Human–AI emotional resonance in generative music: a mini-review of authorship attribution, affective response, and aesthetic evaluation

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Generative artificial intelligence is increasingly involved in music creation, raising important questions about how listeners emotionally respond to music that is generated by, or attributed to, AI. This mini-review synthesizes recent empirical research on listeners' emotional, aesthetic, and physiological responses to AI-generated music. Existing evidence suggests that AI systems can reproduce some acoustic cues associated with basic emotional categories and may elicit measurable affective or attentional responses under specific conditions. However, listeners' evaluations are shaped not only by acoustic features but also by authorship attribution. Several studies report lower liking, perceived quality, authenticity, or engagement when music is attributed to AI, although null and occasionally reversed label effects have also been observed. To explain this pattern, this review proposes a dual-process framework that distinguishes between low-level acoustic–emotional processing and high-level source-based appraisal involving agency, intentionality, authenticity, and human experience. Overall, current evidence supports a partial dissociation between immediate affective responses and higher-order cognitive evaluation. Whether AI-generated music can support deeper forms of emotional resonance, such as long-term attachment, perceived understanding, personal meaning, or source-based empathy, remains insufficiently established. Future research should use identical-audio attribution designs, stricter audio-quality matching, cross-genre comparisons, and longitudinal listening paradigms.