Observer and stimulus factors jointly shape perceptual similarity of static and dynamic facial emotions

Facial emotion perception is influenced by many factors (e.g. context, conceptual knowledge, facial motion), but how these factors jointly shape emotion perception remains unclear. The present study investigated how observer- and stimulus-based information interact to influence the perceived similarity of facial emotions. Participants categorised the emotion (Experiment 1) or assessed the intensity (Experiment 2) of two static or dynamic facial expressions before rating their similarity. The results showed that perceived similarity between facial expressions could be predicted by representational distances computed using stimulus properties, recognised emotions, or perceived intensity. Combining all three factors produced the best prediction performance, though the contribution of perceived intensity was more consistent for dynamic than static facial emotions. Moreover, the perception of static and dynamic facial emotions showed remarkable similarities and critical differences. All measures of similarity between facial expressions were highly correlated across dynamic and static conditions. However, dynamic facial emotions consistently elicited better categorisation performance and lower perceived intensity compared to static facial emotions. These results demonstrate novel differences in the processing of static and dynamic facial emotions and provide new insights into how perceived emotion and intensity join with stimulus properties to shape our perception of facial emotion.

Authors

Institutions

Publication Details

Journal
Cognition & Emotion
Published
2025-10-28
DOI
https://doi.org/10.1080/02699931.2025.2578697
Primary Topic
Face Recognition and Perception
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Observer and stimulus factors jointly shape perceptual similarity of static and dynamic facial emotions

Stéphanie Rossit, Andrew P. Bayliss, Guomei Zhou, Laura Manno et al.
Cognition & Emotion
Face Recognition and Perception
article

Observer and stimulus factors jointly shape perceptual similarity of static and dynamic facial emotions

Stéphanie Rossit, Andrew P. Bayliss, Guomei Zhou, Laura Manno, Mintao Zhao, Louise Ewing
article en

Abstract

Facial emotion perception is influenced by many factors (e.g. context, conceptual knowledge, facial motion), but how these factors jointly shape emotion perception remains unclear. The present study investigated how observer- and stimulus-based information interact to influence the perceived similarity of facial emotions. Participants categorised the emotion (Experiment 1) or assessed the intensity (Experiment 2) of two static or dynamic facial expressions before rating their similarity. The results showed that perceived similarity between facial expressions could be predicted by representational distances computed using stimulus properties, recognised emotions, or perceived intensity. Combining all three factors produced the best prediction performance, though the contribution of perceived intensity was more consistent for dynamic than static facial emotions. Moreover, the perception of static and dynamic facial emotions showed remarkable similarities and critical differences. All measures of similarity between facial expressions were highly correlated across dynamic and static conditions. However, dynamic facial emotions consistently elicited better categorisation performance and lower perceived intensity compared to static facial emotions. These results demonstrate novel differences in the processing of static and dynamic facial emotions and provide new insights into how perceived emotion and intensity join with stimulus properties to shape our perception of facial emotion.

Cognition & Emotion
University of East Anglia (GB), Sun Yat-sen University (CN), Norwich Research Park (GB)
Leverhulme Trust
Openalex Percentile: Top 69%
Face Recognition and Perception
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.