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Publications:  Dr Marcus Pearce

Ycart A, Liu L, Benetos E, Pearce M(2020). Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription. Transactions of the International Society for Music Information Retrieval vol. 3, (1) 68-81.
10.5334/tismir.57
https://qmro.qmul.ac.uk/xmlui/handle/123456789/65069
Bianco R, Harrison PMC, Hu M, Bolger C, Picken S, Pearce MT, Chait M(2020). Long-term implicit memory for sequential auditory patterns in humans. eLife vol. 9, 1-6.
10.7554/eLife.56073
https://qmro.qmul.ac.uk/xmlui/handle/123456789/64583
Quiroga-Martinez DR, Hansen NC, Højlund A, Pearce M, Brattico E, Vuust P(2020). Decomposing neural responses to melodic surprise in musicians and non-musicians: Evidence for a hierarchy of predictions in the auditory system. NeuroImage vol. 215,
10.1016/j.neuroimage.2020.116816
https://qmro.qmul.ac.uk/xmlui/handle/123456789/64996
Clemente A, Vila-Vidal M, Pearce MT, Aguiló G, Corradi G, Nadal M(2020). A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments. Behavioral Research Methods
10.3758/s13428-019-01329-8
https://qmro.qmul.ac.uk/xmlui/handle/123456789/62898
Harrison PMC, Pearce MT(2020). A computational cognitive model for the analysis and generation of voice leadings. Music Perception vol. 37, (3) 208-224.
10.1525/MP.2020.37.3.208
Quiroga-Martinez DR, Hansen NC, Højlund A, Pearce M, Brattico E, Vuust P(2020). Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians. European Journal of Neuroscience
10.1101/754333
Ycart A, Liu L, Benetos E, Pearce MT (2020). Musical Features for Automatic Music Transcription Evaluation.
Quiroga-Martinez DR, C Hansen N, Højlund A, Pearce M, Brattico E, Vuust P(2019). Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians. European Journal of Neuroscience
10.1111/ejn.14667
https://qmro.qmul.ac.uk/xmlui/handle/123456789/62663
Harrison P, Pearce M(2019). Simultaneous consonance in music perception and composition. Psychological Review
10.1037/rev0000169
https://qmro.qmul.ac.uk/xmlui/handle/123456789/59662
Pearce M, Sauvé S(2019). Information-theoretic Modeling of Perceived Musical Complexity. Music Perception
10.1525/mp.2019.37.2.165
https://qmro.qmul.ac.uk/xmlui/handle/123456789/60004
Cheung V, HARRISON PMC, Meyer L, Pearce M, Haynes J-D, Koelsch S(2019). Uncertainty and Surprise Jointly Predict Musical Pleasure and Amygdala, Hippocampus, and Auditory Cortex Activity. Current Biology
10.1016/j.cub.2019.09.067
https://qmro.qmul.ac.uk/xmlui/handle/123456789/60428
Zioga I, Harrison P, Pearce M, Bhattacharya J, Di Bernardi Luft C(2019). From learning to creativity: Identifying the behavioural and neural correlates of learning to predict human judgements of musical creativity. NeuroImage
10.1016/j.neuroimage.2019.116311
https://qmro.qmul.ac.uk/xmlui/handle/123456789/61283
Gold B, Pearce M, Mas-Herrero E, Dagher A, Zatorre RJ(2019). Predictability and uncertainty in the pleasure of music: a reward for learning?. The Journal of Neuroscience
10.1523/JNEUROSCI.0428-19.2019
https://qmro.qmul.ac.uk/xmlui/handle/123456789/60425
de Fleurian R, Harrison PMC, Pearce MT, Quiroga-Martinez DR(2019). Reward prediction tells us less than expected about musical pleasure. Proc Natl Acad Sci U S A
10.1073/pnas.1913244116
https://qmro.qmul.ac.uk/xmlui/handle/123456789/60119
Cameron DJ, Zioga I, Lindsen JP, Pearce MT, Wiggins GA, Potter K, Bhattacharya J(2019). Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms. Experimental Brain Research vol. 237, (8) 1981-1991.
10.1007/s00221-019-05557-4
https://qmro.qmul.ac.uk/xmlui/handle/123456789/58040
Quiroga-Martinez DR, Hansen NC, Højlund A, Pearce MT, Brattico E, Vuust P(2019). Reduced prediction error responses in high-as compared to low-uncertainty musical contexts. Cortex vol. 120, 181-200.
