Daniel N. Cassenti

The Effects of Communication Style on Robot Navigation Performance

With the advance of computer speech recognition programs, robotic operators have a new method of robot-operator communication. An experiment was conducted using a “Wizard of Oz” paradigm to investigate how different styles of communication affect robot navigation performance. Using manual inputs, verbal commands (restricted to directions only), and verbal commands with object referent labels, participants navigated a simulated robot through various simulated indoor environments. Results indicated that manual control was faster than free form verbal commands but not faster than simple directional commands. When provided the opportunity, participants did use object labels particularly objects related to the structure of the building (doors, rooms, and halls). Discussion focuses on improving robotic communication and object recognition in a robotic control system.

Towards the Shape of Mental Workload

Mental workload is a measure of how much mental effort a person devotes to one or more tasks. In two experiments, we investigated the effect of multiple identical tasks on human performance in terms of both accuracy and response time for a visuo-spatial task set and an auditory task set. The findings showed that participants performed linearly worse on some measures of performance when the number of tasks increased, while other measures showed two distinctive variations on this linear decrease in performance. We discuss these results in terms of their effect on the traditional linear representation of workload in IMPRINT (IMproved Performance Research INtegration Tool, Archer & Adkins, 1999), a task-based human performance modeling system.

Observing and modeling cognitive events through event-related potentials and ACT-R

The study of cognition is generally thought to rely on techniques for inferring cognitive processes that are unobservable. One approach to cognitive science is to leverage an understanding of structure and function of the nervous system based on observable neurological events to determine mental processing. Event-related potential (ERP) research offers one technique to objectively measure cortical responses that are believed to be associated with perceptual and cognitive processes. Here, two ACT-R (Adaptive Control of Thought - Rational) models of mental processing are adapted based on the results of two ERP experiments. The models provide both a sequence of mental steps required to complete each task and a greater specificity of time course of mental events than traditional ACT-R models. We conclude with implications of this research for cognitive theory and ACT-R as well as future work to be conducted.

Modeling a robotics operator manager in a tactical battlefield

In a tactical battlefield, ground- and air- based robotic assets can be useful, but there is currently no one role to coordinate the use of multiple robots in the U.S. Army. The current work outlines and models the duties of a robotics operator manager (ROM), which could serve this role, using a theory of dynamic decision-making, instance-based learning (IBL). A model of the ROM is created that is based upon the IBL theory and that is intended to be a model of ROMs cognition. The IBL model first consults a Tactical Ground Reporting Network (TiGRNet) to recognize potential threat locations in a simulated battlefield. Then, the model decides whether a potential threat location warrants more investigation and if so weighs a number of factors to determine the type of robotic surveillance required (i.e., ground- or air- based). These factors include: the presence of certain attributes in a threat location, the utility of investigating a threat determining priority of investigation, the altitude of the terrain where the threat is located, whether there is time pressure, and the number of threat locations to investigate. The execution of the IBL model of the ROM generates predictions of the decisions made by a ROM under different factors. Future work in this area will focus on collecting human data to validate and adjust the predictions made by the IBL model of the ROM.

Modeling the Workload-Performance Relationship

Human factors research is often focused on the mental workload that is required to perform a task or set of tasks with the goal of reducing workload to make systems easier to manage. The Improved Performance Research Integration Tool (IMPRINT) includes an algorithm to predict mental workload. The algorithm was developed using subject matter expert ratings of workload tasks. We aimed to enhance this capability by developing algorithms using data from four new studies investigating change in performance as demands on mental resources increase. The results indicate three task types of similar difficulty and one task type of much greater difficulty. We then map these to our hypothesized workload function. Finally, we propose a way forward in modeling performance as a function of workload in IMPRINT.

Underestimating numerosity of items in visual search tasks.

Previous research on numerosity judgments addressed attended items, while the present research addresses underestimation for unattended items in visual search tasks. One potential cause of underestimation for unattended items is that estimates of quantity may depend on viewing a large portion of the display within foveal vision. Another theory follows from the occupancy model: estimating quantity of items in greater proximity to one another increases the likelihood of an underestimation error. Three experimental manipulations addressed aspects of underestimation for unattended items: the size of the distracters, the distance of the target from fixation, and whether items were clustered together. Results suggested that the underestimation effect for unattended items was best explained within a Gestalt grouping framework.

