The musicality of non-musicians: an index for assessing musical sophistication in the general population

Abstract

Musical skills and expertise vary greatly in Western societies. Individuals can differ in their repertoire of musical behaviours as well as in the level of skill they display for any single musical behaviour. The types of musical behaviours we refer to here are broad, ranging from performance on an instrument and listening expertise, to the ability to employ music in functional settings or to communicate about music. In this paper, we first describe the concept of 'musical sophistication' which can be used to describe the multi-faceted nature of musical expertise. Next, we develop a novel measurement instrument, the Goldsmiths Musical Sophistication Index (Gold-MSI) to assess self-reported musical skills and behaviours on multiple dimensions in the general population using a large Internet sample (n = 147,636). Thirdly, we report results from several lab studies, demonstrating that the Gold-MSI possesses good psychometric properties, and that self-reported musical sophistication is associated with performance on two listening tasks. Finally, we identify occupation, occupational status, age, gender, and wealth as the main socio-demographic factors associated with musical sophistication. Results are discussed in terms of theoretical accounts of implicit and statistical music learning and with regard to social conditions of sophisticated musical engagement.

Figure 1. Factor structure of reduced self-report inventory as formalised by model 2, the Schmid-Leiman variant of the confirmatory factor model.

Figure 2. Structural equation model relating subscales of the self-report inventory to performance scores on the two listening tests.

Figure 3. Structural equation model demonstrating the influence of self-reported general musical sophistication on the performance on the objective listening tasks.

Figure 4. Conditional inference regression tree modelling general musical sophistication with variables of socio-economic status.

The tree model is interpreted by starting at the top of the tree, following each branch down from each node, to arrive at a terminal node with the average scores given inside the squares on the graph. For example, descending to the right from node 1 (‘Occupation’) down the ‘Finance, Medical, Engineering, Administration, etc.’ branch, then descending to the right at node 9 (‘Age’) down the ‘>38’ branch, and finally descending the right branch (‘Administration, Customer Service, etc’) going off node 13 (‘Occupation’) to arrive at terminal node 15, this can be interpreted as follows: People working in administrative or customer service occupation and being older than 38 years will obtain on average a general musical sophistication score of 73.4. Technically, the logical combinations of these two conditions can be regarded as an interaction of the two predictor variables. The significance values for each split are given within the oval nodes and are derived from a Monte Carlo resampling procedure that adjusts for multiple testing.

Figure 5. Conditional inference regression tree modelling accuracy scores (percentage scale from 0 to 100 where 50 indicates chance level) in the melody memory task using self-reported musical training and variables of socio-economic status as predictors.

Figure 6. Conditional inference regression tree modelling accuracy scores (percentage scale from 0 to 100 where 50 indicates chance level) in the beat perception task using self-reported musical training, active engagement, and variables of socio-economic status as predictors.

Figure 7. Distribution of median weekly gross income according to the 2011 Annual Survey of Hours and Earnings survey (Office for National Statistics, 2012) and general musical sophistication, musical training and active engagement across 379 local authorities of Great Britain.

Values for all four variables were each split into 9 quantiles with approximately equal numbers of local authorities.