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Daniel Rehberg

dblp:344/1740 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0002-4979-3078ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 56% Immersive interaction · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Haptics and multimodal interaction › spatial audio
spatial audio localization
0.912025
Repurposing Audio Playback Tools to Test Human Localization with 6DoF Sound · UIST 2025
Immersive interaction
virtual reality
0.912025
Repurposing Audio Playback Tools to Test Human Localization with 6DoF Sound · UIST 2025
Haptics and multimodal interaction
multimodal perception
0.312025
Repurposing Audio Playback Tools to Test Human Localization with 6DoF Sound · UIST 2025

Methods — techniques the papers use, named apart from their topics

6dof audio playback · 0.9
YearPublicationVenuePosition
2025 Repurposing Audio Playback Tools to Test Human Localization with 6DoF Sound
Daniel Rehberg, Adam S. Williams, Anil Ufuk Batmaz, Francisco R. Ortega 0001
UIST1
2023 Fast, Accurate, But Sometimes Too-Compelling Support: The Impact of Imperfectly Automated Cues in an Augmented-Reality Head-Mounted Display on Visual Search Performance
abstract
While the visual search for targets in a complex scene might benefit from using augmented-reality (AR) head-mounted display (HMD) technologies by helping to efficiently direct human attention, imperfectly reliable automation support could manifest in occasional errors. The current study examined the effectiveness of different HMD cues that might support visual search performance and their respective consequences following automation errors. A total of 56 participants searched a three-dimensional environment containing 48 objects in a room, in order to locate a target object that was viewed prior to each trial. They searched either unaided or assisted by one of the three HMD types of cues: an arrow pointing to the target, a plan-view minimap highlighting the target, and a constantly visible icon depicting the appearance of the target object. The cue was incorrect in 17% of the trials for one group of participants and 100% correct for the second group. Through both analysis and modeling of both search speed and accuracy, the results indicated that the arrow and minimap cues depicting location information were more effective than the icon cue depicting visual appearance, both overall, and when the cue was correct. However, there was a tradeoff on the infrequent occasions when the cue erred. The most effective AR-based cue led to a greater automation bias in which the cue was more often blindly followed without careful examination of the raw images. The results speak to the benefits of AR and the need to examine potential costs when AR-conveyed information may be incorrect because of imperfectly reliable systems.
Amelia C. Warden, Christopher D. Wickens, Daniel Rehberg, Francisco R. Ortega 0001, Benjamin A. Clegg
IEEE Trans. Hum. Mach. Syst.3