A new method of exploring affect dynamics in music

A psychometric model based on stochastic processes

Dados Bibliográficos

ID20407828
AutoresFrancesca Borghesi (0000-0003-1356-8271, University of Turin, autor correspondente), Eleonora Diletta Sarcinella (0009-0004-4993-774X, Università Cattolica del Sacro Cuore), Valentina Mancuso (0000-0002-4198-3723, Università degli Studi eCampus), Alice Chirico (0000-0001-9955-1926, Università Cattolica del Sacro Cuore), Pietro Cipresso (0000-0002-0662-7678, University of Turin)
Ano2025
Volume29
Fascículo3
Páginas405-424
Data de publicação2025-09-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoMusicae Scientiae (JOURNAL)
Identificadores do periódicoISSN: 1029-8649 • E-ISSN: 2045-4147
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/10298649251324343
OpenAlexW4413600843
IdiomaEN
Citações recebidas1
Referências citadas68

For several years, researchers in the realm of music psychology have sought to understand how listeners perceive and experience emotions during music listening. Experimental and psychometric tools have been developed to explore the nuances of these emotional experiences, highlighting individual differences. Surprisingly, while much effort has been made to relate musical elements to specific emotional states, it is still an open issue explaining how listeners shift between different affective states (affect dynamics). In this study, we introduce a novel methodological approach to measuring affect dynamics in music by employing a Markov chain model—a stochastic framework that predicts the likelihood of transitions between affective states based on the current state. A single-case study was conducted in which a participant was exposed to emotion-inducing images from the International Affective Picture System (IAPS) and a week later to emotion-inducing music. During both sessions, physiological responses were recorded using facial electromyography (fEMG) to measure corrugator supercilii and zygomaticus major muscle activity, assessing emotional valence, alongside galvanic skin response (GSR) to assess arousal. The Markov chain framework was used to create a matrix of conditional transition probabilities, identifying both the participant’s baseline affective state (self-transitions, reflecting trait-like stability) and three types of affective transitions based on Russell’s circumplex model: vertical (i.e., arousal changes), horizontal (i.e., valence changes), and oblique (i.e., simultaneous arousal and valence changes). Our exploratory analysis demonstrated that affect transitions can be quantified in both conditions, revealing modality-specific patterns. Image exposure led to greater vertical transitions across all signals, whereas music elicited more stable baseline affective states. Oblique transitions showed consistent physiological patterns (specifically, decreased GSR and increased muscle activity) across both modalities, highlighting distinct yet interconnected affective dynamics. Taken together, the findings reveal a complex interplay between stimulus modality and the physiological markers of affect dynamics

Cognitive psychology · Pedagogy · Communication · Music and Audio Processing · Music Technology and Sound Studies · Neuroscience and Music Perception · Psychology · Social Psychology