Music-induced analgesia (MIA) is the ability of music to influence pain perception. Although this phenomenon has been extensively studied in recent years, only a few studies have addressed what musical characteristics are optimal for MIA. Here, we present a novel approach to this topic, using a recently proposed model of music attribute preferences. The model addresses three musical dimensions: arousal, valence, and depth. Thirty participants (15 women and 15 men, M age = 37.1 years, standard deviation = 15.7) were subjected to experimental pain stimulation (cold-pressor task) while listening to brief music excerpts with characteristics of the three attribute dimensions. Each excerpt was selected to score high on one of the three attributes while being average on the other two, to create three distinct music conditions. There was also a control condition, where participants listened to white noise. Results showed that average pain ratings were significantly lower in the arousal (p = 0.002) and depth (p = 0.01) conditions compared to the control condition. Furthermore, participants showed increased pain tolerance in musical conditions compared to the control condition (p = 0.04). This preliminary report introduces a novel approach to studying MIA in the context of music attribute preferences. With the advent of online music streaming services, this research opens new possibilities for music-based pain interventions.
Keywords: analgesia; cold-pressor task; experimental pain; music; music attributes; music preferences; pain.