Dr Marcus Pearce


Dr Marcus Pearce is the director of the EEG Lab at Queen Mary, University of London. Educated in experimental psychology and artificial intelligence at Oxford and Edinburgh, Marcus Pearce received his PhD from City University, London in 2005, before continuing as a post-doctoral research fellow, working on music cognition at Goldsmiths, University of London and neuroaesthetics in the Wellcome Laboratory of Neurobiology at University College London. In 2010, he returned to Goldsmiths as co-investigator on an EPSRC-funded project investigating information and neural dynamics in the perception of musical structure. He is currently Lecturer in Sound and Music Processing at Queen Mary, University of London where he is leader of the Music Cognition Lab, director of the EEG Laboratory and co-director of the cross-faculty Centre for Mind in Society. His research interests cover computational, psychological and neuroscientific aspects of music cognition, with a particular focus on dynamic, predictive processing of melodic, rhythmic and harmonic structure, and its impact on emotional and aesthetic experience. He has over 50 peer-reviewed papers, with a total citation count above 700 and an H-index of 15.




Publications

 

2013

 

Brattico, E. & Pearce, M. T. (2013). The neuroaesthetics of music. Psychology of Aesthetics, Creativity and the Arts, 7, 48-61.

 

Omigie, D., Pearce, M. T., Williamson, V. & Stewart, L. (2013). Electrophysiological correlates of melodic processing in congenital amusia. Neuropsychologia, 51, 1749-1762.

 

Egermann, H., Pearce, M. T., Wiggins, G. A. & McAdams, S. (2013). Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. Cognitive, Affective and Behavioural Neuroscience, 13, 533-553.

 

Bailes, F., Dean, R. T. & Pearce M. T. (2013). Music cognition as mental time travel. Scientific Reports, 3, 2690.

 

Song C., Simpson A. J. R., Harte C. A., Pearce M. T. & Sandler M. B. (2013). Syncopation and the Score. PLoS ONE 8(9): e74692.

 

Whorley, R., Wiggins, G., Rhodes, C. & Pearce, M. T. (2013). Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints in the Harmonisation Problem. Journal of New Music Research, 42, 237-266.

 

2012

 

Carrus, E., Pearce, M. T., & Bhattacharya, J. (2012). Melodic pitch expectation interacts with neural responses to syntactic but not semantic violations. Cortex, 1-15.

 

Pearce, M. T. & Rohrmeier, M. (2012). Music cognition and the cognitive sciences. Topics in Cognitive Science, 4, 468-484.

 

Cameron, D. J., Stewart, L., Pearce, M. T., Grube, M., & Muggleton, N. G. (2012). Modulation of motor excitability by metricality of tone sequences. Psychomusicology, 22, 122-128.

 

Omigie, D., Pearce, M. T., & Stewart, L. (2012). Tracking of pitch probabilities in congenital amusia. Neuropsychologia, 50, 1483-1493.

 

Pearce, M. T. & Wiggins, G. A. (2012). Auditory expectation: The information dynamics of music perception and cognition. Topics in Cognitive Science, 4, 625-652.

 

Pearce, M. T., Christensen, J.F. (2012). Conference Report: The Neurosciences and Music - IV - Learning and Memory. Psychomusicology, 22, 70-73.

 

2011

 

Pearce, M. T. (2011). Time-series analysis of Music: Perceptual and Information Dynamics. Empirical Musicology Review, 6, 125-130.

 

Nadal, M. & Pearce, M. T. (2011). The Copenhagen Neuroaesthetics conference: Prospects and pitfalls for an emerging field. Brain and Cognition, 76, 172-183.

 

2010

 

Pearce, M. T., Müllensiefen, D. & Wiggins, G. A. (2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception, 39, 1367-1391.

 

Pearce, M. T., Ruiz, M. H., Kapasi, S., Wiggins, G. A. & Bhattacharya, J. (2010). Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation. NeuroImage, 50, 302-313.

 

Wiggins, G. A., Müllensiefen, D. & Pearce, M. T. (2010). On the non-existence of music: Why music theory is a figment of the imagination. Musicae Scientiae, Discussion Forum 5, 231-255.

 

Pearce, M. T., Müllensiefen, D. & Wiggins, G. A. (2010). Melodic grouping in music information retrieval: New methods and applications . In Z. W. Ras and A. Wieczorkowska (Eds.), Advances in Music Information Retrieval (pp. 364-388). Berlin: Springer.

 

Whorley, R., Wiggins,G. A., Rhodes, C. S. & Pearce, M. T. (2010). Development of Techniques for the Computational Modelling of Harmony. In Ventura et al. (Eds.), Proceedings of the International Conference on Computational Creativity. Lisbon

 

2009

 

Pearce, M. T.(2009). To beep or not to beep. Contemporary Music Review, 28, 125-126.

