The Impact of Auditory Task Complexity on Primary ...

Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014

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The Impact of Auditory Task Complexity on Primary Task Performance in Military Land Vehicle Crew Michael G Lenné1, Benjamin L Hoggan1, Justin Fidock2, Geoff Stuart1 & Eugene Aidman2,3 1 Monash University Accident Research Centre, Melbourne, Victoria, Australia 2 Defence Science and Technology Organisation, Edinburgh, South Australia, Australia 3 School of Psychology, University of Sydney Military land vehicles are becoming more technologically advanced, offering much greater capabilities for command and control on the move. The capabilities afforded by these modern vehicles are likely to place greater cognitive demands on the vehicle operators, and an increased requirement for vehicle crews to communicate effectively. This study explored the influence of a secondary task load on both individual and crew performance. Eight driver/co-driver crews operated a driving simulator over two days, during which the complexity of an auditory secondary task, which incorporated a crew-based communication component, was manipulated. The impact of this manipulation on both individual and crew performance was assessed. The results suggest that participants prioritized and protected performance on their primary task when the complexity of the secondary task was increased, at the cost of declines in their secondary task performance. Implications for the use of modern land vehicles in defense fleets are discussed, along with our research program aimed at further exploring the impact of varying cognitive load on crew performance.

INTRODUCTION

Copyright 2014 Human Factors and Ergonomics Society. DOI 10.1177/1541931214581459

The Australian Department of Defence is planning to acquire thousands of new field vehicles via Project Overlander (LAND 121), as well as mobile computing technologies, which in combination are designed to enhance mobility, survivability and connectivity. These acquisitions are driven by aging vehicle fleets that are nearing the end of their life of type, as well as by the emergence of new threats associated with modern warfare that demand a more protected, adaptive and connected land force. An image one of the prototype protected mobility vehicles is shown below (Figure 1).

Figure 1: A prototype of the protected mobility vehicle-light (Source http://www.army.gov.au/Our-future/Projects/ProjectLAND-121). In socio-technical terms, the transition might be likened to that which occurred when society shifted from the industrial age to the information age: a shift from employing machines to deliver kinetic effects to also employing machines to

support information and knowledge based work. For operators of these field vehicles, which largely perform logistics support functions, this entails a shift from industrial age trucks with limited communications capability, to having a portion of vehicles in the new fleet that will have advanced information and communication technologies. These new capabilities will need to be operated from within the vehicles in support of the planning and execution of missions. Such a shift means that a single operator will in many cases be insufficient to effectively deploy the vehicle. Instead, many of the new field vehicles will require crews of two or more. A shift of this magnitude will likely entail a significant increase in the demands placed on operators of these field vehicles, since they will be required to complete tasks that are more cognitively demanding, as well as doing so in a work space that involves additional operators and which exposes them to vibration and motion. To assist the Australian Defence Force in identifying how best to exploit the opportunities presented by these new vehicles and their associated mobile computing technologies, a program of research is underway investigating how the performance of operators and crew can be enhanced in these new mobile work environments. An important initial step in this research program has been to build an understanding of the impact of varying task demands on the operators and crew of simulated future field vehicles. A significant amount of research has been published in the human factors literature that documents the impacts of different workloads and the interaction with visual and auditory secondary tasks on driving in civilian settings (e.g., Lee, Lee, & Boyle, 2009; Zhang, Kaber, Rogers, Liang, & Gangakhedkar, 2013). Very little, however, has been published on such issues in military land vehicle settings, nor on the likely demands and functioning of a commander sitting in a co-driver role, and the interaction between driver and codriver. The present study investigated how performance of military land vehicle crews was influenced by manipulating level of complexity on a secondary task.

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Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014

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for the co-driver’s primary task (discussed later in this section) appeared on the periphery of the screen.

