As robots become increasingly capable and autonomous, the role of a human operator may be to supervise multiple robots and intervene to handle problems and provide strategic guidance. In such cases, the extent to which HRI tools support the human supervisor's situational awareness (SA) and ability to intervene in an appropriate and timely fashion will constrain the scale of operations (e.g., the number of robots; the complexity of tasks) that can reasonably be supervised by a single person. One approach to understanding how humans might acquire, maintain, and use situational awareness in multi-robot supervision tasks is to look at video games that require similar activities. We describe our initial efforts at analyzing and modeling data from Real-Time Strategy (RTS) games with the goal of answering basic questions about the nature of situational awareness and supervisory control of multiple semi-autonomous agents.

Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.