Cross-cultural differences in recognizing affect from body posture

Files

IwC06.pdf (485.36 KB)

Links to Files

https://www.sciencedirect.com/science/article/abs/pii/S0953543806000634

Permanent Link

https://doi.org/10.1016/j.intcom.2006.04.003
 http://hdl.handle.net/11603/29982

Collections

UMBC Information Systems Department

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-1007-2553

Date

2006-06-27

Type of Work

journal articles
Text

Department

Program

Citation of Original Publication

Kleinsmith, Andrea, P. Ravindra De Silva, and Nadia Bianchi-Berthouze. “Cross-Cultural Differences in Recognizing Affect from Body Posture.” Interacting with Computers, Special Issue: Symbiotic Performance between Humans and Intelligent Systems, 18, no. 6 (December 1, 2006): 1371–89. https://doi.org/10.1016/j.intcom.2006.04.003.

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Abstract

Conveyance and recognition of human emotion and affective expression is influenced by many factors, including culture. Within the user modeling field, it has become increasingly necessary to understand the role affect can play in personalizing interactive interfaces using embodied animated agents. However, little research within the computer science field aims at understanding cultural differences within this vein. Therefore, we conducted a study to evaluate if differences exist in the way various cultures perceive emotion from body posture. We used static posture images of affectively expressive avatars to conduct recognition experiments with subjects from three cultures. After analyzing the subjects' judgments using multivariate analysis, we grounded the identified differences into a set of low-level posture features. We then used Mixture Discriminant Analysis (MDA) and an unsupervised expectation maximization (EM) model to build separate cultural models for affective posture recognition. Our results could prove useful to aide designers in creating more effective affective avatars.