METHOD Participants Sixteen male military personnel, aged 19 - 31yrs (M = 23.1 yrs, SD = 3.1) took part in the study. The participants had between 1.4 - 5.8 years of military experience (M = 3.3 yrs, SD = 1.4), and were well known to one another. The study was approved by the Defence Science and Technology Organisation’s (DSTO) Ethics Committee. Materials Simulation was implemented with PC-based Virtual BattleSpace 2 (VBS2; Bohemia Interactive). Each PC was connected to a single 46” flat-panel screen mounted approximately 1.0 m from the participant’s eye. The screen operated at a resolution of 1360 × 768 pixels during the experiment. Each simulator incorporated a ‘driver’ position on the right-hand side with a Logitech steering wheel and velocity/brake pedals, with response buttons located on the front and back of the steering wheel, and a ‘co-driver’ position on the left-hand side with a Logitech joystick as an input device. As shown in Figure 2, both positions included over-ear audio headsets to convey the auditory stimulus and sounds from the simulated environment, each with an attached microphone to allow voice communication between the driver and co-driver. A total of eight simulators, separated by high partitions to minimize distraction, were networked to allow data collection from up to 16 participants simultaneously.

Figure 2: Illustration of the simulator set up with vehicle crews Primary and Secondary Tasks Two-person ‘vehicle crews’ (driver and co-driver) were tasked to drive along a designated route with relatively uniform road characteristics, within a simulated 50 × 50 km VBS2 map of nondescript extra-urban terrain (i.e. primarily flat desert, with occasional features such as trees and distant mountains). The vehicle speedometer and tachometer were shown in the bottom-right corner of the screen. Letter symbols

Primary task for the driver. The driver’s task was to minimize variability in performance by maintaining a stable speed of 40 km/h and as steady a central position in the lane as possible. Measures of deviation in speed and lateral placement are typical basic measures of vehicle control used in many civilian driving simulation studies. Primary task for the co-driver. The role of co-driver or commander in the real world would likely involve the use of a touch-screen battle management system (BMS), as used in previous research (Goode, Lenné, & Salmon, 2012; Salmon et al., 2011). As a BMS was not accessible for this study, the primary task for co-drivers was to continuously perform a visual 1-back memory task, seen as broadly analogous to another real-world co-driver task: to scan for, recognize, and respond to irregular events external to the vehicle. The 1-back is a specific case of the ‘N-back’, a continuous, sequential letter memory task where participants compare a target letter to the letter presented N steps prior, responding when the two are identical (Miller, Price, Okun, Montijo, & Bowers, 2009). Although most commonly employed in verbal form during driving studies (Mehler, Reimer, & Dusek, 2011), visual N-back tasks have also been employed in simulator studies to assess visual scanning and working memory (Cassavaugh & Kramer, 2009; Lenneman & Backs, 2009). N-back tasks allow the parametric increase of task difficulty via working memory load (i.e. increasing N), without changing the task procedure. After piloting, N=1 was deemed appropriate for this study. At regular intervals, a letter 2 × 3 cm in size on a translucent 4 × 4 cm square was presented in one of eight areas on the perimeter of the screen (i.e., top, bottom, left and right edges, plus all four corners). The location of presentation was randomized so that participants had to continuously scan the perimeter of the display. As a 1-back task, participants were instructed to press the designated button on the joystick when the presented letter was a repeat of the preceding letter. The inter-stimulus interval between initiations of letter presentations was 4.5 - 6.5 sec with 3.0 sec presentation duration and 20% of letter presentations were a repeat. In the multiple target secondary task condition (discussed in the following section), the letter presentations were every 2.5 - 4.5 sec with a 33% repeat rate. Secondary auditory task. The driver and co-driver were concurrently required to perform a secondary auditory task, involving one of three pre-recorded tones presented at regular intervals. To enhance face validity, the auditory task was designed to resemble operationally relevant tasks of monitoring auditory inputs such as radio messages or BMS alerts. As such, two tones were specified as representing ‘laser’ and ‘electronic warfare’ (EW) warnings, while the third was specified as a ‘null’ tone. Participants responded to warning tones by pressing the corresponding button (laser: left button; EW: right button) on the steering wheel or base of the joystick. The ‘null tone’ required a withdrawal of response.

