Eleonora Diletta Sarcinella

Nostalgia is a complex emotion that connects the present with both the remembered past and the imagined future. While traditionally studied in its past-oriented form, future-oriented variants, such as anticipatory and anticipated nostalgia, remain underexplored, partly due to methodological challenges. Although these forms also engage prospective cognitive processes, like episodic future thinking and mental simulation, they have usually been addressed through conventional, memory-based paradigms. This perspective proposed novel methodological design guidelines for eliciting nostalgia across its full temporal spectrum (past and future oriented variants) by combining Virtual Reality (VR) with a specific class of content: liminal spaces, contexts marked by transition and ambiguity. VR offers immersive and controllable environments, while liminal settings symbolically reflect change, a core feature of nostalgic experience. Specifically, we offered design guidelines for constructing liminal spaces aimed at eliciting both past- and future-oriented nostalgia, through the manipulation of two key variables: familiarity and ambiguity. This approach seeks to enhance experimental control and ecological validity, addressing current limitations in nostalgia research. Finally, it offered potential clinical applications, especially in contexts where reconnecting with meaning and future self-continuity is essential, such as during life transitions or emotional distress.

Flow and creativity have always held a special link in artistic domains. Music-making is increasingly demonstrated as a source of group flow and collaborative musical creativity. The Networked Flow (NF) model outlines factors supporting optimal collaborative group work in different contexts, including music. The model suggests that high levels of affect, group flow, and social presence are crucial for optimal group creativity, along with the progressive emergence of a particular group communication structure. Therefore, it would be crucial to study group creativity over time by examining communication dynamics, experiential features, and creative outcomes. In this study, the NF model was applied to a 2-day real-world music contest. A total of nine bands and eight rappers participated in the first round of the contest. At the end of the first round, each band was randomly paired with a rapper, forming new bands that were asked to compose a piece of music and perform it in the second round of the contest a month later. Measures of affect, group flow, social presence, communication structure, self-assessed and expert-rated performance outcomes, and creativity in group performance were collected during each of the two rounds. Methods of evaluating creative collaboration included Social Network Analysis (communication structure) and Consensual Assessment Technique (creativity in group performance). Significant positive correlations between group flow and social presence emerged. Only one of the three judges reported an increase in group creativity in the final phase. There was little agreement between the judgments of the expert judges and participants in terms of artists’ creativity and performance. Flow dimensions were positively correlated with self-rated performance outcomes and creativity but not with expert-rated performance outcomes. Bands that scored high for flow had a specific communication structure, characterized by high-status members. This study contributes to a deeper understanding of how group flow and intersubjective dynamics interact in live music.

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.