Predicting Task-Induced State Changes In A Multitasking Environment From Personality Factors

Very often, technologies are developed with more of an understanding about the tasks to be accomplished than of the mental processes associated with performing the task. In multitasking environments, this can be detrimental to system and task design since the brain may not distinguish and process tasks in the same way as systems do. This can result in technologies that work against the individual\u27s mental inclinations which can, in part, be attributed to personality factors. The present study investigated the relationships between selected traits and various task outcomes in a multitasking environment. Although several traits were associated with different task-induced states, Emotional Stability and Conscientiousness were significant predictors of several task outcomes, having the most lasting effects throughout the tasks

Determining Language For Human To Robot Navigational Commands

Robots that collaborate with humans must be equipped with interfaces that support deeper and richer interaction. Such interfaces may involve the understanding and production of speech. This calls for an understanding of speech and natural language in various contexts. The present study investigates the preferred words and phrases used in giving directions to a robot teammate in an intelligence and surveillance reconnaissance (ISR) mission. Results indicate that participants may have had a perceptual mental model that influenced choice of words or phrases. Recommendations for future research include examining the factors that affect development of schemas when interacting with robots

Using a simulated environment to investigate experiences reported during space travel

Astronauts report certain experiences that can be classified as awe and wonder when looking out of their space station or shuttle portals at two different stimuli: the earth and deep space. Based on these reports, it was of interest to further investigate those types of experiences by using a mixed-reality environment resembling an International Space Station workstation designed to expose subjects to simulated stimuli of the earth and deep space. The study is multidisciplinary, involving simulation construction, physiological assessment, psychological testing, textual analysis, and phenomenological interviews. The goal was to induce in the average person the experiences and responses of the astronauts. Preliminary results show promise for using a virtual/mixed-reality environment in a laboratory when assessing cognitive/affective experiences, such as awe and wonder, found in a real-world contextPeer reviewe

Comparison Of Measures Used To Assess The Workload Of Monitoring An Unmanned System In A Simulation Mission

As the deployment of unmanned systems becomes increasingly mainstream, it is crucial to understand the effects of the workload (WL) associated with operating and interacting with these systems. There are multiple categories and types of WL measures, but not all meet the criteria for useful measures. It is not uncommon to find that multiple WL measures for the same task do not concur, which raises questions about whether there should be specific WL measures for certain tasks, and if so, how that should be determined. The present experiment investigated the sensitivity of various physiological and self-report measures in detecting changes in WL elicited by different levels of task demands in two tasks. Each participant was asked to assume the role of a Soldier in a human-robot team performing a simulated intelligence, surveillance, and reconnaissance (ISR) mission. The mission entailed performing a change detection task and a peripheral task of maintaining awareness of the robot teammate\u27s location and surroundings. Auditory prompts were presented to probe the participant\u27s situation awareness of the robot, with regard to its direction of travel and features of its surroundings. Physiological devices used to assess WL were the electroencephalogram (EEG), electrocardiogram (ECG), transcranial Doppler (TCD), functional Near-Infrared (fNIR), and eye tracker. Self-report measures included the TLX and DSSQ. Findings from the present experiment inform developers of unmanned systems about the sensitivity of various WL measures in assessing levels of mental demands imposed by working with unmanned systems

Human Interaction With Robotic Systems: Performance And Workload Evaluations

We first tested the effect of differing tactile informational forms (i.e. directional cues vs. static cues vs. dynamic cues) on objective performance and perceived workload in a collaborative human–robot task. A second experiment evaluated the influence of task load and informational message type (i.e. single words vs. grouped phrases) on that same collaborative task. In both experiments, the relationship of personal characteristics (attentional control and spatial ability) to performance and workload was also measured. In addition to objective performance and self-report of cognitive load, we evaluated different physiological responses in each experiment. Results showed a performance–workload association for directional cues, message type and task load. EEG measures however, proved generally insensitive to such task load manipulations. Where significant EEG effects were observed, right hemisphere amplitude differences predominated, although unexpectedly these latter relationships were negative. Although EEG measures were partially associated with performance, they appear to possess limited utility as measures of workload in association with tactile displays. Practitioner Summary: As practitioners look to take advantage of innovative tactile displays in complex operational realms like human–robotic interaction, associated performance effects are mediated by cognitive workload. Despite some patterns of association, reliable reflections of operator state can be difficult to discern and employ as the number, complexity and sophistication of these respective measures themselves increase