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Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014

The secondary task had two levels of complexity. The low complexity task involved a 1-target condition, where only one tone, the ‘laser’ warning tone, was presented. The higher complexity task involved a 3-target condition, where all three tones, including both warning tones, were presented. Only one headset (i.e., driver or co-driver) received each tone presentation, with the order of presentation randomized. All auditory tones were 2.0 sec in duration and presented randomly, with an inter-stimulus interval of 10 sec. Crew communication task. An additional intra-crew communication task required each crew member, in the event of a warning tone, to provide a verbal message copying that warning to the other crew member (i.e., laser: verbal message of “laser”; EW: verbal message of “EW”). The second crew member in turn acknowledged the verbal message with the corresponding button press on their steering wheel or joystick (i.e., laser: left button; EW: right button). This enabled the calculation of crew performance measures. As only one crew member received each auditory signal presentation, either the driver or co-driver initiated the communication sequence. This also meant that an order effect could be analyzed. It should be noted that participants were not told to prioritize the primary, secondary or crew tasks specifically. They were instructed to maintain performance as best as possible on all tasks for the duration of the session. Procedure Upon arrival at DSTO, participants completed an informed consent form and a short demographics survey. This was followed by a brief training session to familiarize participants with the experimental tasks, where they received verbal instructions on the tasks required for the driver and codriver roles. Participants were well practiced on the simulator, having completed a related study in the two days prior. The experiment was conducted over two consecutive days between the hours of 9:30 a.m. and 4:00 p.m. On the first day participants were randomly allocated into crews, which they remained in for both days. Participants were randomly assigned to the driver or co-driver role for the first day, swapping roles for the second day. The low complexity auditory task was employed on the first day, with the higher level of complexity used on the second day. Each day comprised a 90 min session in the simulator, a 120 min lunch break, followed by a further 90 min session after lunch. In the middle of each session (i.e., after 45 min), participants received a 5 min resting period with eyes closed. Data Analysis The primary dependent measures were variability in both speed and lateral placement for the driver, and performance on the visual task (i.e., reaction time (RT), detection accuracy) for the co-driver. Correct responses were defined as those provided within 5.0 sec following critical signal presentation (i.e., repeat letter, auditory warning tone). RTs are reported for correct responses only.

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Crew communication performance was also determined by RT and accuracy. Crew RT was calculated from the time at which the first auditory target was presented through to the point at which the second crew member acknowledged. Crew response accuracy was defined by whether the second crew member pressed the response button corresponding with the original warning tone. Performance measures were recorded in 5 min blocks across the duration of the testing sessions. To avoid issues around repeated measurements from participants not being independent, performance outcomes were analyzed using mixed effects models (West, 2009). Time blocks across each day were considered repeated measures and participants as random variables. Secondary task complexity (i.e., low or high), time of day (i.e., morning or afternoon), and their interaction were considered as fixed variables impacting on primary task outcomes. For the secondary auditory task and crew communication task, role (i.e., driver or co-driver) was incorporated as an additional fixed variable. RESULTS Primary Task Performance Driver performance. As shown in Table 1, a significant main effect of time of day (F(1,115) = 4.06, p < 0.05) was found for standard deviation (SD) of speed, with greater deviation during morning sessions. The main effect of secondary task complexity and interaction effect were not significant. With regards to SD of lateral lane position (SDLP), no significant main effects for secondary task complexity or time of day were found, nor was there an interaction effect. Table 1. Driving performance by time of day and level of secondary task complexity (±standard error (SE)) Measure SD of speed (km/h) SD of lateral position (m)

1-target AM 5.14 (±0.69) 0.431 (±0.027)

