Jason Jiří Musil’s research while affiliated with Goldsmiths, University of London and other places

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.

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.

This study applied a multi-dimensional scaling approach to isolating a number of perceptual dimensions from a dataset of human similarity judgements for 800ms excerpts of recorded popular music. These dimensions were mapped onto the 12 timbral coefficients from the Echo Nest's Analyzer. Two dimensions were identified by distinct coefficients, however a third dimension could not be mapped and may represent a musical feature other than timbre. Implications are discussed within the context of existing research into human musical cognition. Suggestions for further research are given, which may help to establish whether surface features are processed using a common feature set (as in many music information retrieval systems), or whether individuals use features idiosyncratically to quickly process surface features of music.