Kenneth McAnally

The current generation of virtual reality (VR) technologies has improved substantially from legacy systems, particularly in the resolution and latency of their visual display. The presentation of haptic cues remains challenging, however, because haptic systems do not generalise well over the range of stimuli (both tactile and proprioceptive) normally present when interacting with objects in the world. This study investigated whether veridical tactile and proprioceptive cues lead to more efficient interaction with a virtual environment. Interaction in the world results in spatial and temporal correlation of tactile, proprioceptive and visual cues. When cues in VR are similarly correlated, observers experience a sense of embodiment and agency of their avatars. We investigated whether sensorimotor performance was mediated by embodiment of the avatar hands. Participants performed a Fitts’ tapping task in different conditions (VR with no haptics, active haptics, passive haptics, and on a real touchscreen). The active-haptic condition provided abstract tactile cues and the passive haptic condition provided veridical tactile and proprioceptive cues. An additional (hybrid haptics) condition simulated an ideal passive haptic system. Movement efficiency (throughput) and embodiment were higher for the passive than for the active and no-haptics conditions. However, components of embodiment (perceived agency and ownership) did not predict unique variance in throughput. Improved sensorimotor performance and ratings of presence and realism support the use of passive haptics in VR environments where objects are in known and stable locations, regardless of whether performance was mediated by the sense of embodiment.

Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low‐fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near‐optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA.

Metacognitive monitoring and control of situation awareness (SA) are important for a range of safety-critical roles (e.g., air traffic control, military command and control). We examined the factors affecting these processes using a visual change detection task that included representative tactical displays. SA was assessed by asking novice observers to detect changes to a tactical display. Metacognitive monitoring was assessed by asking observers to estimate the probability that they would correctly detect a change, either after study of the display and before the change (judgement of learning; JOL) or after the change and detection response (judgement of performance; JOP). In Experiment 1, observers failed to detect some changes to the display, indicating imperfect SA, but JOPs were reasonably well calibrated to objective performance. Experiment 2 examined JOLs and JOPs in two task contexts: with study-time limits imposed by the task or with self-pacing to meet specified performance targets. JOPs were well calibrated in both conditions as were JOLs for high performance targets. In summary, observers had limited SA, but good insight about their performance and learning for high performance targets and allocated study time appropriately.