Secondary Task Complexity 1-target PM 3-target AM 4.66 6.08 (±0.69) (±0.74) 0.421 0.437 (±0.027) (±0.029)

3-target PM 5.31 (±0.74) 0.440 (±0.029)

Table 2. Co-driver performance on visual symbol recognition task by time of day and level of secondary task complexity (±SE) Measure Reaction time (sec) Reaction accuracy

1-target AM 1.40 (±0.10) 0.627 (±0.082)

Secondary Task Complexity 1-target PM 3-target AM 1.38 1.53 (±0.10) (±0.11) 0.642 0.694 (±0.082) (±0.088)

3-target PM 1.46 (±0.11) 0.611 (±0.088)

Co-driver performance. Co-driver data are shown in Table 2. Neither time of day nor secondary task complexity had a significant impact on reaction time to repeat letter detections. For response accuracy there was a significant interaction between secondary task complexity and time of

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Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014

day (F(1,125) = 5.01, p < 0.05). Under the 3-target auditory task condition, participants’ performance appeared to decline noticeably in the afternoon session compared to the morning. Secondary Task Performance Auditory secondary task performance is summarized in Table 3. Analyses showed that the main effects of task complexity (F(1,290) = 515.39, p < 0.001), role (F(1,290) = 135.65, p < 0.001), and time of day (F(1,410) = 4.83, p < 0.05) each had a significant impact on RT to warning tones. RTs were faster under the 1-target auditory task condition, amongst drivers, and during afternoon sessions. A significant three-way interaction between variables was also identified (F(1,410) = 12.40, p < 0.001). The results indicate that under the 1-target auditory task condition, drivers showed consistently faster responses than co-drivers in both morning and afternoon sessions. Under the 3-target condition, however, this difference was less apparent, with RTs in the afternoon session being comparable for the two roles. Similar trends were found for the rate of correct responses to auditory warning tones. A greater proportion of correct responses were reported under the 1-target auditory task condition (F(1,264) = 63.44, p < 0.001) and amongst drivers (F(1,264) = 43.24, p < 0.001). However, unlike RT, accuracy suffered significantly during afternoon sessions (F(1,515) = 5.28, p = 0.022). No interaction effects were found for this performance measure. Table 3. Performance on auditory secondary task by role, time of day and level of secondary task complexity (±SE) Measure 1-target AM Driver Reaction time (sec) Reaction accuracy Co-driver Reaction time (sec) Reaction accuracy

Secondary Task Complexity 1-target PM 3-target AM

3-target PM

1.22 (±0.17) 0.990 (±0.028)

1.17 (±0.17) 0.999 (±0.028)

1.65 (±0.17) 0.958 (±0.029)

1.69 (±0.17) 0.924 (±0.029)

1.49 (±0.17) 0.956 (±0.028)

1.49 (±0.17) 0.949 (±0.028)

1.81 (±0.17) 0.882 (±0.029)

1.72 (±0.17) 0.867 (±0.029)

Crew Task Performance Performance on the crew communication task is summarized in Table 4. For this task, ‘role’ denotes the crew position which initiated the communication sequence. For crew task RT there were significant main effects of task complexity (F(1,263) = 1237.94, p < 0.001), role (F(1,263) = 33.15, p < 0.001), and time of day (F(1,364) = 4.25, p < 0.05). As for individual performance, RTs were faster under the 1target auditory task condition, when initiated by drivers, and during afternoon sessions. No interactions were significant. Regarding crew response accuracy, secondary task complexity had a significant main effect (F(1,376) = 225.43, p < 0.0001), as did time of day (F(1,355) = 4.04, p < 0.05). As with individual performance, crews gave less accurate responses under the 3-target auditory task condition and

