Learning to make decisions in dynamic environments: effects of time constraints and cognitive abilities

This study investigated the effects of time constraints and cognitive abilities on dynamic decision making (DDM). The learning and performance of individuals trained in a DDM task with time constraints were compared with those who were trained without time constraints. Although all participants received the same total amount of time to perform the task, individuals under more stringent time constraints were given 3 times more practice trials on the first 2 days of the study than were people under less stringent time constraints. Despite the additional practice runs, participants under high time constraints performed worse than did participants under low time constraints on the 3rd day of the study. A subsequent analysis of cognitive abilities and decision heuristics revealed that individuals' actions corresponded with simple heuristic predictions more closely with minimal practice than with extended practice, under high rather than low time constraints, and in individuals with low rather than high cognitive abilities. These findings suggest that mere repetition of a task with less time within a trial is counterproductive to learning and that learning depends on cognitive abilities. Potential applications of this research include the design of training procedures for dynamic tasks.     

An augmented reality training platform for assembly and maintenance skills

Training technicians to acquire new maintenance and assembly skills is important for various industries. Because maintenance and assembly tasks can be very complex, training technicians to efficiently perform new skills is challenging. Training of this type can be supported by Augmented Reality, a powerful industrial training technology that directly links instructions on how to perform the service tasks to the machine parts that require processing. Because of the increasing complexity of maintenance tasks, it is not sufficient to train the technicians in task execution. Instead, technicians must be trained in the underlying skills—sensorimotor and cognitive—that are necessary for the efficient acquisition and performance of new maintenance operations.These facts illustrate the need for efficient training systems for maintenance and assembly skills that accelerate the technicians’ acquisition of new maintenance procedures. Furthermore, these systems should improve the adjustment of the training process for new training scenarios and enable the reuse of worthwhile existing training material. In this context, we have developed a novel concept and platform for multimodal Augmented Reality-based training of maintenance and assembly skills, which includes sub-skill training and the evaluation of the training system. Because procedural skills are considered as the most important skills for maintenance and assembly operations, we focus on these skills and the appropriate methods for improving them.     

The troubleshooting task implementation in automotive chassis using virtual interactive technique and knowledge-based approach

In this study, a troubleshooting task model of automobile chassis is proposed. It integrates a task implementation method with virtual interactive techniques. Declarative knowledge entities make up the kernel of task model. It makes the task more controllable and automatable. Solution of the knowledge entities is directly used to support automotive chassis repair and maintenance. Furthermore, this system allows learners to operate a practical task module for troubleshooting through the Internet. The framework developed is not dependent on specific automobile style or software configuration and it uses open software (e.g. Java, MySQL, etc.) in the problem reasoning, virtual display and data storage. The framework can achieve the advantages and goals of (1) providing references for technicians in detection of chassis problems to promote repairing skills; (2) constructing a Web-based troubleshooting architecture of automotive chassis that can enhance learners’ (e.g. students, freshman, and drivers) knowledge and techniques; and (3) creating a prototype environment for distance learning to eliminate the limitation of space and time.     

Competency-based on-the-job training for aviation maintenance and inspection--a human factors approach.

More than 90% of the critical skills that an aviation maintenance technician uses are acquired through on-the-job training (OJT). Yet many aviation maintenance technicians rely on a 'degenerating buddy system', 'follow Joe around', or unstructured approach to OJT. Many aspects of the aviation maintenance environment point to the need for a structured OJT program, but perhaps the most significant is the practice of job bidding which can create rapid turnover of technicians. The task analytic training system (TATS), a model for developing team-driven structured OJT was developed by the author, and first introduced in Boeing Commercial Airplane Group to provide competency-based OJT for aviation maintenance and inspection personnel. The goal of the model was not only to provide a comprehensive, highly structured training system that could be applied to any maintenance and inspection task, but also to improve team coordination, attitude and morale. The first goal was accomplished by following the systems eight-step process, the latter through incorporating human factors principles such as decision making, communication, team building and conflict resolution into the process itself. In general, the process helps to instill mutual respect and trust, enhance goal-directed behavior, strengthen technicians' self-esteem and responsiveness to new ideas and encourage technicians to make worthwhile contributions. The theoretical background of the model is addressed by illustrating how the proven training methodologies of job task analysis and job instruction training are blended with human factors principles resulting in a unique team-driven approach to training. The paper discusses major elements of the model including needs identification, outlining targeted jobs, writing and verifying training procedures, an approval system, sequencing of training, certifying trainers, implementing, employing tracking mechanisms, evaluating, and establishing a maintenance/audit plan. Relevance to industry. TATS has been successfully installed in several maintenance and inspection areas of The Boeing Company. Four major U.S. airlines--United Airlines, TransWorld Airlines, Northwest Airlines, and USAirways have participated in two years of development and field testing in their maintenance operations (assisted by the author and Dr. Barbara Kanki of NASA Ames Research Center).     

