A new method of exploring affect dynamics in music: A psychometric model based on stochastic processes

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.