Visuo-spatial Abilities In Remote Perception: A Meta-analysis Of Empirical Work

Meta-analysis was used to investigate the relationship between visuo-spatial ability and performance in remote environments. In order to be included, each study needed to examine the relationship between the use of an ego-centric perspective and various dimensions of performance (i.e., identification, localization, navigation, and mission completion time). The moderator analysis investigated relationships involving: (a) visuo-spatial construct with an emphasis on Carroll’s (1993) visualization (VZ) factor; (b) performance outcome (i.e., identification, localization, navigation, and mission completion time); (c) autonomy to support mission performance; (d) task type (i.e., navigation vs. reconnaissance); and (e) experimental testbed (i.e., physical vs. virtual environments). The process of searching and screening for published and unpublished analyses identified 81 works of interest that were found to represent 50 unique datasets. 518 effects were extracted from these datasets for analyses. Analyses of aggregated effects (Hunter & Schmidt, 2004) found that visuo-spatial abilities were significantly associated with each construct, such that effect sizes ranged from weak (r = .235) to moderately strong (r = .371). For meta-regression (Borenstein, Hedges, Figgins, & Rothstein, 2009; Kalaian & Raudenbush, 1996; Tabachnick & Fidell, 2007), moderation by visuo-spatial construct (i.e., focusing on visualization) was consistently supported for all outcomes. For at least one of the outcomes, support was found for moderation by test, the reliability coefficient of a test, autonomy (i.e. to support identification, localization, and navigation), testbed (i.e., physical vs. virtual environment), intended domain of application, and gender. These findings illustrate that majority of what researchers refer to as “spatial ability” actually uses measures that load onto Carroll’s (1993) visualization (VZ) factor. The associations between this predictor and all performance outcomes were significant, but the significant iv variation across moderators highlight important issues for the design of unmanned systems and the external validity of findings across domains. For example, higher levels of autonomy for supporting navigation decreased the association between visualization (VZ) and performance. In contrast, higher levels of autonomy for supporting identification and localization increased the association between visualization (VZ) and performance. Furthermore, moderation by testbed, intended domain of application, and gender challenged the degree to which findings can be expected to generalize across domains and sets of participants

Transparency in human-agent teaming and its effect on complacent behavior

This study examined how transparency of an intelligent agent\u27s reasoning affected complacent behavior in a route selection task in a simulated environment. Also examined was how the information available to the operator affected those results. In two experiments, participants supervised a three-vehicle convoy as it traversed a simulated environment and re-routed the convoy when needed with the assistance of an intelligent agent, RoboLeader. Participants were randomly assigned to an Agent Reasoning Transparency condition. Participants received communications from a commander confirming either the presence or absence of activity in the area. They also received information regarding potential events along their route via icons that appeared on a map displaying the convoy route and surrounding area. Participants in Experiment 1 (low information setting) received information about their current route only; they did not receive any information about the suggested alternate route. Participants in Experiment 2 (high information setting) received information about both their current route and the agent recommended an alternative route. In the first experiment, access to agent reasoning was found to be an effective deterrent to complacent behavior when the operator has limited information about their task environment. However, the addition of information that created ambiguity for the operator encouraged complacency, resulting in reduced performance and poorer trust calibration. Agent reasoning did not increase response time or workload and appeared to have improved performance on the secondary task. These findings align with studies that have shown ambiguous information can increase workload and encourage complacency, as such, caution should be exercised when considering how transparent to make agent reasoning and what information should be included. In the second experiment, access to agent reasoning was found to have little effect on complacent behavior when the operator had complete information about the task environment. However, the addition of information that created ambiguity for the operator appeared to encourage complacency, as indicated by reduced performance and shorter decision times. Agent reasoning transparency did not increase overall workload, and operators reported higher satisfaction with their performance and reduced mental demand. Access to agent reasoning did not improve operators\u27 secondary task performance, situation awareness, or operator trust. However, when agent reasoning transparency included ambiguous information complacent behavior was again encouraged. Unlike the first experiment, there were notable differences in complacent behavior, performance, operator trust, and situation awareness due to individual difference factors. As such, these findings would suggest that when the operator has complete information regarding their task environment, access to agent reasoning may be beneficial, but not dramatically so. However, individual difference factors will greatly influence performance outcomes. The amount of information the operator has regarding the task environment has a profound effect on the proper use of the agent. Increased environmental information resulted in more rejections of the agent recommendation regardless of the transparency of agent reasoning. The addition of agent reasoning transparency appeared to be effective at keeping the operator engaged, while complacent behavior appeared to be encouraged both when agent reasoning was either not transparent or so transparent as to become ambiguous. Even so, operators reported lower trust and usability for the agent than when environmental information was limited. Situation awareness (SA2) scores were also higher in the high information environment when agent reasoning was either not transparent or so transparent as to become ambiguous, compared to the low information environment. However, when a moderate amount of agent reasoning was available to the operator, the amount of information available to the operator had no effect on the operators\u27 complacent behavior, subjective trust, or SA. These findings indicate that some negative outcomes resulting from the incongruous transparency of agent reasoning may be mitigated by increasing the information the operator has regarding the task environment

An Approach to Modeling Simulated Military Human-agent Teaming

With the rise of human-agent teaming (HAT), a new cycle of scientific discovery commenced. Through scientific discovery, a number of theories of constructs in HAT were developed, however, an overarching model is lacking that elucidates the relative importance of these constructs in relation to human performance. The main objective of this research was to develop a model of simulated military HAT and to validate it against selected empirical data. Experimental data borrowed from four simulated military HAT studies were used to test the proposed Core model. The Core model was assumed to be directly affecting task performance and consisted of constructs related to Task Composition, Task Perception, and the qualities that each team member (Human/Agent Qualities) brings to the team. The available experimental data were tested against the null model: everything, within and between these Core sections, are equal contributors to hit rate. Furthermore, in order to validate the Core model, a validation approach was developed based on relative importance, wherein the outcome was a proportional value and followed a beta distribution (Ferrari & Cribari-Neto, 2004). This new modeling approach consisted of (1) application of dominance analysis (DA; Azen & Budescu, 2003; Budescu, 1993) to determine the most important contributors to task performance, (2) establishing robustness and generalizability of the dominance outcome through bootstrap procedures (Azen & Budescu, 2003; Efron, 1981), and (3) combining the dominant predictors into a full beta regression model to evaluate the fit and significance of the model (Ferrari & Cribari-Neto, 2004). DA of all four experimental studies examined in this research led to rejecting the null hypotheses. Constructs in the proposed Core model were not equally important to performance in these simulated military HAT studies. Results showed consistently similar yet different dominance patterns in relation to human performance. Attempts were made to elucidate the most important predictors of task performance. Analyses unveiled the importance of taking task difficulty into consideration when assessing the relative importance within the proposed Core model

Roboleader For Reconnaissance By A Team Of Robotic Vehicles

Autonomous teams of robotic vehicles are gaining significant importance in military applications in the realm of reconnaissance and the execution of vital tasks. As teams of robotic vehicles grow in size and mission complexity, ever increasing burden is placed on the human operators charged with overseeing such operations. It is imperative that, in order to increase future mission complexity and success, a certain amount of work load be removed from the operator and a greater level of autonomy be given to the unmanned systems. A modular architecture has been developed which allows for components to be added on to the RoboLeader utility providing expanded capability of the design with little modification to existing architecture. Currently, the RoboLeader utility assesses intelligence on ground conditions and provides the human operator with alternate path plans for a series of situations which require difference path solutions. © 2010 IEEE