Abstract
We investigated the relationship between music engagement, academic success and intelligence in non-musical students (N = 136). Currently, there is a lack of understanding to the extent in which individual leisure interests and extracurricular engagement influence academic success in different subject areas, as well as the relationship between fluid intelligence and crystallized experience. Clarifying these questions we formed the aim of this study. To diagnose musical engagement and intelligence we used the approved psychometric techniques - Gold-MSI v1.0 “Active engagement” scale, Raven's test, Amthauer's test. We checked the assumptions that musical engagement influences the strength of the correlation between fluid and crystallized intelligence and that the relationship between musical engagement and academic success is determined by the level of fluid intelligence on the one hand, and the degree of homogeneity of the subject areas of engagement on the other. The results of the study showed that musical engagement may play a significant role in predicting the academic performance of non-musicians as well as in the development of crystallized experience. The hypothesis that musical engagement, being a non-relevant behavioral activity in relation to general educational experience and competence, contributes to a decrease in the strength of the link between fluid and crystallized intelligence was confirmed. The other hypothesis was partially confirmed, as it was found that the relationship between musical engagement and academic success is mediated by the fluid intelligence factor, but this mediation is specific to groups with different professional skills. There is a non-linear relationship in the humanities group: the best learning performance is achieved by students with intermediate levels of engagement. In the group of "precise" occupations, the relationship between the variables is linear and negative. The findings are interpreted in terms of modern models of intelligence and subjectoriented knowledge models. The results contribute to improving the quality of prediction of learning success and professional realization, and contribute to elucidating the psychological mechanisms of engagement.
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