The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population

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

Modelling Melodic Discrimination Tests: Descriptive and Explanatory Approaches

Melodic discrimination tests have been used for many years to assess individual differences in musical abilities. These tests are usually analysed using classical test theory. However, classical test theory is not well suited for optimizing test efficiency or for investigating construct validity. This paper addresses this problem by applying modern item response modelling techniques to three melodic discrimination tests. First, descriptive item response modelling is used to develop a short melodic discrimination test from a larger item pool. The resulting test meets the test-theoretic assumptions of a Rasch (1960) item response model and possesses good concurrent and convergent validity as well as good testing efficiency. Second, an explicit cognitive model of melodic discrimination is used to generate hypotheses relating item difficulty to structural item features such as melodic complexity, similarity, and tonalness. These hypotheses are then tested on response data from three melodic discrimination tests (n = 317) using explanatory item response modelling. Results indicate that item difficulty is predicted by melodic complexity and melodic similarity, consistent with the proposed cognitive model. This provides useful evidence for construct validity. This paper therefore demonstrates the benefits of item response modelling both for efficient test construction and for test validity

Perceptual Dimensions of Short Audio Clips and Corresponding Timbre Features

This study applied a multi-dimensional scaling approach to isolating a number of perceptual dimensions from a dataset of human similarity judgements for short excerpts of recorded popular music (800ms). Two dimensions were well identified by two of the twelve timbral coefficients from the Echo Nestâ's Analyze service. One of these was also identified by MFCC features from the Queen Mary Vamp plugin set, however a third dimension could not be mapped by either feature set and may represent a musical feature other than timbre. Implications are discussed within the context of existing research into music cognition and suggestions for further research regarding individual differences in sound perception are given

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.

<p>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.</p

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.

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

Pearson correlations across 379 local authorities between median weekly gross income and the subscales of the self-report inventory as well as the performance scores from the listening tests.

<p><i>Footnote</i>. Pearson’s correlation coefficients and adjusted R² values from a linear regression model having only weekly income (in addition to an intercept) as predictor. *indicates a p-level of <.05 and ** a level of <.01.</p

Variable importance according to random forest model.

<p><i>Footnote.</i> Numerical values represent % increase in mean squared error if variable is omitted from model and hence higher values mean greater importance. Note that the model predicting general musical sophistication did not use the subscale scores for music training and active engagement.</p

Correlations between subscales from MEQ and Gold-MSI.

<p>Footnote. Values of Pearson’s correlation coefficient are reported for correlations between the six dimensions (rows) of the Music Experience Questionnaire (MEQ) and the 5+1 dimensions of the Gold-MSI. * indicates a p-level of <.05 and ** a level of <.01.</p

Correlations between sub-scales of the self-report inventory and performance on the two listening tests.

<p>Footnote. Sample sizes differed slightly between bivariate correlations from the online sample and ranged from n = 136,924 to n = 139,062. Sample size for the test-retest sample was n = 34.</p