Demo Data from EDBT 2026 / reviewers in the wild / expert
Defne Siir Faydali
dblp:404/5516
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 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 |
Human-robot interaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › human behavior modeling › social perception
social perception of robots |
0.9 | 1 | 2025 | Distraction by a Human or a Robot: Effects of Perceptual Load and Action Type · HRI 2025 |
Human-robot interaction
communicative action |
0.3 | 1 | 2025 | Distraction by a Human or a Robot: Effects of Perceptual Load and Action Type · HRI 2025 |
Human-robot interaction
robot communication |
0.3 | 1 | 2025 | Distraction by a Human or a Robot: Effects of Perceptual Load and Action Type · HRI 2025 |
Methods — techniques the papers use, named apart from their topics
perceptual load paradigm · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Distraction by a Human or a Robot: Effects of Perceptual Load and Action TypeabstractThis study investigates how humans process and attend to robot actions compared to human actions under varying cognitive demands. Using a perceptual load paradigm, we examined whether robots capture attention similarly to humans and how this is influenced by the nature of their actions. Participants performed a letter detection task while being presented with task-irrelevant videos of either human or robot agents performing communicative or noncommunicative actions. Results demonstrated that both humans and robots captured attention through their actions, particularly when these actions were communicative. Under high perceptual load conditions, human distractors caused more interference than robot distractors, suggesting that agent identity becomes particularly important when cognitive resources are limited. These findings provide insights into how humans process robot actions in attentiondemanding situations and may have important implications for designing robot behaviors in operational contexts. Ataol Burak Özsu, Tugçe Nur Pekçetin, Defne Siir Faydali, Burcu A. Urgen |
HRI | 3 |