Parsing Emotion in Classical Music: A Behavioral Study on the Cognitive Mapping of Key, Tempo, Complexity and Energy in Piano Performance
Source abstract
Abstract
Music conveys emotion through a complex interplay of structural and acoustic cues, yet how these features map onto specific affective interpretations remains a key question in music cognition. This study explored how listeners, unaware of contextual information, categorized 110 emotionally diverse excerpts—varying in key, tempo, note density, acoustic energy, and expressive gestures—from works by Bach, Beethoven, and Chopin. Twenty classically trained participants labeled each excerpt using six predefined emotional categories. Emotion judgments were analyzed within a supervised multi-class classification framework, allowing systematic quantification of recognition accuracy, misclassification patterns, and category reliability. Behavioral responses were consistently above chance, indicating shared decoding strategies. Quantitative analyses of live performance recordings revealed systematic links between expressive features and emotional tone: high-arousal emotions showed increased acoustic intensity, faster gestures, and dominant right-hand activity, while low-arousal states involved softer dynamics and more left-hand involvement. Major-key excerpts were commonly associated with positive emotions—“Peacefulness” with slow tempos and low intensity, “Joy” with fast, energetic playing. Minor-key excerpts were linked to negative/ambivalent emotions, aligning with prior research on the emotional complexity of minor modality. Within the minor mode, a gradient of arousal emerged, from “Melancholy” to “Power,” the latter marked by heightened motor activity and sonic force. Results support an embodied view of musical emotion, where expressive meaning emerges through dynamic motor-acoustic patterns that transcend stylistic and cultural boundaries.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Neuroscience and Music Perception · Diverse Music Education Insights · Music Technology and Sound Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Cognitive Electrophysiology Laboratory, Department of Psychology, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20162 Milan, Italy
Master’s Program in “Human-Centered Artificial Intelligence” HCAI, Department of Philosophy, University of Milan, 20122 Milan, Italy
Doctoral School in Theoretical and Applied Neuroscience, University of Camerino, 62032 Camerino, Italy
Funding
ATE—Fondo di Ateneo 2025
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