10.1016/j.cortex.2019.06.010
https://qmro.qmul.ac.uk/xmlui/handle/123456789/58784
Omigie D, Pearce M, Lehongre K, Hasboun D, Navarro V, Adam C, Samson S(2019). Intracranial Recordings and Computational Modeling of Music Reveal the Time Course of Prediction Error Signaling in Frontal and Temporal Cortices. J Cogn Neurosci vol. 31, (6) 855-873.
10.1162/jocn_a_01388
https://qmro.qmul.ac.uk/xmlui/handle/123456789/56975
Sears DRW, Pearce MT, Spitzer J, Caplin WE, McAdams S(2018). Expectations for tonal cadences: Sensory and cognitive priming effects. Q J Exp Psychol (Hove)1747021818814472-1747021818814472.
10.1177/1747021818814472
https://qmro.qmul.ac.uk/xmlui/handle/123456789/50463
Duffy S, Pearce M(2018). What makes rhythms hard to perform? An investigation using Steve Reich's Clapping Music. PLoS One vol. 13, (10) e0205847-e0205847.
10.1371/journal.pone.0205847
https://qmro.qmul.ac.uk/xmlui/handle/123456789/53547
Quiroga-Martinez DR, Hansen NC, Højlund A, Pearce M, Brattico E, Vuust P(2018). Reduced prediction error responses in high- as compared to low-uncertainty musical contexts. bioRxiv
10.1101/422949
https://qmro.qmul.ac.uk/xmlui/handle/123456789/62225
PEARCE MT(2018). Statistical Learning and Probabilistic Prediction in Music Cognition: Mechanisms of Stylistic Enculturation. Annals of the New York Academy of Sciences
10.1111/nyas.13654
https://qmro.qmul.ac.uk/xmlui/handle/123456789/38384
Harrison PMC, Pearce MT (2018). An energy-based generative sequence model for testing sensory theories of Western harmony. Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018. 160-167.
https://qmro.qmul.ac.uk/xmlui/handle/123456789/42067
Rohrmeier M, Pearce M(2018). Musical Syntax I: Theoretical Perspectives. Springer Handbooks,
Pearce M, Rohrmeier M(2018). Musical Syntax II: Empirical Perspectives. Springer Handbooks,
Sears DRW, PEARCE MT, Caplin WE, McAdams S(2017). Simulating melodic and harmonic expectations for tonal cadences using probabilistic models. Journal of New Music Research
10.1080/09298215.2017.1367010
https://qmro.qmul.ac.uk/xmlui/handle/123456789/26012
Cameron D, Potter K, Wiggins G, PEARCE MT(2017). Perception of Rhythmic Similarity is Asymmetrical, and Is Influenced by Musical Training, Expressive Performance, and Musical Context. Timing and Time Perception
10.1163/22134468-00002085
https://qmro.qmul.ac.uk/xmlui/handle/123456789/22707
van der Weij B, Pearce MT, Honing H(2017). A Probabilistic Model of Meter Perception: Simulating Enculturation. Front Psychol vol. 8, 824-824.
10.3389/fpsyg.2017.00824
https://qmro.qmul.ac.uk/xmlui/handle/123456789/24485
Pearce M, Müllensiefen D(2017). Compression-based Modelling of Musical Similarity Perception. Journal of New Music Research vol. 46, (2) 135-155.
10.1080/09298215.2017.1305419
https://qmro.qmul.ac.uk/xmlui/handle/123456789/22501
Agres K, Abdallah S, Pearce M(2017). Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory. Cognitive Science
10.1111/cogs.12477
https://qmro.qmul.ac.uk/xmlui/handle/123456789/19783
Halpern AR, Zioga I, Shankleman M, Lindsen J, Pearce MT, Bhattacharya J(2017). That note sounds wrong! Age-related effects in processing of musical expectation. Brain Cogn vol. 113, 1-9.
10.1016/j.bandc.2016.12.006
https://qmro.qmul.ac.uk/xmlui/handle/123456789/25765
Hansen NC, Vuust P, Pearce M(2016). "If You Have to Ask, You'll Never Know": Effects of Specialised Stylistic Expertise on Predictive Processing of Music. PLOS ONE vol. 11, (10) e0163584-e0163584.
10.1371/journal.pone.0163584
https://qmro.qmul.ac.uk/xmlui/handle/123456789/17664
Dean RT, Pearce MT(2016). Algorithmically-generated Corpora that use Serial Compositional Principles Can Contribute to the Modeling of Sequential Pitch Structure in Non-tonal Music. Empirical Musicology Review vol. 11, (1) 27-27.
