Peter A. Wieringa

Exploratory Factor Analysis With Small Sample Sizes

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to which a small N solution can sustain the presence of small distortions such as interfactor correlations, model error, secondary loadings, unequal loadings, and unequal p/f. Factor recovery was assessed in terms of pattern congruence coefficients, factor score correlations, Heywood cases, and the gap size between eigenvalues. A subsampling study was also conducted on a psychological dataset of individuals who filled in a Big Five Inventory via the Internet. Results showed that when data are well conditioned (i.e., high λ, low f, high p), EFA can yield reliable results for N well below 50, even in the presence of small distortions. Such conditions may be uncommon but should certainly not be ruled out in behavioral research data. * These authors contributed equally to this work

A Quantitative Measure for Degree of Automation and Its Relation to System Performance and Mental Load

In this paper we quantitatively model degree of automation (DofA) in supervisory control as a function of the number and nature of tasks to be performed by the operator and automation. This model uses a task weighting scheme in which weighting factors are obtained from task demand load, task mental load and using an experimental system. Based on controlled experiments using operators, analyses of the task effect on system performance, the prediction and assessment of task demand load, and the prediction of mental load were performed. Each experiment had a different DofA. The effect of a change in DofA on system performance and mental load was investigated. It was found that system performance became less sensitive to changes in DofA at higher levels of DofA. The experimental data showed that when the operator controlled a partly automated system, perceived mental load could be predicted from the task mental load for each task component, as calculated by analyzing a situation in which all tasks were manually controlled. Actual or potential applications of this research include a methodology to balance and optimize the automation of complex industrial systems.

Violations and errors during simulation-based driver training

The effectiveness of virtual driving instruction can increase when techniques that automatically distinguish between violations and errors are available, two behaviours requiring different types of remediation. This study reports the analysis of the objectively measured performance of 520 participants completing a simulation-based training programme. Factor analysis of failure reasons showed that violations and errors were the primary underlying factors. Men committed more violations and women made more errors; the magnitude of sex differences corresponded to the factor loadings. Factor analysis of the mean task completion times yielded a factor that can be described as the extent to which motivation for speed resulted in quicker task execution. Quicker participants completed more tasks, committed more violations, but made fewer errors. Participants reduced errors during forced-paced driving and increased speed during self-paced driving. The authors would recommend exploiting the distinction between violations and errors by developing interfaces and feedback for both types of aberration.

The Effect of Concurrent Bandwidth Feedback on Learning the Lane-Keeping Task in a Driving Simulator

Objective: The aim of this study was to investigate whether concurrent bandwidth feedback improves learning of the lane-keeping task in a driving simulator. Background: Previous research suggests that bandwidth feedback improves learning and that off-target feedback is superior to on-target feedback. This study aimed to extend these findings for the lane-keeping task. Method: Participants without a driver’s license drove five 8-min lane-keeping sessions in a driver training simulator: three practice sessions, an immediate retention session, and a delayed retention session 1 day later. There were four experimental groups (n = 15 per group): (a) on-target, receiving seat vibrations when the center of the car was within 0.5 m of the lane center; (b) off-target, receiving seat vibrations when the center of the car was more than 0.5 m away from the lane center; (c) control, receiving no vibrations; and (d) realistic, receiving seat vibrations depending on engine speed. During retention, all groups were provided with the realistic vibrations. Results: During practice, on-target and off-target groups had better lane-keeping performance than the nonaugmented groups, but this difference diminished in the retention phase. Furthermore, during late practice and retention, the off-target group outperformed the on-target group. The off-target group had a higher rate of steering reversal and higher steering entropy than the nonaugmented groups, whereas no clear group differences were found regarding mean speed, mental workload, or self-reported measures. Conclusion: Off-target feedback is superior to on-target feedback for learning the lane-keeping task. Application: This research provides knowledge to researchers and designers of training systems about the value of feedback in simulator-based training of vehicular control.

Theory of Safety-Related Violations of System Barriers

Abstract:This paper focuses on a theory of the safety-related violations that occur in practice during normal operational conditions, but that are not taken into account during risk analysis. The safety-related violations are so-called barrier crossings. A barrier crossing is associated to an operational risk which constitutes a combination of costs: the cost of crossing the barrier, the benefit (negative cost) immediately after crossing the barrier, and a possible deficit (extreme cost) due to the exposure to hazardous conditions that are created after the barrier has been crossed. A utility function is discussed which describes the consequence-driven behaviour and uses an assessment of these costs functions. An industrial case study illustrates the application of the proposed theory.

