Artificial intelligence has been developed to perform all manner of tasks but has not gained capabilities to support social cognition. We suggest that teams comprised of both humans and artificially intelligent agents cannot achieve optimal team performance unless all teammates have the capacity to employ social-cognitive mechanisms. These form the foundation for generating inferences about their counterparts and enable execution of informed, appropriate behaviors. Social intelligence and its utilization are known to be vital components of human-human teaming processes due to their importance in guiding the recognition, interpretation, and use of the signals that humans naturally use to shape their exchanges. Although modern sensors and algorithms could allow AI to observe most social cues, signals, and other indicators, the approximation of human-to-human social interaction -based upon aggregation and modeling of such cues is currently beyond the capacity of potential AI teammates. Partially, this is because humans are notoriously variable. We describe an approach for measuring social-cognitive features to produce the raw information needed to create human agent profiles that can be operated upon by artificial intelligences.

The intent of this evaluation is to describe the unique benefits that may be provided to human robot interaction (HRI) researchers by the capabilities of commercially available binocular head-mounted displays (HMDs) and associated handheld controllers. Three popular HMDs (Oculus Rift, HTC Vive, and Google Daydream) were compared across eight factors: cost, head tracking fidelity, visual resolution, user mobility, hand tracking fidelity, number of input modes, adaptability of input, and provided tracking space. Each of these elements was considered in the context of their relevance to the field of HRI, and potential importance for conducting research in immersive virtual reality (IVR). A Pugh chart was developed to succinctly compare the pros and cons of each headset alongside a description of IVR tasks for HRI military research as well as examples taken from work currently being conducted in our lab.

Research has shown that the perceived sex (female versus male features) of a presented threat can influence participants’ responses. This exploratory analysis examined data from an experiment which utilized a virtual reality signal detection task. Six categorically different character models (three males and three females) transported one of five potential objects (one signal: pistol; five noise: gardening tools) across a virtual environment. The focus of our analysis was to explore the influence of participant sex and character gender on participants' perceptual sensitivity ( d’) and response criterion ( C). Results suggest that character gender had significant effects on d’ and C such that male character models resulted in greater perceptual sensitivity and a more liberal response criterion. Our findings align with previous research that characterize females as less likely to be targeted as a threat, possibly due to stereotypes or predisposed social biases, as opposed to males.

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Our goal was to investigate the impact of dual-tasking on perceived workload, and to study the sensitivity of existing workload measures to components of primary and secondary tasks. Past research has investigated the impact of dual-tasking but has rarely compared the NASA-TLX and the Multiple Resources Questionnaire (MRQ). We made comparisons between workload measures completed after execution of a single-task trial (signal detection task) as compared to a dual-task trial (signal detection accompanied by a secondary auditory task). Special attention was given to comparing the results from the NASA-TLX and the MRQ, along with investigating the sensitivity of the MRQ to specific task components. Facets of the MRQ were analyzed to explore their sensitivity to specific task loading. Results indicate that while both measures reliably registered a change in workload, the MRQ was more sensitive to the source of the change and presented a more holistic picture of cognitive demands.