Adaptive Automation as a Task Switching and Task Congruence Challenge

Adaptive automation refers to a system capability that enables task sharing between a human operator and a system. The purpose for this type of collaborative sharing is to maintain a moderate level of task load, particularly in a multi-tasking environment. However, some costs might accrue from switching automation on and off, as is shown from task switching literature. Additionally, it is possible that congruency between task demand and the level of automation affects performance. Thus, before system-controlled adaptive automation is implemented into an operational environment, the goal for the present experiment is to examine the costs associated with turning automation on and off and to investigate the effects of demand/automation congruence. Analysis of the congruence effects revealed performance to benefit from higher levels of automation, regardless of task load. Task switching caused by adaptive automation was found to be detrimental to performance during periods of high task demand, but was beneficial during periods of low demand. Copyright 2011 by Human Factors and Ergonomics Society, Inc. All rights reserved

Selecting Workload And Stress Measures For Performance Prediction

The study of performance, workload, and stress have become a mainstay in the field of Human Factors. These constructs are multi-faceted and are assessed by a variety of measures. In seeking to enhance performance by managing mental workload and stress, it is important for measures to be anchored to meaningful criteria. Workload and stress must be considered with respect to the performance measures that address the most central objectives. While workload and stress research has progressed over the years and includes research across different levels and domains, there has been less effort to link measures to specific performance outcomes. The present study examined four performance metrics from the same task in terms of the workload and stress measures that are most closely associated with, and predictive of them. Results indicated that different sets of workload and stress measures predicted different performance measures, suggesting that measures should also be selected based on the performance criteria of interest

What To Automate: Addressing The Multidimensionality Of Cognitive Resources Through System Design

The implementation of automation relies on the assumption that automation will reduce the operator\u27s cognitive demand and improve performance. However, accepted models demonstrate the multidimensionality of cognitive resources, suggesting that automation must support an appropriate resource dimension to have an appreciable effect. To evaluate this theory, the present study examined the impact of various types of automation on an unmanned ground vehicle (UGV) operator\u27s performance, workload, and stress. The use of a visually demanding task allowed for comparison between an auditory alert (supporting the heavily burdened visual dimension) and a driving aid (supporting action execution, a relatively unburdened cognitive dimension). Static and adaptive (fluctuating based on task demand) levels were implemented for each automation type. Those receiving auditory alerts exhibited better performance and reduced Worry, but also increased Temporal Demand and Effort relative to those receiving driving automation. Adaptive automation reduced workload for those receiving the auditory alerts, and increased workload for those receiving the driving automation. The results from this research demonstrate the need to consider the multidimensionality of the operator\u27s cognitive resources when implementing automation into a system. System designers should consider the type of automation necessary to support the specific cognitive resources burdened by the task. © 2013, Human Factors and Ergonomics Society

Resilient Autonomous Systems: Challenges And Solutions

Advances in the technology of autonomous systems calls for an examination of the factors that confer resilience on the human-machine system. We identify challenges for teaming between human operators and autonomous systems associated with cognitive demands, trust and operator self-regulation. Solutions to these challenges partly require designing systems for effective signaling of capabilities and \u27intent\u27 to the human operator. They also require selection and training of operators to team with systems that may simulate intelligent, social behaviors, as well as diagnostic monitoring of operator neurocognitive status. Implementing such solutions supports resilience at a systems level, so that machine and human can compensate for each other\u27s limitations in challenging circumstances

Assessing Multimodal Interactions With Mixed-Initiative Teams

The state-of-the-art in robotics is advancing to support the warfighters’ ability to project force and increase their reach across a variety of future missions. Seamless integration of robots with the warfighter will require advancing interfaces from teleoperation to collaboration. The current approach to meeting this requirement is to include human-to-human communication capabilities in tomorrow’s robots using multimodal communication. Though advanced, today’s robots do not yet come close to supporting teaming in dismounted military operations, and therefore simulation is required for developers to assess multimodal interfaces in complex multi-tasking scenarios. This paper describes existing and future simulations to support assessment of multimodal human-robot interaction in dismounted soldier-robot teams