Decoding Musical Valence and Arousal: Exploring the Neural Correlates of Music-Evoked Emotions and the Role of Expressivity Features

Music conveys both basic emotions, like joy and sadness, and complex ones, such as tenderness and nostalgia. Its effects on emotion regulation and reward have attracted much research attention, as the neural correlates of music-evoked emotions may inform neurorehabilitation interventions. Here, we used fMRI to decode and examine the neural correlates of perceived valence and arousal in music excerpts. Twenty participants were scanned while listening to 96 music excerpts, classified beforehand into four categories varying in valence and arousal. Music modulated activity in cortical regions, most noticeably in music-specific subregions of the auditory cortex, thalamus, and regions of the reward network such as the amygdala. Using multivoxel pattern analysis, we created a computational model to decode the perceived valence and arousal of the music excerpts with above-chance accuracy. We further explored associations between musical features and brain activity in valence-, arousal-, reward-, and auditory-related networks. The results emphasize the involvement of distinct musical features, notably expressive features such as vibrato and tonal and spectral dissonance in valence, arousal, and reward brain networks. Using ecologically valid music stimuli, we contribute to delineating the neural correlates of music-evoked emotions with potential implications in the development of novel music-based neurorehabilitation strategies.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Affective Computing
Published
2024-11-27
DOI
https://doi.org/10.1109/taffc.2024.3507192
Citations
5
Primary Topic
Neuroscience and Music Perception
Type
article
Field-Weighted Citation Impact
1.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decoding Musical Valence and Arousal: Exploring the Neural Correlates of Music-Evoked Emotions and the Role of Expressivity Features

Inês Bernardino, Teresa Sousa, Daniela Pereira, Rui Pedro Paiva et al.
5 citations
IEEE Transactions on Affective Computing
Neuroscience and Music Perception
1.71
article

Decoding Musical Valence and Arousal: Exploring the Neural Correlates of Music-Evoked Emotions and the Role of Expressivity Features

Inês Bernardino, Teresa Sousa, Daniela Pereira, Rui Pedro Paiva, César F. Lima, Renato Panda, Bruno Direito, Inês Almeida, Alexandre Sayal, Miguel Castelo‐Branco, Ana Gabriela Guedes
article en
5 citations

Abstract

Music conveys both basic emotions, like joy and sadness, and complex ones, such as tenderness and nostalgia. Its effects on emotion regulation and reward have attracted much research attention, as the neural correlates of music-evoked emotions may inform neurorehabilitation interventions. Here, we used fMRI to decode and examine the neural correlates of perceived valence and arousal in music excerpts. Twenty participants were scanned while listening to 96 music excerpts, classified beforehand into four categories varying in valence and arousal. Music modulated activity in cortical regions, most noticeably in music-specific subregions of the auditory cortex, thalamus, and regions of the reward network such as the amygdala. Using multivoxel pattern analysis, we created a computational model to decode the perceived valence and arousal of the music excerpts with above-chance accuracy. We further explored associations between musical features and brain activity in valence-, arousal-, reward-, and auditory-related networks. The results emphasize the involvement of distinct musical features, notably expressive features such as vibrato and tonal and spectral dissonance in valence, arousal, and reward brain networks. Using ecologically valid music stimuli, we contribute to delineating the neural correlates of music-evoked emotions with potential implications in the development of novel music-based neurorehabilitation strategies.

IEEE Transactions on Affective ComputingVol. 16(2)
Iscte – Instituto Universitário de Lisboa (PT), Iscte – Instituto Universitário de Lisboa (PT), Siemens (Portugal) (PT), Institute for Systems Engineering and Computers (PT), Instituto Politécnico de Tomar (PT), Research Center for Natural Resources, Environment and Society (PT), University of Coimbra (PT)
Reduced inequalities
Openalex Percentile: Top 15%
Neuroscience and Music Perception
1.71
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.