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    <title language="eng">Cognitive Biases in User Interaction with Automated Vehicles: The Influence of Explainability and Mental Models</title>
    <abstract language="eng">To develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings.</abstract>
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    <title language="eng">The impact of explainability &amp; mental modeling on human flawed thinking in automated driving</title>
    <abstract language="eng">If we, as user experience designers, want to create truly user-centered experiences, we need to understand not only what people want, but what they need and how they behave. Human behavior, however, also results from the choices people make every day. The same applies to the development and design of automated transportation. To achieve and maintain these goals, we need to accept that human decisions are not only made on a rational level, but rather on the basis of mental shortcuts, which may be efficient, but can also lead to cognitive biases and thus to sub-optimal results.&#13;
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This master’s thesis specifically sheds light on four cognitive biases - truthiness, illusion of control, action bias and automation bias -, their existence in automated driving, as well as their relationship to each other and possible influencing factors. As part of a preliminary study (n=117), which was implemented as an online questionnaire, the existence of the cognitive biases in focus of this work is examined in more detail and initial findings of visualizations and explanations as an influencing factor are collected. In the further course, a user study (n=34) was completed to build on the knowledge gained and examine the accuracy of the mental model of the occupants of an automated ride and their individual personality traits as further influential parameters. This work should serve as a cornerstone to include cognitive biases and their influencing forces in the development of automated vehicles in the future and to use the knowledge gathered to integrate psychological aspects that affect every human being into user-centered humanmachine interactions.</abstract>
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