Paradoxical effects of information presentation formats and contextual interference on transfer of a complex cognitive skill

In a 2 × 2 factorial design the effects of (1) information presentation format and (2) contextual interference on training behavior, transfer performance and mental effort were studied for learning troubleshooting skills with a computer-based simulation. Participants studied information about the functioning of an alcohol distillery system (system principles) prior to practicing troubleshooting skills. Regarding the first factor, an expository (Exp) format, in which system principles, examples and a troubleshooting strategy were presented in a textual form, was compared to an inquisitory (Inq) format, in which participants had to predict the behavior of the system after they studied the system principles and in which demonstrations of the troubleshooting strategy were given. With regard to the second factor, a low contextual interference (LCI) condition in which participants practiced to diagnose types of system failures in a blocked schedule was compared to a high contextual interference (HCI) condition, in which different failure types were practiced in a random schedule. The main hypothesis is that the Inq and HCI conditions promote the development of cognitive schemata that enable learners to diagnose a malfunctioning system component by interpreting symptoms in terms of violations of system principles. Hence, they are expected to show higher transfer performance than participants in the traditional Exp and LCI conditions, who are believed to develop schemata containing associations between symptoms and malfunctioning components that are context-bound and less useful for diagnosing new failures. Contrary to the predictions, the traditional conditions (Exp and LCI) showed higher performance on a transfer test two weeks after training. Possible explanations for this result are discussed.     

Supporting the instructional design process for team training

Conducting team training is daily business for the military. Designing team training programs and exercises, however, is not always that structured. Instructional designers are in fact trained to design instruction primarily for individuals. After their instructional design course, they learn to design team training more or less on the job. This process may be improved by offering these instructional designers adequate support. During three design-experiments, we developed and tested guidelines and a workshop supporting the analysis of team tasks and the design of team training scenarios. For the first (task analysis) and second (scenario design) experiment, two versions of guidelines were developed: an experimental version with an explicit focus on team aspects, and a control version in which this specific focus was absent, resembling traditional guidelines. The results of the first design-experiment show that the experimental guidelines lead to a significantly better quality of the analysis process; the results of the second design-experiment show no significant effects. The purpose of the third design-experiment was to investigate the effect of a more elaborate introduction (an interactive workshop) of both sets of experimental guidelines. The results show that only on topics that were explicitly dealt with, the analysis and design process improved.     

Training in virtual environments: transfer to real world tasks and equivalence to real task training

Virtual environments (VEs) are extensively used in training but there have been few rigorous scientific investigations of whether and how skills learned in a VE are transferred to the real world. This research aimed to measure and evaluate what is transferring from training a simple sensorimotor task in a VE to real world performance. In experiment 1, real world performances after virtual training, real training and no training were compared. Virtual and real training resulted in equivalent levels of post-training performance, both of which significantly exceeded task performance without training. Experiments 2 and 3 investigated whether virtual and real trained real world performances differed in their susceptibility to cognitive and motor interfering tasks (experiment 2) and in terms of spare attentional capacity to respond to stimuli and instructions which were not directly related to the task (experiment 3). The only significant difference found was that real task performance after training in a VE was less affected by concurrently performed interference tasks than was real task performance after training on the real task. This finding is discussed in terms of the cognitive load characteristics of virtual training. Virtual training therefore resulted in equivalent or even better real world performance than real training in this simple sensorimotor task, but this finding may not apply to other training tasks. Future research should be directed towards establishing a comprehensive knowledge of what is being transferred to real world performance in other tasks currently being trained in VEs and investigating the equivalence of virtual and real trained performances in these situations.     

Training in virtual environments: transfer to real world tasks and equivalence to real task training

Virtual environments (VEs) are extensively used in training but there have been few rigorous scientific investigations of whether and how skills learned in a VE are transferred to the real world. This research aimed to measure and evaluate what is transferring from training a simple sensorimotor task in a VE to real world performance. In experiment 1, real world performances after virtual training, real training and no training were compared. Virtual and real training resulted in equivalent levels of post-training performance, both of which significantly exceeded task performance without training. Experiments 2 and 3 investigated whether virtual and real trained real world performances differed in their susceptibility to cognitive and motor interfering tasks (experiment 2) and in terms of spare attentional capacity to respond to stimuli and instructions which were not directly related to the task (experiment 3). The only significant difference found was that real task performance after training in a VE was less affected by concurrently performed interference tasks than was real task performance after training on the real task. This finding is discussed in terms of the cognitive load characteristics of virtual training. Virtual training therefore resulted in equivalent or even better real world performance than real training in this simple sensorimotor task, but this finding may not apply to other training tasks. Future research should be directed towards establishing a comprehensive knowledge of what is being transferred to real world performance in other tasks currently being trained in VEs and investigating the equivalence of virtual and real trained performances in these situations.     

