Editor: George Rebok, PhD

Received for publication November 21, 2003. Accepted for publication August 13, 2004.

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