Hari Thiruvengada

Analyses of team performance in a dynamic task environment.

Teamwork, a central component of team research, is not readily observable and must be inferred from the manner in which teams operate. Of particular interest is the measurement and evaluation of teamwork. The goal of this paper is to explore the assessment of team data using a temporal accuracy measure called the Relative Accuracy Index (RAI). For the statistical analysis, the generalized mixed model was applied. This model is applicable for binomial data and takes into account the correlation structure within team members. We describe the statistical procedure in detail, aiming to guide researchers who encounter similar problems. Using our statistical analysis, we found that participants whose training focused on coordination activities outperformed those whose training did not. Moreover, we found that workload stress accentuates the difference.

Investigating the effects of metacognition in dynamic control tasks

Metacogntion is a broadly studied concept in cognitive science, educational psychology and developmental psychology. Prior research on metacognition shows that successful learning is often based on specific metacognitive activities which have to be frequently monitored during learning [1]. An important limitation in prior studies is that most experimental tasks were designed within static environments. Only rarely have researchers investigated metacognition during dynamic decision making. Therefore, the purpose of this research is to investigate the significance of metacognition while training to execute dynamic control tasks. Findings from this study demonstrate that the experimental group which was provided more focused feedback had higher situation awareness accuracy, relative to the groups which received relatively less focused feedback or no feedback. In addition, results indicated that metacognitive behaviors which were measured using subjective ratings of Pre and Post Meta probes showed different patterns. Preliminary findings provides evidence that enhanced metacognition has a significant impact on actual situation awareness in dynamic control tasks. Detecting causes for these changes is the next step of the research.

Time windows-based team performance measures: a framework to measure team performance in dynamic environments

In this paper, we present a framework for implementing Team Performance Measures based on a temporal accuracy measure called the relative accuracy index (RAI) to evaluate and compare team performance in a command-and-control human-in-the-loop simulation. The framework allows researchers to collect and analyze team outcomes in an unbiased objective manner based on a temporal performance measure known as a Time Window. Our framework provides experimenters and subject matter experts the necessary tools to evaluate performance in terms of task demands. We also provide a sample analysis of individual and team performance using the RAI.

Affordance-based computational model of driver behavior on highway systems: A Colored Petri Net approach

In this paper, an affordance-based colored Petri net (CPN) model for representing driver behavior is proposed. We adopt a simulation-based approach and conduct an analysis of driver affordances on a driving task on highway systems. The computational CPN model is an extension of the initial conceptual CPN model and allows experimenters to enforce driving preferences as preferential turn probabilities for individual drivers on the highway system. There are two types of driver models: confederate driver model (CDM) and subject driver model (SDM). Whilst, the CDM follows a pre-scripted path of a confederate driver in actual empirical scenarios, the SDM uses a computational algorithm (implemented within the CPN model) to plan a path based on SDM and CDM affordance derived from attributes such as position, velocity and acceleration. This model allows experimenters to analyze and compare the set of affordances that are available for each driver within this dynamic environment. We conclude by providing a descriptive statistical analysis of the results obtained by comparing the empirical and model-predicted driver data for specific scenarios.

Balancing Trust and Automation Needs for Effective Home Energy Management

With the increasing shortage of energy resources and the adverse impact of non-renewable fuels on the environment, there is a shift in the consumer’s mindset to emphasize managing and utilizing energy efficiently, reducing green house emissions and contributing to a clean environment. This is especially true to the residential markets where a trusted Home Energy Manager (HEM) device can aid in automating and delivering effective energy management strategies in homes. Home users are often passive in their interaction and have to be engaged and reassured that a HEM device contributes positively to the goal of home energy management. The objective is to boost their trust and confidence in HEM by making information (such as energy costs, usage patterns, etc.) accessible and enabling them to act and conserve energy effectively based on the same. In this research, we explore and understand the potential factors that influence how users would engage and interact with HEM device. Some basic functions of the HEM device include: a trusted advisor that provides dynamic recommendations based on user’s interaction and behavior in the home; ability to sense occupancy within the home and automatically adjust schedules without the need for explicit human intervention; deduce energy usage patterns; and adapt energy management strategies based on the user profiles derived from their behaviors and interaction with the thermostat. Using a HEM device with the proper balance of automation and user engagement can have a positive impact on reducing the global energy consumption and the sustenance of our environment.

