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Anthropomorphism and Affective Perception: Dimensions, Measurements, and Interdependencies in Aerial Robotics.

Authors :
Herdel, Viviane
Kuzminykh, Anastasia
Parmet, Yisrael
Cauchard, Jessica R.
Source :
IEEE Transactions on Affective Computing; Jul-Sep2024, Vol. 15 Issue 3, p1463-1474, 12p
Publication Year :
2024

Abstract

Assigning lifelike qualities to robotic agents (Anthropomorphism) is associated with complex affective interpretations of their behavior. These anthropomorphized perceptions are traditionally elicited through robots’ designs. Yet, aerial robots (or drones) present a special case due to their – traditionally – non-anthropomorphic design, and prior research shows conflicting evidence on their perception as either person-like, animal-like, or machine-like. In this article, we explore how people perceive drones in a cross-dimensional space between these three dimensions by varying the affective state presented on the drone. To capture these perceptions, we developed a novel measurement instrument AnZoMa. We describe the design, use, and deployment of the instrument in an online study ($N$ N =98). The study results suggest that different drone emotions triggered people to attribute various characteristics to the drone (e.g., interaction metaphors, traits, and features) and variations in acceptability of drone affective states. These results demonstrate the interdependencies between affective perceptions and anthropomorphism of drones. We conclude by discussing the necessity to integrate cross-dimensional perception of anthropomorphism in human-drone interaction and affective computing. This work contributes a novel tool to measure the dimensions and gravity of anthropomorphism and insights into interdependencies between different affective states displayed on drones and their anthropomorphized perception. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19493045
Volume :
15
Issue :
3
Database :
Complementary Index
Journal :
IEEE Transactions on Affective Computing
Publication Type :
Academic Journal
Accession number :
179509543
Full Text :
https://doi.org/10.1109/TAFFC.2024.3349858