Cloud-DLS: Dynamic trusted scheduling for Cloud computing

Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In this paper, inspired by Bayesian cognitive model and referring to the trust relationship models of sociology, we first propose a novel Bayesian method based cognitive trust model, and then we proposed a trust dynamic level scheduling algorithm named Cloud-DLS by integrating the existing DLS algorithm. Moreover, a benchmark is structured to span a range of Cloud computing characteristics for evaluation of the proposed method. Theoretical analysis and simulations prove that the Cloud-DLS algorithm can efficiently meet the requirement of Cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way.     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.     

基于用户自定义兴趣区的飞行员眼动数据可视分析方法

针对传统基于兴趣区的可视化方法在分析飞行员眼动数据过程中无法关注细节的问题,提出了一种基于用户自定义兴趣区的眼动数据可视分析方法。首先,根据具体的分析任务,引入对任务背景图像的自我划分和定义;然后,在此基础上,结合多种辅助视图和交互手段,设计并实现了面向飞行员培训的眼动数据可视分析系统,帮助分析人员分析不同的飞行员之间的眼动差异,最后通过案例分析,证明了可视分析方法的有效性和分析系统的实用性。实验结果表明,较传统方法来说,所提方法增加了分析人员在分析过程中的主动性,在整体和局部方面,支持分析人员对任务背景进行细节的探索,增加了分析人员分析数据的多角度性,让分析人员能够结合整体发现飞行学员在训练过程中认知困难的部分,进而制定更有针对性、更有效的训练课程。     

Development and Evaluation of a Virtual Reality Training System Based on Cognitive Task Analysis: The Case of CNC Tool Length Offsetting

This article reports on the development and evaluation of a virtual reality training system (VRTS) for a specific machining task. A cognitive task analysis of expert machinists was conducted to examine whether this can be effective in developing a VRTS concerning tool length offsetting for a machining center. This analysis provided the necessary information for development and calibration of such a system. Subsequently, the effectiveness of the VRTS was evaluated by conducting an experiment with 29 mechanical engineering students. The VRTS set‐up comprised a video projection of the machining center and a physical mock‐up of its interface. The system demonstrated positive training transfer for the toll length offsetting task in terms of task accomplishment and of time to complete the task. No positive transfer was observed in terms of task accuracy, probably due to perceptual biases induced by the detailed specification of the VRTS. The present work provides evidence that cognitive task analysis was effective in identifying a number of key skills pertaining to the tool length offsetting task and in implementing ways to facilitate training in such tasks in a virtual environment. This article also demonstrates that even for tasks that include subtle perceptual skills VRTS may be beneficial regardless of the level of physical fidelity, provided that the cognitive organization of a task is adequately mapped in the system.     

基于迭代式MapReduce的误差反向传播算法

针对误差反向传播(BP)算法计算迭代的特点,给出了迭代式MapReduce框架实现BP算法的方法。迭代式MapReduce框架在传统MapReduce框架上添加了传送模块,避免了传统框架运用在迭代程序时需要多次任务提交的缺陷。通过对K/TGR146对空台射电开关控制系统进行仿真得到BP算法训练样本,并在Hadoop云计算环境下,分别在基于传统框架和迭代式框架的BP算法中进行训练。实验结果表明,基于迭代式MapReduce框架的BP算法训练速度达到了基于传统MapReduce框架的BP算法训练速度的10倍以上,正确率提升了10%~13%,能有效解决算法训练时间过长和迭代计算中多次任务提交的问题。     

Uncovering the requirements of cognitive work

OBJECTIVE: In this article, the author provides an overview of cognitive analysis methods and how they can be used to inform system analysis and design. BACKGROUND: Human factors has seen a shift toward modeling and support of cognitively intensive work (e.g., military command and control, medical planning and decision making, supervisory control of automated systems). Cognitive task analysis and cognitive work analysis methods extend traditional task analysis techniques to uncover the knowledge and thought processes that underlie performance in cognitively complex settings. METHODS: The author reviews the multidisciplinary roots of cognitive analysis and the variety of cognitive task analysis and cognitive work analysis methods that have emerged. RESULTS: Cognitive analysis methods have been used successfully to guide system design, as well as development of function allocation, team structure, and training, so as to enhance performance and reduce the potential for error. CONCLUSIONS: A comprehensive characterization of cognitive work requires two mutually informing analyses: (a) examination of domain characteristics and constraints that define cognitive requirements and challenges and (b) examination of practitioner knowledge and strategies that underlie both expert and error-vulnerable performance. A variety of specific methods can be adapted to achieve these aims within the pragmatic constraints of particular projects. APPLICATION: Cognitive analysis methods can be used effectively to anticipate cognitive performance problems and specify ways to improve individual and team cognitive performance (be it through new forms of training, user interfaces, or decision aids).     

