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Mieke Boon

dblp:289/4659 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0003-2492-2854ORCID · reported

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 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
humanlikeness
0.512021
Uncanny, Sexy, and Threatening Robots: The Online Community's Attitude to and Perceptions of Robots Varying in Humanlikeness and Gender · HRI 2021
Human-robot interaction › anthropomorphism
robot appearance
0.512021
Uncanny, Sexy, and Threatening Robots: The Online Community's Attitude to and Perceptions of Robots Varying in Humanlikeness and Gender · HRI 2021
Human-robot interaction › anthropomorphism
uncanny valley
0.512021
Uncanny, Sexy, and Threatening Robots: The Online Community's Attitude to and Perceptions of Robots Varying in Humanlikeness and Gender · HRI 2021

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

content analysis · 0.5
YearPublicationVenuePosition
2021 Uncanny, Sexy, and Threatening Robots: The Online Community's Attitude to and Perceptions of Robots Varying in Humanlikeness and Gender
abstract
To get a better understanding of people's natural responses to humanlike robots outside the lab, we analyzed commentary on online videos depicting robots of different humanlikeness and gender. We built on previous work, which compared online video commentary of moderately and highly humanlike robots with respect to valence, uncanny valley, threats, and objectification. Additionally, we took into account the robot's gender, its appearance, its societal impact, the attribution of mental states, and how people attribute human stereotypes to robots. The results are mostly in line with previous work. Overall, the findings indicate that moderately humanlike robot design may be preferable over highly humanlike robot design because it is less associated with negative attitudes and perceptions. Robot designers should therefore be cautious when designing highly humanlike and gendered robots.
Quirien R. M. Hover, Ella Velner, Thomas Beelen, Mieke Boon, Khiet P. Truong
HRI4