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An Activity Theory framework was applied in investigating the pressing issue of Unmanned Aircraft System (UAS) integration into the National Airspace System. As stated in the FAA’s UAS Operational Approval policy notice, the UAS pilot and/or crew are collectively responsible for successfully exercising see-and-avoid duties. To describe how this is achieved in practice, field recordings of visual observers and other UAS crewmembers were collected during three phases of a long-endurance UAS flight test: takeoff, mid-flight, and landing. Four separate radio communications channels were utilized, and pilots’ workload was offloaded in three ways: takeoff and landing flight dynamics were offloaded to the external pilot, see-and-avoid duties were offloaded to visual observers, and some communications were offloaded to the mission commander. Visual observers relied on a combination of visual perception, communication, and team coordination skills to assist pilots and the mission commander in effectively accomplishing see-and-avoid duties during UAS operations.

Many applied screening tasks (e.g., medical image or baggage screening) involve challenging searches for which standard laboratory search is rarely equivalent. For example, whereas laboratory search frequently requires observers to look for precisely defined targets among isolated, non-overlapping images randomly arrayed on clean backgrounds, medical images present unspecified targets in noisy, yet spatially regular scenes. Those unspecified targets are typically oddities, elements that do not belong. To develop a closer laboratory analogue to this, we created a database of scenes containing subtle, ill-specified “oddity” targets. These scenes have similar perceptual densities and spatial regularities to those found in expert search tasks, and each includes 16 variants of the unedited scene wherein an oddity (a subtle deformation of the scene) is hidden. In Experiment 1, eight volunteers searched thousands of scene variants for an oddity. Regardless of their search accuracy, they were then shown the highlighted anomaly and rated its subtlety. Subtlety ratings reliably predicted search performance (accuracy and response times) and did so better than image statistics. In Experiment 2, we conducted a conceptual replication in which a larger group of naïve searchers scanned subsets of the scene variants. Prior subtlety ratings reliably predicted search outcomes. Whereas medical image targets are difficult for naïve searchers to detect, our database contains thousands of interior and exterior scenes that vary in difficulty, but are nevertheless searchable by novices. In this way, the stimuli will be useful for studying visual search as it typically occurs in expert domains: Ill-specified search for anomalies in noisy displays.

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