Dimensional Approach for Using Color in Social Robot Emotion Communication

ABSTRACT Social robots are designed to communicate and interact with humans in a socio‐emotional capacity, including the expression of emotions. Among the various ways that can be employed by robots to convey emotion information, the current study investigates color as a useful tool for emotion expression in social robots. Color is a flexible, inexpensive, and practical channel to convey emotional meaning in social robots, likely due to a robust network of color‐emotion associations existing in human cognition. The current study tests how perceptions of the emotions happiness, sadness, and anger are influenced by social robots displaying a range of colored lights. The results show that color can meaningfully influence the perceptions of these robot emotions. Further, these results show that independent color dimensions (i.e., lightness, chroma, hue) impact emotion evaluation to differing extents, depending on the particular emotion. Additionally, we tested the influence of color on emotion perception for robots that varied in their anthropomorphic appearance, finding some nuanced differences in how their appearance modified their emotion expressions. We discuss the implications with respect to designing emotion‐communicative social robots, dimensional approaches to understanding color‐emotion associations more broadly, and the role of context in color meaning. The current work aims to facilitate the design of social robots and promote more meaningful human‐robot social interactions.

Authors

Institutions

Publication Details

Journal
Color Research & Application
Published
2025-03-14
DOI
https://doi.org/10.1002/col.22981
Citations
2
Primary Topic
Color perception and design
Type
article
Field-Weighted Citation Impact
4.88
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dimensional Approach for Using Color in Social Robot Emotion Communication

Christopher A. Thorstenson, Yuan Tian
2 citations
Color Research & Application
Color perception and design
4.88
article

Dimensional Approach for Using Color in Social Robot Emotion Communication

Christopher A. Thorstenson, Yuan Tian
article en
2 citations

Abstract

ABSTRACT Social robots are designed to communicate and interact with humans in a socio‐emotional capacity, including the expression of emotions. Among the various ways that can be employed by robots to convey emotion information, the current study investigates color as a useful tool for emotion expression in social robots. Color is a flexible, inexpensive, and practical channel to convey emotional meaning in social robots, likely due to a robust network of color‐emotion associations existing in human cognition. The current study tests how perceptions of the emotions happiness, sadness, and anger are influenced by social robots displaying a range of colored lights. The results show that color can meaningfully influence the perceptions of these robot emotions. Further, these results show that independent color dimensions (i.e., lightness, chroma, hue) impact emotion evaluation to differing extents, depending on the particular emotion. Additionally, we tested the influence of color on emotion perception for robots that varied in their anthropomorphic appearance, finding some nuanced differences in how their appearance modified their emotion expressions. We discuss the implications with respect to designing emotion‐communicative social robots, dimensional approaches to understanding color‐emotion associations more broadly, and the role of context in color meaning. The current work aims to facilitate the design of social robots and promote more meaningful human‐robot social interactions.

Color Research & ApplicationVol. 50(5)
Rochester Institute of Technology (US)
Openalex Percentile: Top 7%
Color perception and design
4.88
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.