Michael Oehl

Introducing a multivariate model for predicting driving performance: the role of driving anger and personal characteristics.

INTRODUCTION Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. METHOD We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. RESULTS In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior.

The impact of goal focus, task type and group size on synchronous net-based collaborative learning discourses

Net-based collaborative learning discourses often suffer from deficiencies such as lack of coherence and coordination. It is suggested that the provision of two functionalities, referencing and typing, which learners may optionally use to ground their contributions during a chatbased discourse, can improve collaborative learning. In particular, we examined if goal focus, typeoftaskandgroupsizeaffectlearningoutcomesandtheuseofthesefunctionalities.Achatbased system, called a learning protocol, implements these functionalities and serves as a netbased collaborative learning environment. Results suggest that a learning protocol is more beneficial for knowledge-acquisition tasks than for problem-solving tasks, and that the use of supporting functionalities increases when goal focus is on the group rather than on the individual.Also,thereisatendencythatlearningoutcomesimproveasgroupsizeincreases.We propose that learning protocols provide potentially valuable design features that can promote net-based collaborative learning.

Affective States and Driving Behavior of Novice and Young Drivers

ABSTRACT Although general car safety has increased considerably and at the same time accident numbers have decreased remarkably on average in the European Union during the last years, the percentage of novice and young car drivers involved in heavy car accidents is still remaining dramatically high, e.g., in Germany more than twice as high compared to older and more experienced drivers based on their proportion of the driving population. Traffic psychological research shows that maladjusted driving behavior caused by affective states is a main contributor to traffic accidents. Therefore, our current experimental study analyzes this influence of affective states on driving performance with regard to novice and young drivers. In an experimental scenario affective states (positive vs. negative valence) were induced in participants and subjects were then asked to drive predefined routes in a driving simulator. Results indicated that drivers drove significantly faster in a positive affective state compared with drivers in a negative affective state. This effect was pronounced by trend for novice drivers.

Discomfort in Automated Driving – The Disco-Scale

Due to the increasing amount of automation in vehicles the role of the driver changes from having an active part in the driving of the vehicle to a reactive monitoring task. Since there is currently no method to measure subjective comfort or discomfort we developed a 14-item scale to measure the discomfort of a driver. Research suggests that it is easier for users to sense the lack of comfort and because of this we used experienced discomfort as an indicator for the absence of comfort. The questionnaire was applied in an experimental driving simulator study and proved to have a high internal consistency (r = .91). Results suggest that this questionnaire is a useful tool for assessing discomfort in automated HMI. This first version is focused on, but not limited to, automation and advanced driver assistance systems in vehicles.

Emotional Human-Machine Interaction

This two-volume set LNCS 6771 and 6772 constitutes the refereed proceedings of the Symposium on Human Interface 2011, held in Orlando, FL, USA in July 2011 in the framework of the 14th International Conference on Human-Computer Interaction, HCII 2011 with 10 other thematically similar conferences. The 137 revised papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers accepted for presentation thoroughly cover the thematic area of human interface and the management of information. The 75 papers of this first volume address the following major topics: design and development methods and tools; information and user interfaces design; visualisation techniques and applications; security and privacy; touch and gesture interfaces; adaption and personalisation; and measuring and recognising human behavior.

German taxi drivers' experiences and expressions of driving anger: Are the driving anger scale and the driving anger expression inventory valid measures?

ABSTRACT Objective: The objective of this article was 2-fold: firstly, we wanted to examine whether the original Driving Anger Scale (DAS) and the original Driving Anger Expression Inventory (DAX) apply to German professional taxi drivers because these scales have previously been given to professional and particularly to nonprofessional drivers in different countries. Secondly, we wanted to examine possible differences in driving anger experience and expression between professional German taxi drivers and nonprofessional German drivers. Methods: We applied German versions of the DAS, the DAX, and the State–Trait Anger Expression Inventory (STAXI) to a sample of 138 professional German taxi drivers. We then compared their ratings to the ratings of a sample of 1,136 nonprofessional German drivers (Oehl and Brandenburg n.d.). Results: Regarding our first objective, confirmatory factor analysis shows that the model fit of the DAS is better for nonprofessional drivers than for professional drivers. The DAX applies neither to professional nor to nonprofessional German drivers properly. Consequently, we suggest modified shorter versions of both scales for professional drivers. The STAXI applies to both professional and nonprofessional drivers. With respect to our second objective, we show that professional drivers experience significantly less driving anger than nonprofessional drivers, but they express more driving anger. Conclusions: We conclude that the STAXI can be applied to professional German taxi drivers. In contrast, for the DAS and the DAX we found particular shorter versions for professional taxi drivers. Especially for the DAX, most statements were too strong for German drivers to agree to. They do not show behaviors related to driving anger expression as they are described in the DAX. These problems with the original American DAX items are in line with several other studies in different countries. Future investigations should examine whether (professional) drivers from further countries express their anger as proposed by the DAX. In addition, professional drivers experience less driving anger (DAS) and less general trait anger (STAXI) than nonprofessional drivers, but they report more driving anger expression (DAX) and more current general state anger (STAXI). Subsequent studies should therefore focus on different types of anger within the group of professional drivers.

Current and New Research Perspectives on Dynamic Facial Emotion Detection in Emotional Interface

In recent years there has been an increasing interdisciplinary exchange between psychology and computer science in the field of recognizing emotions for future-oriented Human-Computer and Human-Machine Interfaces. Although affective computing research has made enormous progress in automatically recognizing facial expressions, it has not yet been fully clarified how algorithms can learn to encode or decode a human face in a real environment. Consequently, our research focuses on the detection of emotions or affective states in a Human-Machine setting. In contrast to other approaches, we use a psychology driven approach trying to minimize complex computations by using a simple dot-based feature extraction method. We suggest a new approach within, but not limited to, a Human-Machine Interface context which detects emotions by analyzing the dynamic change in facial expressions. In order to compare our approach, we discuss our software with respect to other developed facial expression studies in context of its application in a chat environment. Our approach indicates promising results that the program could accurately detect emotions. Implications for further research as well as for applied issues in many areas of Human-Computer Interaction, particularly for affective and social computing, will be discussed and outlined.