10.18061/emr.v11i1.4900
https://qmro.qmul.ac.uk/xmlui/handle/123456789/17671
Hansen NC, Sadakata M, Pearce M(2016). Nonlinear Changes in the Rhythm of European Art Music: Quantitative Support for Historical Musicology. Music Perception: An Interdisciplinary Journal vol. 33, (4) 414-431.
10.1525/mp.2016.33.4.414
https://qmro.qmul.ac.uk/xmlui/handle/123456789/17691
Gingras B, Pearce MT, Goodchild M, Dean RT, Wiggins G, McAdams S(2016). Linking melodic expectation to expressive performance timing and perceived musical tension. Journal of Experimental Psychology: Human Perception and Performance vol. 42, (4) 594-609.
10.1037/xhp0000141
https://qmro.qmul.ac.uk/xmlui/handle/123456789/10881
Pearce MT, Zaidel DW, Vartanian O, Skov M, Leder H, Chatterjee A, Nadal M(2016). Neuroaesthetics: The Cognitive Neuroscience of Aesthetic Experience. Perspectives on Psychological Science vol. 11, (2) 265-279.
10.1177/1745691615621274
https://qmro.qmul.ac.uk/xmlui/handle/123456789/12033
Barascud N, Pearce MT, Griffiths TD, Friston KJ, Chait M(2016). Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. Proceedings of the National Academy of Sciences of the United States of America vol. 113, (5) E616-E625.
10.1073/pnas.1508523113
https://qmro.qmul.ac.uk/xmlui/handle/123456789/12031
PEARCE MT, Schubert E(2016). A New Look at Musical Expectancy: The Veridical Versus the General in the Mental Organization of Music. Music, Mind, and Embodiment, Editors: Kronland-Martinet, R, Aramaki, M, Ystad, S, Springer
https://qmro.qmul.ac.uk/xmlui/handle/123456789/18233
Song Y, Dixon S, Pearce MT, Halpern AR(2016). Perceived and Induced Emotion Responses to Popular Music: Categorical and Dimensional Models. Music Perception vol. 33, (4) 472-492.
10.1525/MP.2016.33.4.472
https://qmro.qmul.ac.uk/xmlui/handle/123456789/11214
Carey D, Rosen S, Krishnan S, Pearce MT, Shepherd A, Aydelott J, Dick F(2015). Generality and specificity in the effects of musical expertise on perception and cognition. Cognition vol. 137, 81-105.
10.1016/j.cognition.2014.12.005
https://qmro.qmul.ac.uk/xmlui/handle/123456789/6790
Pearce MT, Halpern AR(2015). Age-related patterns in emotions evoked by music. Psychology of Aesthetics, Creativity, and the Arts vol. 9, (3) 248-253.
10.1037/a0039279
Song C, Pearce M, Harte C (2015). Synpy: A python toolkit for syncopation modelling. Proceedings of the 12th International Conference in Sound and Music Computing, SMC 2015. 295-300.
Hansen NC, Pearce MT(2014). Predictive uncertainty in auditory sequence processing. Front Psychol vol. 5,
10.3389/fpsyg.2014.01052
https://qmro.qmul.ac.uk/xmlui/handle/123456789/6446
Whorley R, Wiggins GA, Rhodes CS, Pearce MT(2013). Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints. Journal of New Musical Research
10.1080/09298215.2013.831457
https://qmro.qmul.ac.uk/xmlui/handle/123456789/10883
Bailes F, Dean RT, Pearce MT(2013). Music cognition as mental time travel. Scientific Reports vol. 3,
10.1038/srep02690
https://qmro.qmul.ac.uk/xmlui/handle/123456789/64997
Omigie D, Pearce MT, Williamson VJ, Stewart L(2013). Electrophysiological correlates of melodic processing in congenital amusia. NEUROPSYCHOLOGIA vol. 51, (9) 1749-1762.
10.1016/j.neuropsychologia.2013.05.010
Cherla S, Weyde T, d¿Avila Garcez A, Pearce M (2013). A distributed model for multiple-viewpoint melodic prediction. Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013. 15-20.
Agres K, Abdallah S, Pearce MT (2013). An Information-Theoretic Account of Musical Expectation and Memory. CogSci. Editors: Knauff, M, Pauen, M, Sebanz, N, Wachsmuth, I et al.,
Song Y, Dixon S, Pearce M, Halpern A (2013). Do online social tags predict perceived or induced emotional responses to music?. Proceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013. 89-94.
Omigie D, Pearce MT, Stewart L, Williamson VJ(2013). Electrophysiological correlates of melodic processing in congenital amusia. Neuropsychologia
10.1016/j.neuropsychologia.2013.05.010.
