Domain specific traits predict achievement in music and multipotentiality

Örjan de Manzano and Fredrik Ullén

Intelligence, 2021, vol. 89, issue C

Abstract: Previous research shows that individuals choose careers based on the relative strengths of various traits. More debated however, is how specific combinations of traits predict individual differences in professional achievements. General intelligence is often proposed to be the best predictor of eminence, but some studies suggest that more specific traits can be relatively important when performance depends on specific skills and expertise. Here we identified a comprehensive set of variables relevant for music achievement (intelligence, auditory ability, absolute pitch, Big-five personality traits, psychosis proneness, music flow proneness, childhood environment and music practice), and tested how they predicted level of musicianship (non-musicians vs. amateur musicians vs. professional musicians) and number of achievements among professional musicians. We used web survey data from a total of 2150 individuals, and generalized additive models that can also reveal non-linear relationships. The results largely confirmed our three main hypotheses: (i) non-musicians, amateur musicians, and professional musicians are best differentiated by domain specific abilities, personality traits, and childhood factors; (ii) largely the same significant predictors are also associated with the number of creative achievements within professional musicians; (iii) individuals who reach a professional level in two domains (here science and music) possess the union of the relevant traits of both domains. In addition, many of the associations between predictors and achievement were non-linear. This study confirms that in music, and potentially in other occupational fields where performance relies on specific competences, domain relevant characteristics may be better predictors of engagement and creative achievement than broad traits.

Keywords: Intelligence; Achievement; Music; Creativity; Expertise; Generalized additive models (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intell:v:89:y:2021:i:c:s0160289621000684

DOI: 10.1016/j.intell.2021.101584

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