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Robotics » "Advances in Human-Robot Interaction", book edited by Vladimir A. Kulyukin, ISBN 978-953-307-020-9, Published: December 1, 2009 under CC BY-NC-SA 3.0 license. © The Author(s).

# Effectiveness of Concurrent Performance of Military and Robotics Tasks and Effects of Cueing and Individual Differences in a Simulated Reconnaissance Environment

By Jessie Y.C. Chen
DOI: 10.5772/6837

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## Overview

Figure 1. TCU (left) and Gunnery station (gunner’s out-the-window view) (right)

Figure 2. Gunner’s enemy target detection performance and effects of spatial ability (SpA).

Figure 3. Robotics (teleoperation) task performance and effects of spatial ability (SpA).

Figure 5. Gunnery task performance (hostile targets).

Figure 6. Gunnery task performance (hostile targets)- effects of AiTR reliability (100 = AiTR with perfect reliability; 60F = FAP; 60M = MP) and SpA.

Figure 7. Interaction between PAC and AiTR unreliability.

Figure 8. Effects of PAC on gunnery task performance (hostile targets) in MP conditions.

Figure 9. Gunnery task performance (neutral targets) - effects of Robotics and PAC.

Figure 13. SpA and AiTR display modality preference.

# Effectiveness of Concurrent Performance of Military and Robotics Tasks and Effects of Cueing and Individual Differences in a Simulated Reconnaissance Environment

Jessie Y.C. Chen1

## 1. Introduction

### 1.1. Background

The goal of this research is to examine if and how aided target recognition (AiTR) cueing capabilities facilitates multitasking (including operating a robot) by gunners in a military tank crewstation environment. Specifically, we examine if gunners are able to effectively perform their primary task - maintaining local security - while performing a pair of secondary tasks: (1) managing a robot and (2) communications with fellow crew members. According to Mitchell (2005), who used the Improved Performance Research Integration Tool (IMPRINT) to examine the workload of the crew of a future tank system, the gunner is the most viable option for performing the robotics control tasks compared to the other two positions (i.e. vehicle commander and driver). She found that the gunner had the fewest instances of overload and, therefore, may be able to assume control of the robot. However, she also discovered that there were instances in the model when the gunner dropped his/her primary tasks of detecting and engaging targets to perform robotics tasks, which could be catastrophic for the team and mission during a real operation. If the gunner is the individual who will most likely be assigned the responsibility of robotics control, then it is important to consider what design changes will be necessary to allow successful multitasking without a critical performance decrement in maintaining local security.

### 1.2. Tactile cueing

In the current study, we examined if and how tactile cueing, which delivered simulated AiTR capabilities (i.e. cues to the direction of a potential target), enhanced gunner’s performance in a military multitasking environment. In the first experiment, the simulated AiTR was perfectly reliable; in the second experiment, it was either false-alarm prone (FAP) or miss prone (MP). Sklar and Sarter (1999) found tactile cueing to be particularly useful for target detection and response time with a concurrent visual task, both in conjunction with visual cueing and alone. Terrence et al. (2005) compared spatial auditory and spatial tactile cues and found that participants perceived the tactile cues both faster and more accurately. In another study by Krausman et al. (2005), on the other hand, tactile cueing was not found to be more effective than auditory cueing in terms of response time, although it was more effective than visual cueing. Additionally, participants rated tactile cueing as the most helpful among the three types of alerts.

Spatial attention has been found to have cross-modal links across visual, auditory, and tactile inputs (Spence & Driver 1997). The level of effectiveness of one spatial information display relative to other display modalities may be dependent on the operational context of the experimental procedure (i.e. the demands of the tasks). Ho et al. (2005) found vibrotactile alerts were powerful directors of spatial attention in simulated driving scenarios, with faster responses even when reliability levels made the alerts spatially non-predictive. Clearly, there are potential benefits to offloading information to the relatively underutilized sensory pathways, though the exact nature of the performance gains is in need of further elucidation. With proper implementation, tactile alerts may improve performance when multitasking with man-machine interfaces (Van Erp & Van Veen 2004).

