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

The fit statistics of the four structural equation models confirming the factor structure of the self-report inventory on the data test set (n = 73,739).

<p>Footnote. BIC = Bayesian Information Criterion, TLI = Tucker-Lewis Index, CFI = Bentler’s Comparative Fit Index, RMSEA = Root Mean Square Error of Approximation, SPMR = Standardized Root Mean Square Residual.</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

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

<p>Structural equation model relating subscales of the self-report inventory to performance scores on the two listening tests.</p

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

<p>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.</p

Correlations between ‘Big Five’ personality traits as measured by the TIPI and Eysenck’s Extraversion scale and the subscales of the Gold-MSI self-report inventory.

<p>Footnote. For all correlations n = 224, except for those involving Singing Abilities, where n = 161 due to a technical error. Means and standard deviations of the summed personality scores (range TIPI: 2–14, range Eysenck: 0–12) are also given. * indicates a p-level of <.05 and ** a level of <.01.</p