Understanding perceived complexity in human supervisory control

Abstract: Industrial processes are becoming more complex owing to technological developments and new opportunities. Technological developments, hardware and software, have become more reliable and system configurations more robust. However, the reliability of operator control actions has not improved at the same pace. Consequently human reliability has become the relatively weakest aspect of automated, operator-supervised systems. Hence, understanding how the human operator experiences increasing complexity may play an important role in task allocation and human–machine system design. In this paper the perceived complexity is studied within four typical operational environments in supervisory control. Mathematical formulations for these four operational environments are proposed, and their properties are analysed. A laboratory system is used to investigate the perceived complexity under various operational environments. The experimental results show a significantly different perceived complexity for the coupled and uncoupled operation environments. Extrapolation of the results revealed that the operator would have perceived the system as extremely complex if he/she would have to operate more than eight strongly interconnected subsystems extensively in 30 minutes. Implications of this study are also addressed.

A Two-Dimensional Weighting Function for a Driver Assistance System

Driver assistance systems that supply force feedback (FF) on the accelerator commonly use relative distance and velocity with respect to the closest lead vehicle in front of the own vehicle. This 1-D feedback might not accurately represent the situation and can cause unwanted step-shaped changes in the FFs during lateral maneuvers. To address these shortcomings, a 2-D system is proposed that calculates FF using a weighted average of the influences of lead vehicles. Offline simulations and an experiment in a driving simulator were performed to compare no feedback, 1-D systems, and the novel 2-D system during a car-following task with cut-in maneuvers. Results show that the 2-D feedback resulted in lower mean forces, lower response times to cut-in vehicles, and favorable subjective experiences as compared to the 1-D systems.

Reasoning with multilevel flow models

Abstract Complex heterogeneous systems, such as power plants or petro-chemical process plants, nowadays contain complex automation for start-up and shut-down control and support systems for the operators. Often, however, the operator support and automation suffers from a lack of flexibility, and only functions for a number of well defined operating modes and pre-defined paths for the transition between these modes. This paper proposes an alternative and more flexible method for developing and describing intentional mode transitions, and for developing diagnostic systems, using Multilevel Flow Modeling (MFM). MFM models a system by expressing it in terms of its goals and in terms of elementary functions that describe the mass, energy and information flows in the system. This paper describes the use of MFM models as a basis for reasoning about the actions that are necessary to achieve the goals of a system or to obtain an intentional change in the systems mode. For this, data measured from the system must be used to update the state of the MFM model so that the state of the model reflects the state of the system. The outcome of the reasoning can be used as support for an operator or for automated control of complex systems. This paper defines the relevant states for goals and flow functions and presents a set of rules for determining these states on the basis of measurements from a process. The relations between goals and functions, and among functions themselves, are discussed. A mechanism is introduced to produce a change in the desired mode of a process, and expressed in rules to implement this change. The approach is explained at the hand of a simple example system. An MFM model of this example system is presented, and used to illustrate how measured variables can be used to calculate the states of the elements in the MFM model. At the hand of the same model the rules for inferring the states of goals and functions, and for determining the required actions will be illustrated.

Friction manipulation for intestinal locomotion

The importance of colonoscopies on the one hand and the drawbacks of the conventional instrumentation on the other have led to research into alternative colonoscopic devices. The main question for the development of such devices is the method of locomotion along the slippery and flaccid intestinal tube. This paper suggests a new intestinal locomotion method based on friction manipulation between the device and the intestinal wall. A new concept to manipulate friction is described. The concept is based on the active alterations of the friction between the device and the intestinal wall and it is inspired by the locomotion of biological species such as snails and starfish.

Nonvestibular motion cueing in a fixed-base driving simulator: Effects on driver braking and cornering performance

Motion platforms can be used to provide vestibular cues in a driving simulator, and have been shown to reduce driving speed and acceleration. However, motion platforms are expensive devices, and alternatives for providing motion cues need to be investigated. In independent experiments, the following eight low-cost nonvestibular motion cueing systems were tested by comparing driver performance to control groups driving with the cueing system disengaged: (1) seat belt tensioning system, (2) vibrating steering wheel, (3) motion seat, (4) screeching tire sound, (5) beeping sound, (6) road noise, (7) vibrating seat, and (8) pressure seat. The results showed that these systems are beneficial in reducing speed and acceleration and that they improve lane-keeping and/or stopping accuracy. The seat belt tensioning system had a particularly large influence on driver braking performance. This system reduced driving speed, increased stopping distance, reduced maximum deceleration, and increased stopping accuracy. It is concluded that low-cost nonvestibular motion cueing may be a welcome alternative for improving in-simulator performance so that it better matches real-world driving performance.