### 1.3. Imperfect automation and multitasking performance

In contrast to Meyer’s model and the aforementioned findings, Dixon et al. (2007) showed that FAP automation impaired “performance more on the automated task than did miss-prone automation, (e.g. the “cry wolf” effect) and hurt performance (both speed and accuracy) at least as much as MP automation on the concurrent task (p. 570-571).” FAP automation was found to affect both operator compliance and reliance, while MP automation affected only operator reliance. The authors suggested that the FAP automation had a negative impact on reliance because of the operator’s overall reduced trust in the automated system. Similarly, Wickens, Dixon, and Johnson (2005) demonstrated a greater cost associated with FAP automation (than with MP automation), which affected both the automated and concurrent tasks.

Furthermore, Wickens and Dixon (2005) demonstrated that when the reliability level is below approximately 70%, operators often ignore the alerts. In their meta-analytic study, Wickens and Dixon found that “a reliability of 0.70 was the ‘crossover point’ below which unreliable automation was worse than no automation at all.” Although Wickens and his colleagues have done extensive research in this area, their studies were conducted in a different environment (unmanned aerial vehicle control display monitoring), and they did not use tactile cueing. The current study was the first one to examine these issues in the context of combined roles of gunner and robotics operator. Since an AiTR cannot have a perfect reliability rate in foreseeable real world operations, the data from this study should provide useful information to the design community of future military systems, in which AiTR will play an integral role.

### 1.4. Individual differences in spatial ability and attentional control

In the current study, we also sought to investigate the effects of individual differences in spatial ability (SpA) and perceived attentional control (PAC) on the operators’ concurrent performance. SpA has been found to be a significant factor in virtual environment navigation (Stanney & Salvendy, 1995), learning to use a medical teleoperation device (Eyal & Tendick, 2001), target search task (Chen et al., 2008, Chen & Joyner, 2009), and robotics task performance (Cassenti et al., 2009, Lathan & Tracey, 2002, Menchaca-Brandan et al., 2007). For example, Lathan and Tracey (2002) demonstrated that people with higher SpA performed better in a teleoperation task through a maze. They finished their tasks faster and had fewer errors. In a recent study, Cassenti et al. (2009) demonstrated that robotics operators with higher SpA (measured by a mental rotation test) performed robot navigation tasks significantly better than those with lower SpA. Our previous studies (Chen et al., 2008, Chen & Joyner, 2009) also found SpA to be a good predictor of the operator’s robotics and gunnery task performance. In the domain of visual spatial displays, Stanney and Salvendy (1995) found that high SpA individuals outperformed those with low SpA on tasks that required visuo-spatial representations to be mentally constructed. While many SpA tests measures focus on visually presented stimuli, the interconnections of sensory modalities at the level of spatial perception may translate into differential effects of multisensory spatial displays across SpA levels (Spence et al., 2004).

In addition to SpA, we also examined the relationship between attentional control and multitasking performance. Several studies show that there are individual differences in multitasking performance, and some people are less prone to performance degradation during multitasking conditions (Rubinstein et al., 2001, Schumacher et al., 2001). There is some evidence that attention-switching flexibility can predict performance of such diverse tasks as flight training and bus driving (Kahneman et al., 1973). There is also evidence that people with better attention control can allocate their attention more flexibly and effectively (Bleckley et al., 2003, Derryberry & Reed, 2002), and this was partially confirmed by Chen and Joyner (2009). It is likely that operators with different levels of attention switching abilities may react differently to automated systems with FAs and misses. In other words, operators’ compliance and reliance behaviors may be altered based on their ability to effectively switch their attention among the systems. For example, the complacency effect may be more severe for poor attentional control individuals compared with those with better attentional control. The current study sought to examine if the compliance vs. reliance effects reported in the literature might be moderated by individual attentional control.

