@article{63611,
  abstract     = {{When humans interact with artificial intelligence (AI), one desideratum is appropriate trust. Typically, appropriate trust encompasses that humans trust AI except for instances in which they either explicitly notice AI errors or are suspicious that errors could be present. So far, appropriate trust or related notions have mainly been investigated by assessing trust and reliance. In this contribution, we argue that these assessments are insufficient to measure the complex aim of appropriate trust and the related notion of healthy distrust. We introduce and test the perspective of covert visual attention as an additional indicator for appropriate trust and draw conceptual connections to the notion of healthy distrust. To test the validity of our conceptualization, we formalize visual attention using the Theory of Visual Attention and measure its properties that are potentially relevant to appropriate trust and healthy distrust in an image classification task. Based on temporal-order judgment performance, we estimate participants' attentional capacity and attentional weight toward correct and incorrect mock-up AI classifications. We observe that misclassifications reduce attentional capacity compared to correct classifications. However, our results do not indicate that this reduction is beneficial for a subsequent judgment of the classifications. The attentional weighting is not affected by the classifications' correctness but by the difficulty of categorizing the stimuli themselves. We discuss these results, their implications, and the limited potential for using visual attention as an indicator of appropriate trust and healthy distrust.}},
  author       = {{Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}},
  issn         = {{1664-1078}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{appropriate trust, healthy distrust, visual attention, Theory of Visual Attention, human-AI interaction, Bayesian cognitive model, image classification}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention}}},
  doi          = {{10.3389/fpsyg.2025.1694367}},
  volume       = {{16}},
  year         = {{2026}},
}

@article{59755,
  abstract     = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine,
trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception,
for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated
classification as advice. Research to improve such interactions often aims to foster the
user’s trust, which in turn should improve the combined human-AI performance. Given that AI
models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is
an equally interesting target of research.
We created two image classification scenarios in which the participants received mock-up
AI advice. The quality of the advice decreases for a phase of the experiment. We studied the
task performance, trust and distrust of the participants, and tested whether an instruction to
remain skeptical and review each piece of advice led to a better performance compared to a
neutral condition. Our results indicate that this instruction does not improve but rather worsens
the participants’ performance. Repeated single-item self-report of trust and distrust shows an
increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no
difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory
analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether
the problems of under- and over-reliance have been mitigated. We discuss implications of our
results for the usage of disclaimers before interacting with AI, as prominently used in current
LLM-based chatbots, and for trust and distrust research.}},
  author       = {{Peters, Tobias Martin and Scharlau, Ingrid}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}},
  title        = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}},
  doi          = {{10.3389/fpsyg.2025.1574809}},
  volume       = {{16}},
  year         = {{2025}},
}

@misc{55196,
  author       = {{Jacke, Christoph}},
  booktitle    = {{Musiktheorie. Zeitschrift für Musikwissenschaft.}},
  issn         = {{0177-4182}},
  keywords     = {{Popmusik, Gender, Schlager, Frauen, Image, Deutschland}},
  number       = {{2}},
  pages        = {{181--185}},
  publisher    = {{Laaber}},
  title        = {{{Marina Forell (2023): Atemlos zum Erfolg. Gender, Frauenbild und Entwicklungstendenzen im deutschen Schlager.}}},
  volume       = {{39}},
  year         = {{2024}},
}

@inproceedings{22481,
  abstract     = {{During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.}},
  author       = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold}},
  booktitle    = {{22nd IEEE International Conference on Industrial Technology (ICIT)}},
  isbn         = {{9781728157306}},
  keywords     = {{Image Processing, Defect Detection, wooden surfaces, Machine Learning, Neural Networks}},
  location     = {{Valencia, Spain }},
  publisher    = {{IEEE}},
  title        = {{{Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction}}},
  doi          = {{10.1109/icit46573.2021.9453646}},
  year         = {{2021}},
}

@inproceedings{10780,
  author       = {{Guettatfi, Zakarya and Hübner, Philipp and Platzner, Marco and Rinner, Bernhard}},
  booktitle    = {{12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC)}},
  keywords     = {{embedded systems, image sensors, power aware computing, wireless sensor networks, Zynq-based VSN node prototype, computational self-awareness, design approach, platform levels, power consumption, visual sensor networks, visual sensor nodes, Cameras, Hardware, Middleware, Multicore processing, Operating systems, Runtime, Reconfigurable platforms, distributed embedded systems, performance-resource trade-off, self-awareness, visual sensor nodes}},
  pages        = {{1--8}},
  title        = {{{Computational self-awareness as design approach for visual sensor nodes}}},
  doi          = {{10.1109/ReCoSoC.2017.8016147}},
  year         = {{2017}},
}

@article{11892,
  abstract     = {{For an environment to be perceived as being smart, contextual information has to be gathered to adapt the system's behavior and its interface towards the user. Being a rich source of context information speech can be acquired unobtrusively by microphone arrays and then processed to extract information about the user and his environment. In this paper, a system for joint temporal segmentation, speaker localization, and identification is presented, which is supported by face identification from video data obtained from a steerable camera. Special attention is paid to latency aspects and online processing capabilities, as they are important for the application under investigation, namely ambient communication. It describes the vision of terminal-less, session-less and multi-modal telecommunication with remote partners, where the user can move freely within his home while the communication follows him. The speaker diarization serves as a context source, which has been integrated in a service-oriented middleware architecture and provided to the application to select the most appropriate I/O device and to steer the camera towards the speaker during ambient communication.}},
  author       = {{Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Journal of Selected Topics in Signal Processing}},
  keywords     = {{audio streaming, audio visual data streaming, context information speech, face identification, face recognition, image segmentation, middleware, multimodal telecommunication, online diarization, service oriented middleware architecture, sessionless telecommunication, software architecture, speaker identification, speaker localization, speaker recognition, steerable camera, telecommunication computing, temporal segmentation, terminal-less telecommunication, video streaming}},
  number       = {{5}},
  pages        = {{845--856}},
  title        = {{{Online Diarization of Streaming Audio-Visual Data for Smart Environments}}},
  doi          = {{10.1109/JSTSP.2010.2050519}},
  volume       = {{4}},
  year         = {{2010}},
}

@article{6094,
  abstract     = {{In the current study, we tested whether search for a visual motion singleton presented among several coherently moving distractors can be more efficient than search for a motion stimulus presented with a single distractor. Under a variety of conditions, multiple spatially distributed and coherently moving distractors facilitated search for a uniquely moving target relative to a single-motion-distractor condition (Experiments 1,3, and 4). Color coherencies among static distractors were not equally effective (Experiments 1 and 2). These results confirm that humans are highly sensitive to antagonistically directed motion signals in backgrounds compared with spatially more confined regions of visual images. (PsycINFO Database Record (c) 2016 APA, all rights reserved)}},
  author       = {{Ansorge, Ulrich and Scharlau, Ingrid and Labudda, Kirsten}},
  issn         = {{0340-0727}},
  journal      = {{Psychological Research}},
  keywords     = {{visual search, motion singleton, visual images, visual motion, coherently moving distractors, Adult, Attention, Exploratory Behavior, Female, Humans, Male, Motion Perception, Visual Perception, Motion Perception, Stimulus Salience, Visual Search, Distraction, Retinal Image}},
  number       = {{2}},
  pages        = {{103 -- 116}},
  title        = {{{Visual search for a motion singleton among coherently moving distractors.}}},
  volume       = {{70}},
  year         = {{2006}},
}