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Mimicry and expressiveness of an ECA in human-agent interaction: familiarity breeds content!

Overview of attention for article published in Computational Cognitive Science, June 2016
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Title
Mimicry and expressiveness of an ECA in human-agent interaction: familiarity breeds content!
Published in
Computational Cognitive Science, June 2016
DOI 10.1186/s40469-016-0008-2
Pubmed ID
Authors

Catherine J. Stevens, Bronwyn Pinchbeck, Trent Lewis, Martin Luerssen, Darius Pfitzner, David M. W. Powers, Arman Abrahamyan, Yvonne Leung, Guillaume Gibert

Abstract

Two experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA). In Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA's vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated. In Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement. Influences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Student > Master 9 21%
Researcher 6 14%
Student > Bachelor 4 9%
Professor > Associate Professor 4 9%
Other 5 12%
Unknown 4 9%
Readers by discipline Count As %
Computer Science 14 33%
Psychology 9 21%
Engineering 5 12%
Linguistics 1 2%
Economics, Econometrics and Finance 1 2%
Other 4 9%
Unknown 9 21%