Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael A. Perez
dblp:195/6917
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1
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 2 heaviest of 2, 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 |
0.3 | 1 | 2017 | The Robotic Social Attributes Scale (RoSAS): Development and Validation · HRI 2017 |
Human-robot interaction › anthropomorphism
robot appearance |
0.1 | 1 | 2017 | The Robotic Social Attributes Scale (RoSAS): Development and Validation · HRI 2017 |
Methods — techniques the papers use, named apart from their topics
scale development · 0.3factor analysis · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | The Robotic Social Attributes Scale (RoSAS): Development and ValidationabstractAccurately measuring perceptions of robots has become increasingly important as technological progress permits more frequent and extensive interaction between people and robots. Across four studies, we develop and validate a scale to measure social perception of robots. Drawing from the Godspeed Scale and from the psychological literature on social perception, we develop an 18-item scale (The Robotic Social Attribute Scale; RoSAS) to measure people's judgments of the social attributes of robots. Factor analyses reveal three underlying scale dimensions-warmth, competence, and discomfort. We then validate the RoSAS and show that the discomfort dimension does not reflect a concern with unfamiliarity. Using images of robots that systematically vary in their machineness and gender-typicality, we show that the application of these social attributes to robots varies based on their appearance. Colleen M. Carpinella, Alisa B. Wyman, Michael A. Perez, Steven J. Stroessner |
HRI | 3 |