Professor Daniel Müllensiefen

I'm a music psychologist and Co-Director of the MSc in Music, Mind, and Brain.

Staff details

Position

Emeritus Professor

I'm a Professor in Psychology at Goldsmiths and also a research fellow with the University of Music, Drama, and Media at Hannover, Germany.

I'm interested in (almost) all questions relating to music psychology, ranging from industry-related work on music and advertising (working as Scientist in Residence with adam&eveDDB) to individual differences in musical ability and to musical memory and plagiarism where I have served as an expert witness in many cases.

I was awarded the prestigious Anneliese-Maier award by the Humboldt- Foundation in 2016 and I'm the editor of the international open-access journal Empirical Musicology Review. I am currently heading the LongGold project, a longitudinal study on the development of musical and other cognitive and social abilities during adolescence.

Research interests

My main themes of research relate to the study of the development of musical abilities and the psychometrics of music, computational and statistical models of music perception, cognitive biases in musical judgement and the behavioural economics of music. For full details on projects and research interests see.

Here are some areas that I've worked on in the past:

Memory for Music, Musical Similarity Perception, Music in Advertising, Audiobranding, Musical Sophistication, Development of Musical Abilities, Judgement and Decision Making in Music, Individual Differences and Measurement of Musical Skills, Cognitive Issues Related to Music Copyright

Featured publications

2014: The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population.

2017: Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item ge

2017: The repeated recording illusion: The effects of extrinsic and individual difference factors on musical judgements

2016: Dissecting an Earworm: Melodic Features and Song Popularity Predict Involuntary Musical Imagery

2004: Cognitive Adequacy in the Measurement of Melodic Similarity: Algorithmic vs. Human Judgments

Publications and research outputs

Book Section

Statistical Methods in Music Corpus Studies: Application, Use Cases, and Best Practice Examples Müllensiefen, Daniel and Frieler, Klaus. 2022. Statistical Methods in Music Corpus Studies: Application, Use Cases, and Best Practice Examples. In: Daniel Shanahan; John Ashley Burgoyne and Ian Quinn, eds. The Oxford Handbook of Music and Corpus Studies. Oxford: Oxford University Press. ISBN 9780190945442
Musikalische Fertigkeiten und ihre Messbarkeit Müllensiefen, Daniel and Hemming, Jan. 2017. Musikalische Fertigkeiten und ihre Messbarkeit. In: Andreas C. Lehmann, and Reinhard Kopiez,, eds. Handbuch Musikpsychologie. Göttingen: Hogrefe.
Is it the song and not the singer? Hit song prediction using structural features of melodies. Frieler, K; Jakubowski, Kelly and Müllensiefen, Daniel. 2015. Is it the song and not the singer? Hit song prediction using structural features of melodies. In: , ed. Yearbook of the German Society for Music Psychology. 24 , pp. 41-54

Article

The Influence of Psychosocial Skills on the Development of Musical Abilities: Cross-Sectional Results From Secondary School Pupils in Latvia Voitova, Tatjana; Bernhofs, Valdis and Müllensiefen, Daniel. 2025. The Influence of Psychosocial Skills on the Development of Musical Abilities: Cross-Sectional Results From Secondary School Pupils in Latvia. Gifted Child Quarterly, 69(2), pp. 184-201. ISSN 0016-9862
Development of an adaptive test of musical scene analysis abilities for normal-hearing and hearing-impaired listeners Hake, Robin; Bürgel, Michel; Nguyen, Ninh K.; Greasley, Alinka; Müllensiefen, Daniel and Siedenburg, Kai. 2024. Development of an adaptive test of musical scene analysis abilities for normal-hearing and hearing-impaired listeners. Behavior Research Methods, 56(6), pp. 5456-5481. ISSN 1554-351X
Perception of Chord Sequences Modeled with Prediction by Partial Matching, Voice-Leading Distance, and Spectral Pitch-Class Similarity: A New Approach for Testing Individual Differences in Harmony Perception Eitel, Matthew; Ruth, Nicolas; Harrison, Peter; Frieler, Klaus and Müllensiefen, Daniel. 2024. Perception of Chord Sequences Modeled with Prediction by Partial Matching, Voice-Leading Distance, and Spectral Pitch-Class Similarity: A New Approach for Testing Individual Differences in Harmony Perception. Music & Science, 7, ISSN 2059-2043

Conference or Workshop Item

Can correlations imply causation? Causal modeling and music psychology research. Müllensiefen, Daniel; Floridou, Georgia A. and Jakubowski, Kelly. 2015. 'Can correlations imply causation? Causal modeling and music psychology research.'. In: Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music. Manchester, United Kingdom.
Corpus Analysis Tools for Computational Hook Discovery. Van Balen, J; Burgoyne, J.A; Bountouridis, D; Müllensiefen, Daniel and Veltkamp, R. 2015. 'Corpus Analysis Tools for Computational Hook Discovery.'. In: Proceedings of the 16th International Society for Music Information Retrieval (ISMIR) Conference 2014. Spain.
Multi-modal Musical Performance Capture Müllensiefen, Daniel; Baker, David; Lewis, Richard; Rhodes, Christophe; Fields, Ben and Crawford, Tim. 2014. 'Multi-modal Musical Performance Capture'. In: Digital Music Research Network (DMRN+9). London, United Kingdom.