@phdthesis{Grundke2023,
  author    = {Grundke, Andrea},
  title     = {Head and Heart: On the Acceptability of Sophisticated Robots Based on an Enhancement of the Mind Perception Dichotomy and the Uncanny Valley of Mind},
  doi       = {10.25972/OPUS-33015},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-330152},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {With the continuous development of artificial intelligence, there is an effort to let the expressed mind of robots resemble more and more human-like minds. However, just as the human-like appearance of robots can lead to feelings of aversion to such robots, recent research has shown that the apparent mind expressed by machines can also be responsible for their negative evaluations. This work strives to explore facets of aversion evoked by machines with human-like mind (uncanny valley of mind) within three empirical projects from a psychological point of view in different contexts, including the resulting consequences. In Manuscript \#1, the perspective of previous work in the research area is reversed and thus shows that humans feel eeriness in response to robots that can read human minds, a capability unknown from human-human interaction. In Manuscript \#2, it is explored whether empathy for a robot being harmed by a human is a way to alleviate the uncanny valley of mind. A result of this work worth highlighting is that aversion in this study did not arise from the manipulation of the robot's mental capabilities but from its attributed incompetence and failure. The results of Manuscript \#3 highlight that status threat is revealed if humans perform worse than machines in a work-relevant task requiring human-like mental capabilities, while higher status threat is linked with a higher willingness to interact, due to the machine's perceived usefulness. In sum, if explanatory variables and concrete scenarios are considered, people will react fairly positively to machines with human-like mental capabilities. As long as the machine's usefulness is palpable to people, but machines are not fully autonomous, people seem willing to interact with them, accepting aversion in favor of the expected benefits.},
  subject      = {Humanoider Roboter},
  language  = {en}
}
@phdthesis{Wandtner2018,
  author    = {Wandtner, Bernhard},
  title     = {Non-driving related tasks in highly automated driving - Effects of task characteristics and drivers' self-regulation on take-over performance},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173956},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {The rise of automated driving will fundamentally change our mobility in the near future. This thesis specifically considers the stage of so called highly automated driving (Level 3, SAE International, 2014). At this level, a system carries out vehicle guidance in specific application areas, e.g. on highway roads. The driver can temporarily suspend from monitoring the driving task and might use the time by engaging in so called non-driving related tasks (NDR-tasks). However, the driver is still in charge to resume vehicle control when prompted by the system. This new role of the driver has to be critically examined from a human factors perspective. The main aim of this thesis was to systematically investigate the impact of different NDR-tasks on driver behavior and take-over performance. Wickens' (2008) architecture of multiple resource theory was chosen as theoretical framework, with the building blocks of multiplicity (task interference due to resource overlap), mental workload (task demands), and aspects of executive control or self-regulation. Specific adaptations and extensions of the theory were discussed to account for the context of NDR-task interactions in highly automated driving. Overall four driving simulator studies were carried out to investigate the role of these theoretical components. Study 1 showed that drivers focused NDR-task engagement on sections of highly automated compared to manual driving. In addition, drivers avoided task engagement prior to predictable take-over situations. These results indicate that self-regulatory behavior, as reported for manual driving, also takes place in the context of highly automated driving. Study 2 specifically addressed the impact of NDR-tasks' stimulus and response modalities on take-over performance. Results showed that particularly visual-manual tasks with high motoric load (including the need to get rid of a handheld object) had detrimental effects. However, drivers seemed to be aware of task specific distraction in take-over situations and strictly canceled visual-manual tasks compared to a low impairing auditory-vocal task. Study 3 revealed that also the mental demand of NDR-tasks should be considered for drivers' take-over performance. Finally, different human-machine-interfaces were developed and evaluated in Simulator Study 4. Concepts including an explicit pre-alert ("notification") clearly supported drivers' self-regulation and achieved high usability and acceptance ratings. Overall, this thesis indicates that the architecture of multiple resource theory provides a useful framework for research in this field. Practical implications arise regarding the potential legal regulation of NDR-tasks as well as the design of elaborated human-machine-interfaces.},
  subject      = {Autonomes Fahrzeug},
  language  = {en}
}