Whorley R, Rhodes C, Wiggins G, Pearce M (2013). Harmonising melodies: Why do we add the bass line first?. Proceedings of the 4th International Conference on Computational Creativity, ICCC 2013. 79-86.
Egermann H, Pearce MT, Wiggins GA, McAdams S(2013). Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. Cognitive, Affective and Behavioral Neuroscience vol. 13, (3) 533-553.
10.3758/s13415-013-0161-y
https://qmro.qmul.ac.uk/xmlui/handle/123456789/12125
Song C, Simpson AJR, Harte CA, Pearce MT, Sandler MB(2013). Syncopation and the score. PLoS One vol. 8, (9)
10.1371/journal.pone.0074692
https://qmro.qmul.ac.uk/xmlui/handle/123456789/10967
Brattico E, Pearce MT(2013). The Neuroaesthetics of Music. Psychology of Aesthetics, Creativity and the Arts vol. 7, (1) 48-61.
10.1037/a0031624
Pearce M, Rohrmeier M(2012). Music Cognition and the Cognitive Sciences. Topics in Cognitive Science vol. 4, (4) 468-484.
10.1111/j.1756-8765.2012.01226.x
Pearce MT, Wiggins GA(2012). Auditory Expectation: The Information Dynamics of Music Perception and Cognition. Topics in Cognitive Science vol. 4, (4) 625-652.
10.1111/j.1756-8765.2012.01214
Pearce MT, Christensen JF(2012). Conference Report: The Neurosciences and Music - IV - Learning and Memory. Psychomusicology vol. 22, (1) 70-73.
10.1037/a0027235
Song Y, Dixon S, Pearce M (2012). Evaluation of musical features for emotion classification. Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012. 523-528.
Carrus E, Pearce MT, Bhattacharya J(2012). Melodic pitch expectation interacts with neural responses to syntactic but not semantic violations. Cortex vol. 49, (8) 2186-2200.
10.1016/j.cortex.2012.08.024
Omigie D, Pearce MT, Stewart L(2012). Tracking of pitch probabilities in congenital amusia. Neuropsychologia vol. 50, (7) 1483-1493.
10.1016/j.neuropsychologia.2012.02.034
Pearce MT, Müllensiefen D, Wiggins GA(2010). The role of expectation and probabilistic learning in auditory boundary perception: a model comparison. Perception vol. 39, (10) 1365-1389.
10.1068/p6507
Pearce MT, Müllensiefen D, Wiggins GA(2010). Melodic grouping in music information retrieval: New methods and applications. Studies in Computational Intelligence vol. 274, 365-389.
10.1007/978-3-642-11674-2-16
Whorley R, Wiggins GA, Rhodes C, Pearce MT (2010). Development of Techniques for the Computational Modelling of Harmony. Proceedings of the First International Conference on Computational Creativity. Editors: Ventura, others,
Pearce MT, Müllensiefen D, Wiggins GA(2010). Melodic Grouping in Music Information Retrieval: New Methods and Applications. Advances in Music Information Retrieval, Editors: Ras, Z, Wieczorkowska, A, vol. 274, Springer (Berlin/Heidelberg),
Wiggins GA, Müllensiefen D, Pearce MT(2010). On the non-existence of Music: Why music theory is a figment of the imagination. Musicae Scientiae vol. Discussion Forum 5, 231-255.
10.1177/10298649100140S110
Nadal M, Pearce MT(2010). The Copenhagen Neuroaesthetics conference: Prospects and pitfalls for an emerging field. Brain and Cognition vol. 76, 172-183.
10.1016/j.bandc.2011.01.009
Pearce MT, Müllensiefen D, Wiggins GA(2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception vol. 39, (10) 1367-1391.
10.1068/p6507
Pearce MT, Herrojo Ruiz M, Kapasi S, Wiggins GA, Bhattacharya J(2010). Unsupervised Statistical Learning Underpins Computational, Behavioural and Neural Manifestations of Musical Expectation. NeuroImage vol. 50, Article 1, 303-313.
10.1016/j.neuroimage.2009.12.019
Pearce MT, Ruiz MH, Kapasi S, Wiggins GA, Bhattacharya J(2010). Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation. NeuroImage vol. 50, 302-313.
Wiggins GA, Pearce MT, Müllensiefen D(2009). Computational Modelling of Music Cognition and Musical Creativity. Oxford Handbook of Computer Music, Editors: Dean, R, Oxford University Press
Rohrmeier M, Honing H, Rebuschat P, Loui P, Wiggins G, Pearce MT, Müllensiefen D (2009). Music Cognition: Learning and Processing. Proceedings of the 31st Annual Conference of the Cognitive Science Society. Editors: Taatgen, NA, Rijn, HV, 41-42.