一种基于重复训练的支持向量机方法

针对支持向量机中存在的对噪音和野值敏感的问题,提出了一种基于重复训练的支持向量机方法。该方法选取重复训练后会对分类面有影响的样本,根据其类别隶属度,重复训练相应的次数,以此来改变样本的权值,减小噪音和野值的影响。将该算法应用于文本分类中,实验结果表明,该方法在适度增加了训练时间的情况下,不仅比标准支持向量机方法具有更好的抗噪音和野值的能力,而且提高了分类性能。     

Applied cognitive task analysis (ACTA): a practitioner's toolkit for understanding cognitive task demands

Cognitive task analysis (CTA) is a set of methods for identifying cognitive skills, or mental demands, needed to perform a task proficiently. The product of the task analysis can be used to inform the design of interfaces and training systems. However, CTA is resource intensive and has previously been of limited use to design practitioners. A streamlined method of CTA, Applied Cognitive Task Analysis (ACTA), is presented in this paper. ACTA consists of three interview methods that help the practitioner to extract information about the cognitive demands and skills required for a task. ACTA also allows the practitioner to represent this information in a format that will translate more directly into applied products, such as improved training scenarios or interface recommendations. This paper will describe the three methods, an evaluation study conducted to assess the usability and usefulness of the methods, and some directions for future research for making cognitive task analysis accessible to practitioners. ACTA techniques were found to be easy to use, flexible, and to provide clear output. The information and training materials developed based on ACTA interviews were found to be accurate and important for training purposes.     

Development and evaluation of a virtual training environment for on-line robot programming

The paper reports on the development and evaluation of a virtual reality system to support training in on-line programming of industrial robots. The system was evaluated by running training experiments with three groups of engineering students in the real, virtual and virtual augmented robot conditions. Results suggest that, the group with prior training in the virtual reality system augmented with cognitive/perceptual aids clearly outperformed the group that executed the tasks in the real robot only. The group trained in the non-augmented virtual reality system did not demonstrate the same results. It is concluded that the cognitive/perceptual aids embedded in the augmented virtual reality system had a positive effect on all task performance metrics and on the consistency of results across participants on the real robot. Virtual training environments need not be designed as close as possible to the real ones. Specifically designed augmented cognitive/perceptual aids may foster skill development that can be transferred to the real task. The suggested training environment is simple and cost effective in training of novices in an entry level task.     

Ergonomic factors on task performance in laparoscopic surgery training

This paper evaluates the effect of ergonomic factors on task performance and trainee posture during laparoscopic surgery training. Twenty subjects without laparoscopic experience were allotted into 2 groups. Group 1 was trained under the optimal ergonomic simulation setting according to current ergonomic guidelines (Condition A). Group 2 was trained under non-optimal ergonomic simulation setting that can often be observed during training in a skills lab (Condition B). Posture analysis showed that the subjects held a much more neutral posture under Condition A than under Condition B (p < 0.001). The subjects had less joint excursion and experienced less discomfort in their neck, shoulders, and arms under Condition A. Significant difference in task performance between Conditions A and B (p < 0.05) was found. This study shows that the optimal ergonomic simulation setting leads to better task performance. In addition, no significant differences of task performance, for Groups 1 and 2 using the same test setting were found. However, better performance was observed for Group 1. It can be concluded that the optimal and non-optimal training setting have different learning effects on trainees’ skill learning.     

Cognitive task analysis and interface design in a technical troubleshooting domain

A model of the interface design process is proposed that uses two interdependent levels of cognitive analysis: (a) study of the criterion task through the analysis of expert/novice differences, and (b) evaluation of the working user interface design through the application of practical interface analysis methodology (the GOMS model). This dual analysis is reviewed in the context of HYDRIVE, an intelligent tutoring system that has been designed to facilitate the development of aircraft hydraulics systems troubleshooting skills. Initial cognitive task analyses identified critical troubleshooting skills and procedures. However, even with an initial cognitive task analysis, the GOMS analysis resulted in significant and beneficial design changes.