Multi-level Cognitive Cybernetics in Human Factors

Cybernetics provides a framework for understanding the behavior of closed-loop systems, including the feedback control intrinsic to cognitive systems (Smith and Smith in continuing the conversation: a newsletter of ideas in cybernetics. Greg and Pat Williams, Gravel Switch, KY, [1]). We propose adopting our interpretation of the cybernetics concept of feedback control of cognition by integrating across metacognition, performance, computational cognitive modeling, and physiological levels of analysis. To accomplish this objective, we tie cognitive variables to each level of analysis, including: (1) metacognition—self-evaluation of cognition; (2) performance—objective measures of progress toward a goal state; (3) physiology—indications of cognitive function (e.g., heart rate variability as an index of the level of task engagement); and (4) cognitive models—prediction and understanding of empirical results using sequences of cognitive steps. We call this integrative approach, Multi-Level Cognitive Cybernetics (MLCC). In this paper, we define the MLCC framework, discuss how MLCC can inform the design of adaptive automation technologies, and discuss the benefits of the MLCC approach in human factors.

Visualizing approaches for displaying measures of sentiment

The overall purpose of intelligence analysis platforms is to extract key information from multi-source data. Ultimately, these systems are meant to save intelligence analysts time and effort by offering knowledge discovery capabilities. However, intelligence analysis platforms only assist analysts to the extent they are designed with human factors in mind. Poorly designed intelligence analysis platforms can hinder the knowledge discovery process, or worse, promote the misinterpretation of analysis results. Future intelligence systems must be critical enablers for improving speed, efficiency, and effectiveness of command-level decision making. Human-centered research is needed to address the challenge of visualizing large data collections to facilitate orientation and context, enable the discovery and selection of relevant information, and provide dynamic feedback for identifying changes in the state of a targeted region or topic. From the perspective of the ‘Human as a Data Explorer,’ this study investigates the visual presentation of intelligence information to support timely and accurate decision making. The investigation is a starting point in understanding the rich and varied set of information visualizations sponsored by the Army in recent years. A human-subjects experiment explores two visualization approaches against a control condition for displaying sentiment about a set of topics with an emphasis on the performance metrics of decision accuracy and response time. The resulting data analysis is the first in a series of experiments providing input for technology development informing future interface designs and system prototypes.

A network science approach to future human-robot interaction

The vision for future Soldier-robot relationships has supported the transition of the robots role from a tool to an integrated team member. This vision has provided support for the advancement of robot autonomy and intelligence as a means to better support action and cognitive decision-making in the network-centric operational environment. To accomplish this goal, the Soldiers perspective of the human-robot interaction must be further developed, as it directly impacts overall situation management: mission planning, operational roles, function allocation, and decision-making. Here we present a theoretical concept paper that promotes using the foundation of network science to better understand how and why advances in effective Soldier-robot situation management may be realized. We begin by providing a primer on how a network science approach may be used to understand multi-agent teams and network-centric operations. This is followed with a review on the impact of human perception on the human-robot team network structure. Two key points are highlighted. First, the network structure is influenced by the extent to which a Soldier-robot coupling performs independent operations. Second, the degree of automaticity for several properties of the robot specifies the strength of their networked relationship. We conclude with possible advantages of using a network science approach for understanding situation management of Soldier-robot teams in an operational environment. This approach provides a structure for creating visual maps of team structures to understand perceived and anticipated role interdependency, which thus provides the foundation for developing a mathematical description of the dynamic Soldier-robot relationship.

Effects of information accuracy and volume on decision making

ABSTRACT Recent advances in information technology have resulted in increasingly complex work environments. Much of the complexity is due to a deluge of information available across networks (Gleick, 2011) and unknown reliability, which introduces additional uncertainty (Platt & Huettel, 2008). Understanding the performance characteristics of human information processing for optimal decision making under such conditions of data abundance and uncertainty is critical in complex networked information environments, such as military operations. One focus of the United States military is providing troops with the richness of information formally reserved for higher echelons (Bawden & Robinson, 2009). However, this effort risks cognitive overload, where too much information can result in impaired performance. The present study examined human decision making under varying levels of cognitive load and source reliability. Participants determined the reliability of two information sources and decided how to use them to minimize cognitive load and improve performance in a visual search task. Unbeknownst to participants, one source provided highly accurate information and one provided moderately accurate information. Results showed that participants had more trust in the more accurate than the less accurate source, and decision making accuracy decreased as cognitive load increased. When cognitive load was highest, participants were more accurate on trials with the more accurate source. Thus, the more accurate source facilitated better performance, whether through better intel or through participants offloading some of the cognitive load to the reliable source.