TY - JOUR
T1 - EmoCodes
T2 - a Standardized Coding System for Socio-emotional Content in Complex Video Stimuli
AU - Camacho, M. Catalina
AU - Williams, Elizabeth M.
AU - Balser, Dori
AU - Kamojjala, Ruchika
AU - Sekar, Nikhil
AU - Steinberger, David
AU - Yarlagadda, Sishir
AU - Perlman, Susan B.
AU - Barch, Deanna M.
N1 - Publisher Copyright:
© 2022, The Society for Affective Science.
PY - 2022/3
Y1 - 2022/3
N2 - Social information processing is vital for inferring emotional states in others, yet affective neuroscience has only begun to scratch the surface of how we represent emotional information in the brain. Most previous affective neuroscience work has used isolated stimuli such as static images of affective faces or scenes to probe affective processing. While this work has provided rich insight to the initial stages of emotion processing (encoding cues), activation to isolated stimuli provides limited insight into later phases of emotion processing such as interpretation of cues or interactions between cues and established cognitive schemas. Recent work has highlighted the potential value of using complex video stimuli to probe socio-emotional processing, highlighting the need to develop standardized video coding schemas as this exciting field expands. Toward that end, we present a standardized and open-source coding system for complex videos, two fully coded videos, and a video and code processing Python library. The EmoCodes manual coding system provides an externally validated and replicable system for coding complex cartoon stimuli, with future plans to validate the system for other video types. The emocodes Python library provides automated tools for extracting low-level features from video files as well as tools for summarizing and analyzing the manual codes for suitability of use in neuroimaging analysis. Materials can be freely accessed at https://emocodes.org/ . These tools represent an important step toward replicable and standardized study of socio-emotional processing using complex video stimuli.
AB - Social information processing is vital for inferring emotional states in others, yet affective neuroscience has only begun to scratch the surface of how we represent emotional information in the brain. Most previous affective neuroscience work has used isolated stimuli such as static images of affective faces or scenes to probe affective processing. While this work has provided rich insight to the initial stages of emotion processing (encoding cues), activation to isolated stimuli provides limited insight into later phases of emotion processing such as interpretation of cues or interactions between cues and established cognitive schemas. Recent work has highlighted the potential value of using complex video stimuli to probe socio-emotional processing, highlighting the need to develop standardized video coding schemas as this exciting field expands. Toward that end, we present a standardized and open-source coding system for complex videos, two fully coded videos, and a video and code processing Python library. The EmoCodes manual coding system provides an externally validated and replicable system for coding complex cartoon stimuli, with future plans to validate the system for other video types. The emocodes Python library provides automated tools for extracting low-level features from video files as well as tools for summarizing and analyzing the manual codes for suitability of use in neuroimaging analysis. Materials can be freely accessed at https://emocodes.org/ . These tools represent an important step toward replicable and standardized study of socio-emotional processing using complex video stimuli.
KW - Emotion processing
KW - Movie-watching
KW - Naturalistic stimuli
KW - Valence–arousal system
UR - http://www.scopus.com/inward/record.url?scp=85126143233&partnerID=8YFLogxK
U2 - 10.1007/s42761-021-00100-7
DO - 10.1007/s42761-021-00100-7
M3 - Article
AN - SCOPUS:85126143233
SN - 2662-2041
VL - 3
SP - 168
EP - 181
JO - Affective Science
JF - Affective Science
IS - 1
ER -