 

Wiggins, G. A., Pearce M. T. & Müllensiefen, D. (2009). Computational modelling of music cognition and musical creativity . In R. Dean (Ed.), The Oxford Handbook of Computer Music (pp. 383-420). Oxford: Oxford University Press.

 

Rohrmeier, M., Honing, H., Rebuschat, P., Loui, P., Wiggins, G., Pearce, M. T. & Müllensiefen, D. (2009). Music Cognition: Learning and Processing. In N. A. Taatgen & H. v. Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 41-42). Austin, TX: Cognitive Science Society.

 

2008

 

Pearce, M. T., Müllensiefen, D., & Wiggins, G. A. (2008). A comparison of statistical and rule-based models of melodic segmentation. In Proceedings of the Ninth International Conference on Music Information Retrieval, (pp. 89-94). Philadelphia, USA: Drexel University.

 

Pearce, M. T. & Müllensiefen, D. (2008). David Huron, Sweet Anticipation: Music and the Psychology of Expectation. Cambridge, MA: MIT Press, 2006, 512 pp., ISBN 0262083450, (Hardcover). Musicae Scientiae, 12, 158-168.

 

2007

 

Potter, K., Wiggins, G. A. & Pearce, M. T. (2007). Towards greater objectivity in music theory: Information-dynamic analysis of minimalist music. Musicae Scientiae, 11, 295-322.

 

Pearce, M. T. & Wiggins, G. A. (2007). Evaluating cognitive models of musical composition . In A. Cardoso and G. A. Wiggins (Eds.), Proceedings of the 4th International Joint Workshop on Computational Creativity, (pp. 73-80). London: Goldsmiths, University of London.

 

Pearce, M. T., Müllensiefen, D., Lewis, D. & Rhodes, C. S. (2007). David Temperley, Music and Probability. Cambridge, Massachusetts: MIT Press, 2007, ISBN-13: 978-0-262-20166-7 (hardcover) $40.00. Empirical Musicology Review, 2, 155-163.

 

2006

 

Pearce, M. T. & Wiggins, G. A. (2006). Expectation in melody: The influence of context and learning. Music Perception, 23, 377-405.

 

Pearce, M. T. & Wiggins, G. A. (2006). The information dynamics of melodic boundary detection. In M. Baroni, A. R. Addessi, R. Caterina and M. Costa (Eds.), Proceedings of the 9th International Conference of Music Perception and Cognition, (pp. 860-867). Bologna, Italy: SMPC and ESCOM.

 

2005

 

Pearce, M. T. (2005). The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition . Doctoral Dissertation, Department of Computing, City University, London, UK. Examiners: Petri Toiviainen and Artur d'Avila Garcez.

 

Pearce, M. T., Conklin, D. & Wiggins, G. A. (2005). Methods for combining statistical models of music. In U. K. Wiil (Ed.), Computer Music Modelling and Retrieval (pp. 295-312). Heidelberg: Springer.

 

2004

 

Pearce, M. T. & Wiggins, G. A. (2004). Improved methods for statistical modelling of monophonic music. Journal of New Music Research, 33, 367-385.

 

Pearce, M. T. & Wiggins, G. A. (2004). Rethinking Gestalt influences on melodic expectancy. In S. D. Lipscomb, R. Ashley, R. O. Gjerdingen and P. Webster (Eds.), Proceedings of the 8th International Conference of Music Perception and Cognition, (pp. 367-371). Adelaide, Australia: Causal Productions.

 

Pearce, M. T. & Meredith, D. (2004). Review of the Third International Symposium on Computer Music Modelling and Retrieval. In Computer Music Journal, 28, 91-93.

 

2003

 

Pearce, M. T. & Wiggins, G. A. (2003). An empirical comparison of the performance of PPM variants on a prediction task with monophonic music. In Proceedings of the AISB'03 Symposium on Artificial Intelligence and Creativity in Arts and Science, (pp. 74-83). Brighton, UK: SSAISB.

 

2002

 

Pearce, M. T., Meredith, D. & Wiggins, G. A. (2002). Motivations and methodologies for automation of the compositional process. Musicae Scientiae, 6, 119-147.

 

Pearce, M. T. & Wiggins, G. A. (2002). Aspects of a cognitive theory of creativity in musical composition. In Proceedings of the ECAI'02 Workshop on Creative Systems, (pp. 17-24). Lyon, France.

 

2001

 

Pearce, M. T. & Wiggins, G. A. (2001). Towards a framework for the evaluation of machine compositions. In Proceedings of the AISB'01 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences, (pp.22-32). Brighton, UK: SSAISB.

 

Pearce, M. T. (2001). Report on the ICCBR'01 Workshop on Creative Systems. AISB Quarterly 106, 6-7.

 

2000

Pearce, M. T.(2000). Generating Rhythmic Patterns: A Combined Neural and Evolutionary Approach . Masters Dissertation, Department of Artificial Intelligence, University of Edinburgh, UK.