ABSTRACT
The promise of measuring situation awareness (SA) unobtrusively is a goal for many basic and applied researchers. Measurement in general works best when its administration does not affect its outcome. Nonetheless, many researchers have begun to view inadvertent measurement effects as a necessary cost, assessing SA by means of potentially disruptive performance probes and/or queries (e.g., Situation Awareness Global Assessment Technique (SAGAT), Situation Present Assessment Method (SPAM), etc.). Advances in eye tracking technology, however, have begun to facilitate continuous, unobtrusive inferences about underlying mental states, including workload (e.g., Ahlstrom & Friedman-Berg, 2006) and SA (e.g., Ratwani, McCurry, & Trafton, 2010), among others. Although these techniques have been employed by many researchers, certain methodological and procedural issues remain pertaining to how eye gaze data are processed and applied to inform human factors research. The current chapter aims to provide guidance on these issues by reviewing literature relevant to SA measurement and by presenting original empirical work on eye tracking data collected within a dynamic, complex Unmanned Aerial Vehicle (UAV) management simulation. The chapter discusses the strengths and limitations of the different SA measurement techniques and demonstrates how eye-based measures of SA can complement the existing measures.
