Using the situation present assessment method to measure situation awareness in simulated submarine track management

2013 ◽  
Vol 2 (1) ◽  
pp. 33 ◽  
Author(s):  
Shayne Loft ◽  
Daniel B. Morrell ◽  
Samuel Huf
Author(s):  
Ting Zhang ◽  
Jing Yang ◽  
Nade Liang ◽  
Brandon J. Pitts ◽  
Kwaku O. Prakah-Asante ◽  
...  

Objective The goal of this review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). Background Assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SA global assessment technique (SAGAT), situation present assessment method (SPAM), and/or SA rating technique (SART). However, research suggests that physiological sensing methods may also be capable of inferring SA. Method Seven databases were searched. Eligibility criteria included human–subject experiments that used at least one direct SA assessment technique as well as at least one physiological measurement. Information extracted from each article were the physiological metric(s), direct SA measurement(s), correlation between these two metrics, and experimental task(s). Results Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA was mixed. Only three electroencephalography (EEG) studies were identified, and their results suggest that EEG was sensitive to changes in SA. Overall, medium correlations were observed among the studies that reported a correlation coefficient between physiological and direct SA measures. Conclusion Reviewed studies observed relationships between a wide range of physiological measurements and direct assessments of SA. However, further investigations are needed to methodically collect more evidence. Application This review provides researchers and practitioners a summary of observed methods to indirectly assess SA with sensors and highlights research gaps to be addressed in future work.


Author(s):  
Shayne Loft ◽  
Vanessa Bowden ◽  
Janelle Braithwaite ◽  
Daniel B. Morrell ◽  
Samuel Huf ◽  
...  

Author(s):  
Mitsuki Fujino ◽  
Jieun Lee ◽  
Toshiaki Hirano ◽  
Yuichi Saito ◽  
Makoto Itoh

Evaluation of air traffic controller’s situation awareness (SA) is becoming important for air traffic management with the growth of air traffic. This study compared two SA evaluation methods: Situation Awareness Global Assessment Technique (SAGAT) and Situation Present Assessment Method (SPAM) to understand how these techniques affect controllers’ predictability in different traffic density settings. Twenty students undertook simple air traffic control simulations by using both techniques. We investigated how these techniques affect their workload with Subjective Workload Assessment Technique (SWAT) and NASA-TLX. SWAT scores showed that high traffic density increased participants’ workload, and extra workload was posed right after answering SA queries. NASA-TLX scores were larger when SAGAT was used than when SPAM was used throughout the simulation. We found that the workload with SAGAT interferes with main tasks more than that of SPAM. The results of query scores suggested that SPAM is more predictive to the assessment of the controller’s SA.


2019 ◽  
Vol 119 ◽  
pp. 330-343 ◽  
Author(s):  
Pengcheng Li ◽  
Li Zhang ◽  
Licao Dai ◽  
Yanhua Zou ◽  
Xiaofang Li

Author(s):  
Monica Tatasciore ◽  
Vanessa K. Bowden ◽  
Troy A.W. Visser ◽  
Stephanie Chen ◽  
Shayne Loft

