TY - JOUR
T1 - Neural tracking in infants – An analytical tool for multisensory social processing in development
AU - Jessen, Sarah
AU - Obleser, Jonas
AU - Tune, Sarah
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021/12
Y1 - 2021/12
N2 - Humans are born into a social environment and from early on possess a range of abilities to detect and respond to social cues. In the past decade, there has been a rapidly increasing interest in investigating the neural responses underlying such early social processes under naturalistic conditions. However, the investigation of neural responses to continuous dynamic input poses the challenge of how to link neural responses back to continuous sensory input. In the present tutorial, we provide a step-by-step introduction to one approach to tackle this issue, namely the use of linear models to investigate neural tracking responses in electroencephalographic (EEG) data. While neural tracking has gained increasing popularity in adult cognitive neuroscience over the past decade, its application to infant EEG is still rare and comes with its own challenges. After introducing the concept of neural tracking, we discuss and compare the use of forward vs. backward models and individual vs. generic models using an example data set of infant EEG data. Each section comprises a theoretical introduction as well as a concrete example using MATLAB code. We argue that neural tracking provides a promising way to investigate early (social) processing in an ecologically valid setting.
AB - Humans are born into a social environment and from early on possess a range of abilities to detect and respond to social cues. In the past decade, there has been a rapidly increasing interest in investigating the neural responses underlying such early social processes under naturalistic conditions. However, the investigation of neural responses to continuous dynamic input poses the challenge of how to link neural responses back to continuous sensory input. In the present tutorial, we provide a step-by-step introduction to one approach to tackle this issue, namely the use of linear models to investigate neural tracking responses in electroencephalographic (EEG) data. While neural tracking has gained increasing popularity in adult cognitive neuroscience over the past decade, its application to infant EEG is still rare and comes with its own challenges. After introducing the concept of neural tracking, we discuss and compare the use of forward vs. backward models and individual vs. generic models using an example data set of infant EEG data. Each section comprises a theoretical introduction as well as a concrete example using MATLAB code. We argue that neural tracking provides a promising way to investigate early (social) processing in an ecologically valid setting.
UR - http://www.scopus.com/inward/record.url?scp=85118885523&partnerID=8YFLogxK
U2 - 10.1016/j.dcn.2021.101034
DO - 10.1016/j.dcn.2021.101034
M3 - Journal articles
C2 - 34781250
AN - SCOPUS:85118885523
SN - 1878-9293
VL - 52
JO - Developmental Cognitive Neuroscience
JF - Developmental Cognitive Neuroscience
M1 - 101034
ER -