Ashley Perez

dblp:316/5718 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › assistive robotics
socially assistive robotics
0.612022
Perceptions of Cognitive and Affective Empathetic Statements by Socially Assistive Robots · HRI 2022
Human-robot interaction
attitudes toward robots
0.212022
Perceptions of Cognitive and Affective Empathetic Statements by Socially Assistive Robots · HRI 2022

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

within-subjects study · 0.6RoPE scale · 0.6
YearPublicationVenuePosition
2022 Perceptions of Cognitive and Affective Empathetic Statements by Socially Assistive Robots
abstract
Communicating empathy is important for building relationships in numerous contexts. Consequently, the failure of robots to be perceived as empathetic by human users could be detrimental to developing effective human-robot interaction. Work on computational models of empathy has been growing rapidly, reflecting the importance of this ability for machines. Despite growing recent work, there remain unanswered questions about how users perceive different forms of empathetic expression by robots and how attitudes towards robots may mediate perceptions of robot empathy. Do people really believe that robots can feel or understand emotions? This work studied the difference in viewers' perceptions of cognitive and affective empathetic statements made by a robot in response to hu-man disclosure. In a within-subjects study, participants (n=111) watched videos in which a human disclosed negative emotions around COVID-19, and a robot responded with either affective or cognitive empathetic responses. Using an adapted version of the Robot's Perceived Empathy (RoPE) scale, participants rated their perceptions of the robot's empathy in both cases. We found that participants perceived the robot that made affective empathetic statements as being more empathetic that the robot that made cognitive empathetic statements; we also found that participants with more negative attitudes toward robots were more likely to rate the cognitive condition as more empathetic than the affective condition. These results inform HRI in general and future work into developing robots that will be perceived as empathetic and could personalize empathetic responses to each user.
Christopher Birmingham, Ashley Perez, Maja J. Mataric
HRI2