Development of a Novel Measure of Situation Awareness: The Case for Eye Movement Analysis

Abstract

Situation awareness (SA) is a measure of an individual's knowledge and understanding of the current and expected future states of a situation. While there are numerous options for SA measurement, none are currently suitable in dynamic, uncontrolled environments. The current research explored the relationship between direct measures of SA and eye tracking measures as a first step in the development of an unobtrusive measure to be used in environments not suited for existing SA measurement methods. Results showed that the more individuals fixated on an important aircraft in an air traffic control task, the higher their SA for that aircraft. The study also provided evidence that the way operators allocate attention (i.e., distributed widely or narrowly) affects their SA, as well as their task performance. The results indicate that eye tracking may be a viable option for measuring SA in environments not conducive to current direct SA measurement techniques.

Measuring the Situation Awareness of Tower Controllers by Using Eye Movement Analysis

“…Under the same experimental difficulty, the dispersed group exhibited a higher SA than the clustered group. This result is consistent with the findings of Moore and Gugerty [31] that participants whose attention distribution is more extensive could consider the information of each functional area in the simulated tower control interface and have a better SA. Regardless of the group, the participants' SA decreased as the experimental difficulty increased because increasing the experimental difficulty greatly increased the demand for attention resource (Demand), while the situational understanding Review 13 (1) (2020) 133 -140 (Understand) and the supply of attention resource (Supply) remained limited.…”

“…The independent sample t-test for the different groups' number of errors in the simple, medium, and difficult experiments showed a nearly significant difference between the dispersed and clustered groups (the respective p values of the simple, medium, and difficult experiments were 0.06, 0.05, and 0.01). Specifically, the participants whose fixation feature is dispersed distribution (NNI>1) had a better operation performance, which is inconsistent with the conclusion of Moore and Gugerty [31] that "Errors also increased as NNI increased towards 1, which indicates that the spatial distribution of fixations became less aggregated and more randomly dispersed". The possible reasons are as follows: (1) the interfaces of these two experiments were not the same, that is, Kristin's was based on the radar control interface, whereas ours was based on the simulated tower control interface.…”

“…Existing scholars introduced the nearest neighbor index (NNI) into recognition of subjects' fixation pattern and found that the NNI is highly sensitive to workload changes [26,27]. NNI was soon adopted by many scholars [28][29][30][31][32][33], because it considers all the fixation points of a given situation and does not need to prioritize the AOI, thus greatly increasing the availability of eye movement in the real environment. For instance, Nocera et al [29] used the NNI to calculate the random distribution of fixation points at the flight deck to characterize the pilot's visual glance strategy and explore its relationship with the workload.…”

“…Fidopiastis et al [30] characterized the unmanned operator's eye movement model with the NNI and studied the effects of automation and workload on eye movement patterns. Moore and Gugerty [31] introduced the NNI to analyze the aggregation and dispersion degree of the controller's fixation points on the radar interface and studied its relationship with SA and performance, arriving at the conclusion that how "operators allocate attention (distributed widely or narrowly) affects their SA, as well as their task performance".…”

How much time do drivers need to obtain situation awareness? A laboratory-based study of automated driving

“…We used eye tracking to gain a deeper understanding of how participants build SA as a function of time. It is well known that eye movements are correlated with bottom-up and top-down attention (Borji and Itti, 2013;Henderson, 2003;Itti and Koch, 2001) and memory of visual objects (Irwin and Zelinsky, 2002;Moore and Gugerty, 2010). For example, using a SAGAT method, Moore and Gugerty (2010) found that the more participants fixated on an aircraft in an air traffic control task, the higher their SA (i.e., responses to state queries) for that aircraft.…”