### 1.5. Current study

For the second experiment, based on the data from Wickens, Dixon, Goh et al. (2005), we expected that the operator’s gunnery (automated) task performance would degrade if the FA rate of the AiTR for the gunnery system was high because of reduced compliance with the automation. Conversely, if the cueing was MP, the operator’s robotics (concurrent) task performance would be affected more than the gunnery task because of reduced reliance on the automation. More mental and visual resources would be devoted to checking the raw data for the automated task, and therefore, the performance of the concurrent task would be degraded. On the other hand, there was evidence that FAP automation was more detrimental to both the automated and concurrent tasks than MP automation (Dixon et al., 2007). Therefore, it is likely that FAP automation would have a more negative impact on the overall performance than would MP automation. In other words, there have been conflicting results in the literature regarding the independence of the effects of FAP and MP automation on operator compliance and reliance. It is possible that individual differences may be responsible for some of the observed differences in the literature. Therefore, we investigated the effects of individual differences on FAP and MP conditions as a possible explanation for the discrepancies.

## 2. Experiment 1

### 2.1. Method

#### 3.1.2. Apparatus

The simulators and cueing displays were identical to those used in Experiment 1. The simulated AiTR was either FAP or MP, with a reliability level at 60%. The low reliability level was deliberately chosen to investigate if the compliance vs. reliance effects as well as the individual differences reported previously in the literature would be amplified in the high workload multitasking environment in the current study. The FAP condition consisted of ten hits (i.e. alerts when there were targets), eight FAs (i.e. alerts when there were no targets), no misses (i.e. no alerts when there were targets), and two correct rejections (CRs) (i.e. no alerts when there were no targets). The MP condition consisted of two hits, no FAs, eight misses, and ten CRs.

The communication task materials, spatial tests, and surveys (i.e., Attentional Control Survey, NASA-TLX, and Usability Survey) were identical to those used in Experiment 1. Participants were also asked to evaluate their trust in the AiTR system using a modified survey by Jian et al. (2000) (items 22-33).

#### 3.1.3. Experimental design

The overall design of the study is a 2 x 3 mixed design. The between-subject variable is AiTR type (FAP vs. MP). The within-subject variable is Robotics Task type (Monitor vs. Auto vs. Teleop) (see Procedure).

#### 3.1.4. Procedure

The preliminary session (i.e., surveys and spatial tests) and the training session were identical to Experiment 1 and lasted about 2.5 hrs. The experimental procedure was also identical to Experiment 1, except that it followed the training session on the same day and the participants were told that the AiTR cueing was unreliable. There were three types of robotics tasks: Monitor, Auto, and Teleop. The Monitor task required the operator to continuously monitor the video feed as the robot traveled autonomously and verbally report detection of targets. There were twenty targets (five hostile and fifteen neutral) along the route. The Auto and Teleop tasks were identical to those in Experiment 1. While the participants were performing their gunnery and robotics control tasks, they simultaneously performed the communication task by answering questions delivered to them via DECtalk®. There were 2-min breaks between experimental scenarios. Participants assessed their workload using the computerized NASA-TLX after each scenario. They also evaluated their perceived utility of and trust in the AiTR at the end of the experiment. The entire experimental session lasted about 1 hr.

The dependent measures include mission performance (i.e. number of targets detected in the remote environment using the robot and number of hostile/neutral targets detected in the immediate environment), communication task performance, and perceived workload.

### 3.2. Results

#### 3.2.1. Target detection performance

A mixed ANOVA was performed to examine the effects of the concurrent robotic control tasks on the gunnery task performance (percentage of hostile targets detected), with the AiTR condition (FAP vs. MP) being the between-subject factor and the Robotics Task condition (Monitor vs. Auto vs. Teleop) as the within-subject factor. The analysis revealed that Robotics condition significantly affected number of targets detected, F(2, 15) = 4.6, p <.05 (Figure 5). Post hoc (LSD) tests showed that target detection in the Monitor condition was significantly higher than in the Auto and Teleop conditions. Neither AiTR nor the Robotics x AiTR interaction was significant.

