Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jason Kim 0003

dblp:38/6855-3 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2015
—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 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › anthropomorphism
intentionality attribution
0.112015
Evidence that Robots Trigger a Cheating Detector in Humans · HRI 2015

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

statistical analysis · 0.2between-participant user study · 0.2
YearPublicationVenuePosition
2015 Evidence that Robots Trigger a Cheating Detector in Humans
abstract
Short et al. found that in a game between a human participant and a humanoid robot, the participant will perceive the robot as being more agentic and as having more intentionality if it cheats than if it plays without cheating. However, in that design, the robot that actively cheated also generated more motion than the other conditions. In this paper, we investigate whether the additional movement of the cheating gesture is responsible for the increased agency and intentionality or whether the act of cheating itself triggers this response. In a between-participant design with 83 participants, we disambiguate between these causes by testing (1) the cases of the robot cheating to win, (2) cheating to lose, (3) cheating to tie from a winning position, and (4) cheating to tie from a losing position. Despite the fact that the robot changes its gesture to cheat in all four conditions, we find that participants are more likely to report the gesture change when the robot cheated to win from a losing position, compared with the other conditions. Participants in that same condition are also far more likely to protest in the form of an utterance following the cheat and report that the robot is less fair and honest. It is therefore the adversarial cheat itself that causes the effect and not the change in gesture, providing evidence for a cheating detector that can be triggered by robots.
Alexandru Litoiu, Daniel Ullman 0002, Jason Kim 0003, Brian Scassellati
HRI3