Operators and users interacting with computer environments often have to deal with multiple tasks at once, responding to each in series. Diagnostic automation, that is, automation that alerts users when and where to look, has been suggested to support the unique challenges of multiple task environments: activating tasks, switching between tasks, and tasks interfering with each other. Automation is not always reliable, however. Because of the common interaction with novel systems and the importance of training, the Simultaneous Task Environment Platform program—a multiple-task environment—was developed to understand the effects of experience on interaction with these automation-supported systems, as well as what strategies were developed. It was found that participants became more efficient with experience only when they interacted with higher reliability automation. Furthermore, the strategies participants developed focused on the differences between tasks and patterns across those tasks. Automated systems training should be sure to employ these findings.

A critical construct related to human-robot interaction (HRI) is autonomy, which varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots may interact with one another. Thus, there is a need to understand HRI by identifying variables that influence - and are influenced by - robot autonomy. Our overarching goal is to develop a framework for levels of robot autonomy in HRI. To reach this goal, the framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, the framework proposes a process for determining a robot's autonomy level, by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as guidelines to determine autonomy, categorize the LORA along a qualitative taxonomy, and consider which HRI variables (e.g., acceptance, situation awareness, reliability) may be influenced by the LORA.

Researchers have suggested that attention is a key moderating variable predicting performance with an input device [e.g., Greenstein & Arnaut, 1988] without directly assessing the attention demands of devices We hypothesized that the attentional demands of input devices would be intricately linked to whether the device matched the input requirements of the on-screen task. Further, matching task and device should be more important for attentionally reduced groups, such as older adults. Younger and older adults used either a direct (touch screen) or indirect (rotary encoder) input device to perform matched or mismatched input tasks under a spectrum of attention allocation conditions. Input devices required attention - more so for older adults, especially in a mismatch situation. In addition, task performance was influenced by the mach between task demands and input device characteristics. Though both groups benefited from a match between input device and task input requirements, older adults benefited more and this benefit increased as less attention was available. We offer an a priori method to choose an input device for a task by considering the overlap between device attributes and input requirements. These data have implications for design decisions concerning input device selection across age groups and task contexts.

In this investigation we evaluated the effect of two types of factors that affect human-automation interaction: those specific to the automation (Error Type: miss versus false alarm) and those specific to the human (domain experience, in this study automated farm equipment experience versus no experience). Participants performed a simulated harvesting task and used an obstacle avoidance automated decision aid. The type of unreliability of the automation had a major impact on behavioral reliance as a function of components of the avoidance decision task. The analysis of the effects of domain experience on automation use indicated that those with experience operating agricultural vehicles had different tendencies of reliance. Specifically, participants with experience operating agricultural vehicles were less likely to rely on automated alarms than those without experience. The results of this investigation have important implications for understanding how humans adjust their behavior according to the characteristics of an automated system

We investigated performance in a complex, dynamic decision-making task. Individual judgment performance in a command and control environment was modeled as linear combinations of environmental cue values using a lens model approach. Examination of the judgment models indicated that participants had similar judgment policies, while correlational and error analyzes indicated that performance differences were due to participants' abilities to execute judgment strategies rather than their knowledge of the task environment. The research demonstrated how the lens model approach can be extended to account for dynamic aspects of decision making in complex environments, through the use of individual, time dependent environmental models for each participant. Additionally, the research showed that a lens model approach is useful for characterizing factors in individual performance in complex judgment tasks. Empirically, this research suggests that training on task knowledge should be supplemented by training which focuses on the consistent execution of judgment strategies.