### Figure 5.

Participants with higher SpA had significantly higher gunnery task performance than did those with lower SpA, F(1, 16) = 6.3, p <.05. When comparable data from both experiments were examined in the same analysis (with only the TacVis condition from Experiment 1 and Robotics and Teleop conditions from Experiment 2), it was found that AiTR reliability contributed significantly to the hostile target detection performance of gunnery task, F(2,30) = 11.8, p =.000. Post-hoc (LSD) tests show that AiTR with perfect reliability (Experiment 1) was significantly higher than MP, and FAP was also significantly higher than MP, p’s <.05.

### Figure 6.

Gunnery task performance (hostile targets)- effects of AiTR reliability (100 = AiTR with perfect reliability; 60F = FAP; 60M = MP) and SpA.

Participants’ SpA was found to affect their gunnery task performance, and there was a significant SpA x AiTR reliability interaction (Figure 6). As Figure 6 shows, there was a large difference between low SpA and high SpA individuals in the FAP condition.

Participants were classified as high or low PAC based on their attentional control survey scores (median split). There was a significant AiTR x PAC interaction, F(1, 16) = 7.4, p <.05 (Figure 7, upper left). Those with lower PAC performed better with the FAP cueing, whereas those with higher PAC performed at a similar level regardless of the AiTR conditions.

### Figure 7.

Interaction between PAC and AiTR unreliability.

In order to further examine the effect of task load on reliance of AiTR, the data of the MP condition were analyzed separately. Due to the small sample size (N = 12), no significant differences were found between those with high vs. low PAC, F(1, 10) = 1.4, p >.05. However, the trend was evident that, while those with high PAC maintained a fairly stable level of reliance throughout the experimental conditions, those with low PAC became increasingly reliant on the AiTR (and missed more targets), as task load became heavier (i.e. Teleop > Auto > Monitor, based on Chen & Joyner, 2009) (Figure 8). For the low PAC participants, the difference between the Monitor and Teleop conditions was statistically significant, F(1, 6) = 7.1, p <.05.

Participants’ detection of neutral targets was also assessed. Since the AiTR only alerted the participants when hostile targets were present, the neutral target detection could be used to indicate how much visual attention was devoted to the gunnery station. A mixed ANOVA revealed a significant main effect for Robotics, F(2,15) = 4.4, p <.05. Post hoc tests (LSD) showed that neutral target detection in the Teleop condition was significantly lower than in the Auto condition. The main effect for AiTR failed to reach statistical significance, F(1, 22) = 3.3, p >.05. There was a significant AiTR x PAC interaction, F(1, 16) = 3.6, p <.05 (Figure 7, upper right panel). Those with lower PAC performed at about the same level, regardless of the AiTR type, while those with higher PAC had a better performance with the MP cueing

### Figure 8.

Effects of PAC on gunnery task performance (hostile targets) in MP conditions.

than with the FAP cueing. When comparable data from both experiments were examined in the same analysis (with only the TacVis condition from Experiment 1 and Robotics and Teleop conditions from Experiment 2), it was found that both the main effect of Robotics and the Robotics x PAC interaction were significant, F(1,30) = 8.8, p =.006 and F(1,30) = 4.5, p =.04 respectively (Figure 9). The difference between low PAC and high PAC individuals was larger in the Teleop condition than in the Auto condition.

### Figure 9.

Gunnery task performance (neutral targets) - effects of Robotics and PAC.

