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Conference Talk—SMPC 2015: Physical Fitness and Musical Taste: Do Physically Fit Listeners Prefer More Stressful Music?
- Lindsay Warrenburg
- David Huron
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Description: Research suggests that person-based dispositional factors, as well as properties of the music, influence a person’s musical tastes (e.g., Ladinig & Schellenberg, 2012). In this study, we examined the possibility that the interaction between the stressfulness of the music and a listener’s capacity for handling stress contributes to that listener’s musical preferences. We propose that three personal-dispositional factors may be expected to contribute to the development of a person’s musical taste: a person’s physical fitness at the time the musical taste was formed, the person’s androgen levels as indexed by their risk tolerance, and the person’s listening behaviors during the reminiscence bump – the tendency where adults show increased memories and preferences for events that happened during the adolescent years. The key prediction relating fitness and musical taste is that at the time the musical preference is formed, the stressfulness of the music should tend to reflect the person’s capacity for handling stress, including their physical fitness. This could help explain why older people, who are generally less physically fit, tend to prefer “milder” music. The study method made use of an online questionnaire, which received 696 responses. The questionnaire included a measure of physical fitness habits in different periods of the listener’s life, the impulsivity/sensation-seeking subset of the Zuckerman-Kuhlman Personality Questionnaire, and questions intended to characterize participants’ musical preferences. In order to create an independent index for estimating musical stressfulness, a parallel study was conducted, where an independent group of participants assessed the stressfulness of the music identified by participants in the main study. The stressfulness of the music was then predicted using the survey-based personality factors multiple linear regression model.