Automation that supports our workplaces is intended to relieve the requirement for humans to control tasks, as a way to reduce operator workload and maximize system capacity. Researchers have long recognized the potential costs associated with automation. These costs include the loss of an operator’s understanding of a task and an inability to anticipate future task events ( situation awareness; SA; Endsley, 1995) that can occur due to automation induced complacency (Parasuraman, Molloy, & Singh, 1993), and the subsequent lack of ability to regain manual control after automation (Kaber & Endsley, 2004). These costs to automation are more likely to occur when the degree of automation (DOA) increases. DOA has been defined based on whether automation is doing more or less ‘work’ ( levels of automation; Sheridan & Verplank, 1978), and at which of the four stages of human information processing the automation is directed; information acquisition, information analysis, decision selection, and action implementation ( stages of automation; Parasuraman, Sheridan, & Wickens, 2000). As the DOA increases, performance and workload tend to improve. However, SA and return-to-manual performance can decline. Recent research by Chen, Huf, Visser, and Loft (2017) reported that a low DOA had minimal benefits to performance and workload, and also impaired SA and non-automated task performance compared to a manual control condition in a simulated submarine track management task. However, the low DOA did not lead to any return-to-manual deficits when automation was unexpectedly removed. The current study compared the effects of low and high DOA on operator performance, workload, SA, non-automated task performance, and return-to-manual performance in submarine track management. Participants ( N= 122) monitored a tactical display that presented the location and heading of contacts in relation to the Ownship and landmarks, and a ‘waterfall’ display that presented sonar bearings of contacts and how those bearings change with time. Participants performed three tasks: classification, closest point of approach (CPA), and dive. The classification task involved classifying contacts depending on how long they had spent within display regions. The CPA task involved monitoring changes in contact heading to determine their closest point of approach to the Ownship. The dive task involved integrating contact location and heading information to determine when the submarine could safely dive. Automated assistance was provided for the classification and CPA tasks, but not for the dive task. The low DOA condition received information acquisition and analysis support (stages 1 and 2), whereas the high DOA received decision selection support (stage 3). In a mixed design, the between-subjects factor was condition (no automation, high DOA, low DOA) and the within-subjects factor was automation state (routine, automation removal). Participants completed three track management scenarios, and during the last scenario the automation was unexpectedly removed. Firstly, we predicted that a high DOA would have larger benefits to performance and workload compared to a low DOA, but that these benefits might be accompanied by costs to SA, non-automated task performance, and return-to-manual performance. Secondly, we predicted that a low DOA would show minimal benefits to performance and workload, significant costs to SA and non-automated task performance, and no effect on return-to-manual performance when compared to no automation, thus replicating the findings of Chen et al. (2017). The results from this study indicated that relative to the low DOA condition, participants provided with high DOA support had better performance and lower workload, without any further costs to SA, non-automated task performance, or return-to-manual performance. Furthermore, relative to no automation, participants provided with low DOA support only had minor benefits to performance (replicating Chen et al., 2017) and no benefits to workload, and significant costs to SA and non-automated task performance. In summary, the high DOA produced larger benefits to performance and workload than the low DOA, without increasing costs. In light of these results, the automated system that recommended decisions was effectively utilized by operators in the current context, and appeared to be superior to the automated system that supported information acquisition and analysis.


Author(s):  
Monica Tatasciore ◽  
Vanessa K. Bowden ◽  
Troy A. W. Visser ◽  
Steph I. C. Michailovs ◽  
Shayne Loft

Objective The objective of this study is to examine the effects of low and high degree of automation (DOA) on performance, subjective workload, situation awareness (SA), and return-to-manual control in simulated submarine track management. Background Theory and meta-analytic evidence suggest that as DOA increases, operator performance improves and workload decreases, but SA and return-to-manual control declines. Research also suggests that operators have particular difficulty regaining manual control if automation provides incorrect advice. Method Undergraduate student participants completed a submarine track management task that required them to track the position and behavior of contacts. Low DOA supported information acquisition and analysis, whereas high DOA recommended decisions. At a late stage in the task, automation was either unexpectedly removed or provided incorrect advice. Results Relative to no automation, low DOA moderately benefited performance but impaired SA and non-automated task performance. Relative to no automation and low DOA, high DOA benefited performance and lowered workload. High DOA did impair non-automated task performance compared with no automation, but this was equivalent to low DOA. Participants were able to return-to-manual control when they knew low or high DOA was disengaged, or when high DOA provided incorrect advice. Conclusion High DOA improved performance and lowered workload, at no additional cost to SA or return-to-manual performance when compared with low DOA. Application Designers should consider the likely level of uncertainty in the environment and the consequences of return-to-manual deficits before implementing low or high DOA.


Author(s):  
Monica Tatasciore ◽  
Vanessa K. Bowden ◽  
Troy A. W. Visser ◽  
Shayne Loft

Objective To examine the effects of action recommendation and action implementation automation on performance, workload, situation awareness (SA), detection of automation failure, and return-to-manual performance in a submarine track management task. Background Theory and meta-analytic evidence suggest that with increasing degrees of automation (DOA), operator performance improves and workload decreases, but SA and return-to-manual performance declines. Method Participants monitored the location and heading of contacts in order to classify them, mark their closest point of approach (CPA), and dive when necessary. Participants were assigned either no automation, action recommendation automation, or action implementation automation. An automation failure occurred late in the task, whereby the automation provided incorrect classification advice or implemented incorrect classification actions. Results Compared to no automation, action recommendation automation benefited automated task performance and lowered workload, but cost nonautomated task performance. Action implementation automation resulted in perfect automated task performance (by default) and lowered workload, with no costs to nonautomated task performance, SA, or return-to-manual performance compared to no automation. However, participants provided action implementation automation were less likely to detect the automation failure compared to those provided action recommendations, and made less accurate classifications immediately after the automation failure, compared to those provided no automation. Conclusion Action implementation automation produced the anticipated benefits but also caused poorer automation failure detection. Application While action implementation automation may be effective for some task contexts, system designers should be aware that operators may be less likely to detect automation failures and that performance may suffer until such failures are detected.


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