A mixed ANOVA revealed that there was a significant main effect for Robotics, F(2,15) = 25.4, p <.001 (Figure 10). The Monitor condition was significantly higher than both the Auto and the Teleop conditions, in terms of percentage of targets detected. The main effect for AiTR was not significant, p >.05. There was a significant Robotics x AiTR interaction, F(2,32) = 4.0, p <.05. The Monitor task performance stayed at the same level regardless of the AiTR types. The Auto task performance was slightly higher with the MP cueing (although the difference failed to reach statistical significance), while the Teleop task performance was significantly higher with the FAP cueing (p <.05). There was also a significant AiTR x PAC interaction, F(1,16) = 4.8, p <.05 (Figure 7, lower left panel). Those with lower PAC had a better performance with the FAP cueing, while those with higher PAC performed better with the MP cueing.

### Figure 10.

A mixed ANOVA revealed that there was a significant main effect for Robotics, F(2,44) = 3.3, p <.05. The Monitor condition was significantly higher than the Teleop conditions, F(1,22) = 5.5, p <.05. Neither the main effect for AiTR nor the Robotics x AiTR interaction was significant, p’s >.05 (Figure 7, lower right panel). When comparable data from both experiments were examined in the same analysis (with only the TacVis condition from Experiment 1 and Robotics and Teleop conditions from Experiment 2), it was found that the main effect of AiTR reliability was significant, F(2,29) = 5.3, p =.011 (Figure 11). Post-hoc (LSD) tests showed that communication task performance in Experiment 1 (perfect reliability) was significantly better than either FAP or MP (p’s <.05).

### Figure 11.

Participants’ self-assessment of workload (weighted ratings of the scales of the NASA-TLX) was significantly affected by Robotic condition, F(2,15) = 25.1, p <.001 (Figure 12). The perceived workload was significantly higher in the Teleop condition (M = 77.7) than in the Auto condition (M = 69.6) and the Monitor condition (M = 61.1). The difference between Auto and Monitor was also significant. The main effect for AiTR was not significant, p >.05. There was a significant Robotics x AiTR interaction, F(2,15) = 5.5, p <.05.

### Figure 12.

#### 3.2.3. AiTR display usability assessment

Following their interaction with the AiTR systems, 41% of participants responded that they relied predominantly or entirely on the tactile AiTR display, while 36% responded that they relied predominantly or entirely on the visual AiTR display. AiTR preference was also significantly correlated with SpA (composite spatial test scores), r =.51, p <.01. Those with

### Figure 13.

SpA and AiTR display modality preference.

higher SpA tended to prefer tactile cueing over visual cueing. Conversely, those with lower SpA favored visual cueing over tactile cueing. Figure 13 shows the data from both experiments examined in the same analysis, F(1,35) = 12.1, p =.001. There was also a significant negative correlation between the participants’ ages and their preference of tactile display, r = -.42, p =.003 (i.e., older participants tended to prefer visual cueing display while younger participants tended to prefer tactile display).

## 4. General discussion

Results of Experiment 2 also showed that there was a significant interaction between types of unreliable AiTR and participants’ PAC. For those with high PAC, our data are consistent with the notion that operator reliance on and compliance with automation are independent constructs and are separately affected by system misses and false alarms (Dixon & Wickens, 2006, Meyer, 2001, 2004, Wickens, Dixon, Goh et al., 2005). Based on Figure 7, it is evident that high PAC participants did not comply with alerts in the FAP condition. Since the FAP AiTR had a 0% miss rate, a full compliance should result in a detection rate over 84%, as reported in Experiment 1 (with perfectly reliable AiTR). As predicted, Figure 7 shows that in MP conditions, high PAC participants did not rely on the AiTR and detected more targets than were cued. However, an examination of the data for the low PAC participants revealed a completely opposite trend. Specifically, with the FAP condition, low PAC participants showed a strong compliance with the alerts, which resulted in a good performance in target detection (at a similar level as in Experiment 1). With the MP condition, however, low PAC participants evidently overly relied on the automation and therefore had a very poor performance. Indeed, Figure 8 shows that as task load became heavier, those with low PAC became increasingly reliant on the AiTR (and missed more targets), while those with high PAC maintained a fairly stable level of reliance throughout the experimental conditions. According to Biros et al. (2004), higher task loads tend to induce a higher level of reliance on automated systems. Data of Experiment 2 suggest that this heightened level of reliance is also moderated by PAC. More specifically, only those with low PAC tend to exhibit over-reliance on automation (i.e. complacency) under a heavy task load.

