Paul Derby

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.

Eye Movement Behavior During Confusion Toward a Method

The purpose of the present study was to develop a methodology to identify when a user is confused while using a product. Eye movements were measured to determine whether they reflect confusion while users completed tasks with two simulated devices. First, two devices that differed in subjective ratings of confusion were identified. Then eye movements and task performance were measured while experienced and inexperienced users conducted nine tasks with the devices. The relationship between eye movement and confusion measures depended on the task and the user group. Results provide a foundation for developing methods to identify and predict user confusion on the basis of eye movements, and ultimately to design products to avoid confusion.

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.

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.