Pearce MT(2009). To beep or not to beep. Contemporary Music Review vol. 28, Article 1, 125-126-125-126.
Pearce MT, Müllensiefen D, Wiggins GA (2008). A comparison of statistical and rule-based models of melodic segmentation. Proceedings of the Ninth International Conference on Music Information Retrieval. Editors: Bello, JP, Chew, E, 89-94.
Pearce MT, Müllensiefen D(2008). David Huron, Sweet Anticipation: Music and the Psychology of Expectation. Cambridge, Massachusetts: MIT Press, 2007, 512 pp., ISBN 0262083450, (Hardcover). Musicæ Scientiæ vol. 12, Article 1,
Pearce MT, Müllensiefen D, Lewis D, Rhodes CS(2007). David Temperley, Music and Probability. Cambridge, Massachusetts: MIT Press, 2007, ISBN-13: 978-0-262-20166-7 (hardcover) $40.00. Empirical Musicology Review vol. 2, Article 4, 155-163.
Pearce MT, Wiggins GA (2007). Evaluating cognitive models of musical composition. Proceedings of the 4th International Joint Workshop on Computational Creativity. Editors: Cardoso, A, Wiggins, GA, 73-80.
Whorley RP, Wiggins GA, Pearce MT (2007). Systematic Evaluation and Improvement of Statistical Models of Harmony. Proceedings of the 4th International Joint Workshop on Computational Creativity. Editors: Cardoso, A, Wiggins, GA, 81-88.
Potter K, Wiggins GA, Pearce MT(2007). Towards greater objectivity in music theory: Information-dynamic analysis of minimalist music. Musicæ Scientiæ vol. 11, Article 2, 295-322.
10.1177/102986490701100207
Pearce MT, Wiggins GA(2006). Expectation in Melody: The Influence of Context and Learning. Music Perception vol. 23, Article 5, 377-405.
10.1525/mp.2006.23.5.377
Pearce MT, Wiggins GA (2006). The information dynamics of melodic boundary detection. Proceedings of the Ninth International Conference of Music Perception and Cognition. Editors: Baroni, M, Addessi, AR, Caterina, R, Costa, M et al., 860-867.
Pearce MT, Conklin D, Wiggins GA(2005). Methods for Combining Statistical Models of Music. Computer Music Modelling and Retrieval, Editors: Wiil, UK, Springer Verlag (Heidelberg, Germany),
Pearce MT (2005). The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition. Notes: date-modified: 2012-02-29 16:11:06 +0000 bdsk-url-1: http://www.doc.gold.ac.uk/ mas01mtp/papers/Pearce2005.pdf ,
Pearce MT, Wiggins GA(2004). Improved Methods for Statistical Modelling of Monophonic Music. Journal of New Music Research vol. 33, Article 4, 367-385.
10.1080/0929821052000343840
Pearce MT, Wiggins GA (2004). Rethinking Gestalt Influences on Melodic Expectancy. Proceedings of the Eighth International Conference of Music Perception and Cognition. Editors: Lipscomb, SD, Ashley, R, Gjerdingen, RO, Webster, P et al., 367-371.
Pearce MT, Meredith D(2004). Review of the Third International Symposium on Computer Music Modelling and Retrieval. Computer Music Journal vol. 28, Article 4, 91-93.
Pearce MT, Wiggins GA (2003). An empirical comparison of the performance of PPM variants on a prediction task with monphonic music. Proceedings of the AISB’03 Symposium on Artificial Intelligence and Creativity in Arts and Science. 74-83.
Pearce MT, Wiggins GA (2002). Aspects of a cognitive theory of creativity in musical composition. Proceedings of the ECAI’02 Workshop on Creative Systems. 17-24.
Pearce M, Wiggins GA, Meredith D(2001). Motivations and Methodologies for Automation of the Compositional Process. Musicae Scientiae vol. 6, Article 2, 119-147.
10.1177/102986490200600203
Pearce MT(2001). Report on the ICCBR’01 Workshop on Creative Systems. AISB Quarterly vol. 102, 6-7.
Pearce MT, Wiggins GA (2001). Towards a framework for the evaluation of machine compositions. Proceedings of the AISB’01 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences. 22-32.
Pearce MT (2000). Generating Rhythmic Patterns: A Combined Neural and Evolutionary Approach. Notes: date-modified: 2012-02-29 16:11:06 +0000 ,
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