Participants’ workload assessment was found to be affected by the type of concurrent robotics task as well as whether their gunnery task was aided by AiTR. They experienced higher workload when the robot required teleoperation or when their gunnery task was unassisted by AiTR. These results are consistent with Mitchell’s (2005) analysis and with the findings of Chen and Joyner (2009) and Schipani (2003), which evaluated robotics operator workload in a field setting. Although many of the ground robots in the Army’s future robotics programs will be semi-autonomous, teleoperation will still be an important part of any missions involving robotics (e.g., when robots encounter obstacles or other problems). The higher workload associated with teleoperation needs to be taken into account when designing the user interfaces for the robots (see Chen et al., 2007, for a review of user interface designs for teleoperated robots).

The data of both Experiment 1 and 2 showed significant positive correlations of AiTR preference with SpA, indicating that as AiTR ratings tended toward considerable reliance on the tactile display, there was a concurrent shift with higher SpA. Perhaps those with higher SpA can more easily employ the spatial tactile signals in the dual task setting and therefore have a stronger preference for something that makes the gunner task easier to complete. Individuals with lower SpA, on the other hand, may have not utilized the spatial tactile cues to their full extent and therefore continued to prefer the visual AiTR display. According to Kozhevnikov et al. (2002), visualizers with lower SpA tend to rely on iconic imagery while those with higher SpA tend to prefer using spatial-schematic imagery while solving problems. Therefore, it is likely that in our study, those who preferred visual AiTR displays might be more iconic in their mental representations. However, this preference may have caused degraded target detection performance due to more visual attention being devoted to the visual AiTR display, not to the simulated environment. In contrast, those who were more spatial could take advantage of the directional information of the tactile display to help them with the visually demanding tasks, resulting in a more effective performance. Finally, our data showed that older participants tended to prefer visual cueing display while younger participants tended to prefer tactile display. It is not clear to which extent this shift is related to decline of SpA as people age (Berg et al., 1982).

## 5. Conclusions

The data of Experiment 2 suggest that there is a strong interaction between the type of AiTR unreliability and participants’ PAC for almost all the performance measures. Overall, it appears that for high PAC participants, FAP alerts were more detrimental than MP alerts. FAP alerts affected not only their automated task but also the concurrent task. However, for low PAC participants, MP automation was more harmful than FAP automation. Future research should incorporate performance-based measures of attentional shifting effectiveness (e.g., Synthetic Work Environment) in addition to surveys such as the attentional control survey. In the area of SpA, Experiment 2 replicated the finding of Experiment 1 that the operator’s preference of modality of the AiTR display is correlated with his or her SpA. Low SpA individuals prefer visual cueing over tactile cueing, although tactile display would be more effective in highly visual environments (so visual attention can be devoted to the tasks, not to the cues). These findings may have important implications for personnel selection, system designs, and training development. For example, to better enhance the task performance for low SpA individuals, the visual cueing display should be more integrated with the visual scene. Augmented reality (i.e., visual overlays) is a potential technique to embed directional information onto the video (Calhoun & Draper, 2006). Additionally, the capabilities and limits of the automated systems should be conveyed to the operator, when feasible, in order for the operator to develop appropriate trust and reliance (Lee & See, 2004).

## 6. Acknowledgements

This project was funded by the U.S. Army’s Robotics Collaboration ATO. The author thanks Mr. Michael Barnes of ARL - HRED, Dr. Peter Terrence of State Farm Insurance, and MAJ Carla Joyner of U.S. Military Academy for their contributions to this project.

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