Training for Metacognition in Simulated Environments

Metacognition has been recognized as an important mechanism in the learning process within the cognitive psychology and education literatures. However, due to its focus on relatively static domains, there are several constraints in applying the concept to real-world domains that are highly complex and dynamic in nature. For example, being able to self-regulate the selection of our skills and strategies is essential to maintain a high level of human performance in dynamic environments. Therefore it is important to identify effective training mechanisms to improve metacognition while performing in real-world contexts. An effective platform for cognitive training is human-in-the-loop simulations or virtual environment-training. Hence, in this chapter, we have briefly described the manner in which metacognition is currently defined in the literature and the limitations in its current direction. After identifying the limitations, we provide a definition for the concept of metacognition that may increase its applicability to dynamic domains. Furthermore, we have listed guidelines for developing effective metacognitive training methods in virtual environments as well as an example of the application of these guidelines.

Time Windows-based Team Performance Measures: Design and Implementation

Team Performance Measures (TPM) are pivotal to the evaluation of both individual and team performance in a team environment. We propose a temporal construct called time windows to collect TPM. The design and implementation time windows within a multi-user, multi-modal team-in-the-loop simulation test bed known as TASP (Team Aegis Simulation Platform) is discussed. Finally, a sample performance analysis based on time windows is conducted.

PerFECT: An Automated Framework for Training on the Fly

Currently available cognitive training systems can highly benefit from more adaptable and encapsulated frameworks that include better performance assessment methods, robust feedback mechanisms and automated mechanisms that reduce the manual intervention and curriculum management required during training sessions. In short, there is an ardent need for an automated human in the loop training system that can effectively train cognitive skills required for military operations. An automated training system would be extremely beneficial if it can be easily coupled with a synthetic learning environment to function autonomously is an entirely data driven manner. Such a system would enable rapid deployment of key training scenarios, skills and tactics to war fighters and help them maintain a superior level of competence in the battlefield. An automated framework for training on the fly also known as performance feedback engine for conflict training (PerFECT) which includes key components for simulating training scenarios, measuring trainee’s performance, providing relevant feedback and dynamic curriculum management is discussed in this chapter. First, the training system comprises of custom plug-in interface that allows components of the training framework to readily interface with a simulated virtual learning environment. Second, it has a “Performance Evaluator” that enables automated, real-time and objective evaluation of a trainee’s performance grounded within an objective framework known as time window and enables run-time evaluation of performance skills based on a skills matrix. Third, PerFECT has a “Feedback System” that can provide contextual and immediate feedback to trainees based on process measures. Finally, PerFECT includes a “Curriculum Manager” that dynamically selects appropriate training scenario from a template library with varying levels of complexity. The selection algorithm for training scenario is based on the trainee’s historical performance scores and complexity of the earlier scenarios. We also present the initial findings from a pilot study which helps illustrate the capabilities of the framework and conclude with future directions in this area of research.

Using Petri Nets for Gibson's Affordances: First Steps Into Perception-Based Task Analysis

In this paper, we introduce the notion of affordance proposed by Gibson, and provide a computational formalism based on Petri Nets (PNs) for conducting perception-based task analysis. Gibson used affordance to refer to what an environment offers an animal for either ill or good. Since then, affordance has been widely adopted and used in several areas such as human computer interaction, mobile robotics, etc. We argue that Petri Net provides the appropriate framework for performing a perception-based task analysis (PTA) as it helps investigate the task from an ecological perspective based on concepts such as affordance, effectivity and actualizations. Additionally, Petri Net can be used to study behavioral properties such as reachability, boundedness and liveness, which relate to the perception-based task. We illustrate how the ecological concepts can be modeled using the proposed PN formalism with reference to a driving task. We conclude that Petri Net is a very useful tool for conducting perception-based task analysis.

Comparing Active vs. Passive Participation in a Virtual Tour An Initial Look at User Attitudes and Preferences

In corporate virtual world (VW) demonstrations, it can oftentimes be difficult to gain the active participation (i.e., first hand interaction) of all users of the demonstration. Due to the general willingness or ability to register with VWs (e.g., Second Life®) and self-efficacy associated with controlling an avatar, many users may be more apt to participate passively (e.g., watch someone else interact). Therefore, in the present work, we investigated the differences and similarities in the attitudes between visitors to a virtual tour, who either actively or passively participated. The results of the study indicated that large group active participation led to more confusion and distraction when compared to large group passive participation. However, passive participants indicated less confidence in their ability to interact with the tour on their own. This paper concludes with lessons learned and recommendations for this virtual tour.