Demo Data from SIGIR 2026 / reviewers in the wild / expert
Z. A. D. Dwi Pramono
dblp:83/3704
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › emotion
emotional response |
0.1 | 1 | 2008 | Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings · HRI 2008 |
Human-robot interaction › anthropomorphism
uncanny valley |
0.1 | 1 | 2008 | Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings · HRI 2008 |
Human-robot interaction
humanlikeness |
0.0 | 1 | 2008 | Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings · HRI 2008 |
Human-robot interaction › anthropomorphism
robot appearance |
0.0 | 1 | 2008 | Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings · HRI 2008 |
Methods — techniques the papers use, named apart from their topics
multidimensional scaling · 0.1isomap · 0.1generalized linear model · 0.1factor analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratingsabstractThe eerie feeling attributed to human-looking robots and animated characters may be a key factor in our perceptual and cognitive discrimination of the human and humanlike. This study applies regression, the generalized linear model (GLM), factor analysis, multidimensional scaling (MDS), and kernel isometric mapping (Isomap) to analyze ratings of 27 emotions of 18 moving figures whose appearance varies along a human likeness continuum. The results indicate (1) Attributions of eerie and creepy better capture our visceral reaction to an uncanny robot than strange. (2) Eerie and creepy are mainly associated with fear but also shocked, disgusted, and nervous. Strange is less strongly associated with emotion. (3) Thus, strange may be more cognitive, while eerie and creepy are more perceptual/emotional. (4) Human features increase ratings of human likeness. (5) Women are slightly more sensitive to eerie and creepy than men; and older people may be more willing to attribute human likeness to a robot despite its eeriness. Chin-Chang Ho, Karl F. MacDorman, Z. A. D. Dwi Pramono |
HRI | 3 |