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during afternoon sessions. However, the role that initiated the communication sequence did not significantly impact response accuracy. A significant three-way interaction between the variables was identified (F(1,355) = 4.89, p < 0.05). The results indicated that for most combinations of role and task complexity, there was some degree of decline in the accuracy of responses during the afternoon session compared to the morning. However, under the 1-target condition driverinitiated sequences were more accurate in the afternoon than the morning. Table 4. Performance on crew communication task by role initiating communication, time of day and level of secondary task complexity (±SE) Measure 1-target AM Driver initiated Reaction time (sec) Reaction accuracy Co-driver initiated Reaction time (sec) Reaction accuracy

Secondary Task Complexity 1-target PM 3-target AM

3-target PM

2.37 (±0.22) 0.959 (±0.030)

2.28 (±0.22) 0.965 (±0.030)

3.09 (±0.22) 0.865 (±0.031)

3.07 (±0.22) 0.819 (±0.031)

2.53 (±0.22) 0.947 (±0.030)

2.49 (±0.22) 0.929 (±0.030)

3.10 (±0.22) 0.853 (±0.031)

3.13 (±0.22) 0.850 (±0.031)

DISCUSSION With the acquisition of new vehicle fleets and in-vehicle technologies, military land vehicle crews are taking on more command and control functions whilst on the move. It is important to establish what impact the complexity of invehicle tasks and communications might have on basic elements of driver and co-driver performance. This study represents a first step in this endeavor. Elements of driving performance were influenced by time of day. Variability in performance, as measured through SD of speed, was greater in the morning sessions, which is consistent with research showing that driving performance is less variable in the mid-late afternoon compared to the morning (Lenné, Triggs & Redman, 1997). The absence of any effect of secondary task complexity on SDLP was unexpected. While SDLP has typically been found to increase as a result of driver engagement in visual or physical-manual activities (Caird, Johnston, Willness, Asbridge, & Steel, 2014), other studies focusing specifically on cognitive load have shown that SDLP decreases with additional cognitive load introduced via a secondary task (Atchely & Chan, 2011; Cooper, Medeiros-Ward, & Strayer, 2013). While degrading other aspects of driving performance, the increase in cognitive demand through a secondary task has been found to afford a performance gain for SDLP potentially because participants are protecting lateral control against the risk of distraction (He, McCarley, & Kramer, 2014). The increase in secondary task complexity also did not influence the co-driver’s primary task performance on the Nback task. While the participants were not instructed to give priority to either their primary or secondary task, it would

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Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014

appear that primary task performance was prioritized and protected, at the cost of declines in secondary task performance. While it could be suggested that primary tasks for this participant group were relatively easy and that performance on these tasks could easily be maintained, this is unlikely however for the N-back task, which places moderate demands on working memory. Results for the secondary auditory task showed that RT for both the driver and co-driver was faster in the simpler 1target audio condition. Further, as illustrated in Table 3, RT for drivers was faster than for co-drivers, as indicated by the significant main effect. These results were mirrored by those found for the crew communication task. The level of complexity on the secondary audio task did influence crew performance, with RT being faster for the simpler 1-target condition. Interestingly RT was also faster when the sequence was initiated by the driver, compared to co-driver initiated sequences. This finding is likely linked to two elements that distinguish the two primary tasks. Firstly, the driving scenario was very simplistic and certainly less cognitively demanding than the N-back task used by the co-drivers. The demands imposed by the N-back task could have contributed to the longer RTs compared to the drivers, especially over an extended period of time. However, the tasks differ not only in the level of cognitive demand, but also in their structure and in the approach to performance measurement required. Driving is a continuous task, and hence the measures of driving performance were continuous in nature. In contrast, the codriver metrics were discrete measures of events occurring approximately every five seconds. The differences in the nature of these tasks and their measurement frameworks need to be explored further. More generally, the conceptualization and measurement of team workload remains an area where significant developments are required (Funke, Knott, Salas, Pavlas, & Strang, 2012). Our study used a simplistic driving scenario, a surrogate commander task for the co-driver, and a secondary task that represents a moderate level of demand at best. Addressing each of these limitations is an obvious next step in this research. Further research with more balanced designs should also examine primary and secondary task trade-offs in more detail, and examine how crews perform and adapt to recurring variations in workload under conditions of significant stress, such as those associated with extended sleep loss. In closing, a program of research is underway investigating how the performance of operators and crew can be enhanced in new military land vehicles. An important initial step was to build an understanding of the impact of varying task demand on the operators and crew. The results of our study suggest that participants prioritized and protected their performance on driving and co-driving primary tasks when the complexity of a secondary auditory and crew communication task was increased, to the detriment of their performance on the secondary task. This is perhaps the most important finding to explore in future research as it has the potential to influence how military land vehicle crews operate as a team in more technologically advanced land vehicles that afford significantly enhanced command and control capability.

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ACKNOWLEDGEMENTS This study was funded under a Research Agreement between the Defence Science and Technology Organisation and the Monash University Accident Research Centre. We acknowledge Glen Pearce, John Stewien and Nebojsa Tomasevic for providing technical assistance with scenario design, data parsing and data processing. REFERENCES Atchley, P., & Chan, M. (2011). Potential benefits and costs of concurrent task engagement to maintain vigilance: a driving simulator investigation. Human Factors, 53, 3-12. Cassavaugh, N.D., & Kramer, A.F. (2009). Transfer of computerbased training to simulated driving in older adults. Applied Ergonomics, 40, 943–952 Caird, J.K., Johnston, K.A., Willness, C. R., Asbridge, M., & Steel, P. (2014). A meta-analysis of the effects of texting on driving. , Accident Analysis & Prevention, 71, 311-318. Cooper, J. M., Medeiros-Ward, N., & Strayer, D. L. (2013). The impact of eye movements and cognitive workload on lateral position variability in driving. Human Factors, 55, 1001-1014. Goode, N., Lenné, M. G., & Salmon, P. M. (2012). The impact of onroad motion on BMS touch screen device operation. Ergonomics, 55, 986-996. Funke, G. J., Knott, B. A., Salas, E., Pavlas, D., & Strang, A. J. (2012). Conceptualization and measurement of team workload: a critical need. Human Factors, 54, 36-51. He, J., McCarley, J. S., & Kramer, A. F. (2014). Lane keeping under cognitive load: performance changes and mechanisms. Human Factors, 56, 414-426. Lee, Y., Lee, J. D., & Boyle, L. N. (2009). The interaction of cognitive load and attention-directing cues in driving. Human Factors, 51, 271-280. Lenné, M. G., Triggs, T. J., & Redman, J. R. (1997). Time of day variations in driving performance. Accident Analysis & Prevention, 29, 431-437. Lenneman, J.K. & Backs, R.W. (2009). Cardiac autonomic control during simulated driving with a concurrent verbal working memory task. Human Factors, 51, 404-418. Mehler, B., Reimer, B., & Dusek, J. (2011). MIT AgeLab delayed digit recall task (n-back). (MIT AgeLab White Paper No. 20113B). Cambridge: Massachusetts Institute of Technology. Miller, K. M., Price, C. C., Okun, M. S., Montijo, H., & Bowers, D. (2009). Is the n-back task a valid neuropsychological measure for assessing working memory? Archives of Clinical Neuropsychology, 24, 711–717. Salmon, P. M., Lenné, M. G., Triggs, T. J., Cornelissen, M., Williamson, A., Tomasevic, N., & Demczuk, V. (2011). The effects of motion on in-vehicle touch screen system operation: a battle management system case study. Transportation Research Part F: Traffic Psychology & Behaviour, 14, 494-503. West, B.T. (2009). Analyzing longitudinal data with the linear mixed models procedure in SPSS. Evaluation and the Health Professions, 32, 207-228. Zhang, Y., Kaber, D. B., Rogers, M., Liang, Y., & Gangakhedkar, S. (2013). The effects of visual and cognitive distractions on operational and tactical driving behaviors. Human Factors, 56, 592-604.

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