Marcus Pearce

Full list: https://scholar.google.co.uk/citations?user=UImWMekAAAAJ

2024

• Bonetti, L., Stevner, A., Deco, G., Whybrow, P. C., Pearce, M. T., Pantazis, D., Vuust, P., & Kringelbach, M. L. (2024). Spatiotemporal whole-brain activity and functional connectivity of melodies recognition. Cerebral Cortex, 34, bhae320. https://doi.org/10.1093/cercor/bhae320
• Clemente, A., Kaplan, T. M., & Pearce, M. T. (2024). Perceptual representations mediate effects of stimulus properties on liking for music. Annals of the New York Academy of Sciences, 1533, 169-180. https://doi.org/10.1111/nyas.15106
• Hamilton, M., & Pearce, M. T. (2024). Trajectories and revolutions in popular melody based on U.S. charts from 1950 to 2023. Scientific Reports, 14, 14749. https://doi.org/10.1038/s41598-024-64571-x
• Hamilton, M., Clemente, A., Hall, E. T. R., & Pearce, M. T. (2024). The Billboard melodic music dataset (BiMMuDa). Transactions of the International Society for Music Information Retrieval, 7(1), 113– 128. https://doi.org/10.5334/tismir.168
• Reed, C., Pearce, M. T., & McPherson, A. (2024). Auditory imagery ability influences accuracy when singing with altered auditory feedback. Musicae Scientiae, in press. https://doi.org/10.1177/10298649231223077
• van der Weij, B., Pearce, M. T., & Honing, H. (2024). Computational modelling of rhythm perception and the role of enculturation. In D. Shanahan, J. A. Burgoyne, & I. Quinn (Eds.) Oxford Handbook of Music and Corpus Studies. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190945442.013.13
• Zioga, I., Harrison, P. M. C., Pearce, M. T., Bhattacharya, J., Di, C., & Luft, B. (2024). The association between liking, learning and creativity in music. Scientific Reports, 14, 19048. https://doi.org/10.1038/s41598-024-70027-z

2023

• Pearce, M. T. (2023). Music Perception. In Oxford Research Encyclopedia of Psychology. https://doi.org/10.1093/acrefore/9780190236557.013.890
• Klarlund, M., Brattico, E., Pearce, M. T., Wu, Y., Vuust, P., Overgaard, M., & Du, Y. (2023). Worlds apart? Testing the cultural distance hypothesis in music perception of Chinese and Western listeners. Cognition, 235, 105405. https://doi.org/10.1016/j.cognition.2023.105405
• Gold, B. P., Pearce, M. T., McIntosh, A. R., Chang, C., Dagher, A., & Zatorre, R. J. (2023). Auditory and reward structures reflect the pleasure of musical expectancies during naturalistic listening. Frontiers in Neuroscience, 17, 1209398. https://doi.org/10.3389/fnins.2023.1209398
• Kaplan, T., Jamone, L., & Pearce, M. T. (2023). Probabilistic modelling of microtiming perception. Cognition, 239, 105532. https://doi.org/10.1016/j.cognition.2023.105532
• Bianco, R., Hall, E. T., Pearce, M. T., & Chait, M. (2023). Implicit auditory memory in older listeners: From encoding to 6-month retention. Current Research in Neurobiology, 100115. https://doi.org/10.1016/j.crneur.2023.100115
• Cheung, V., Harrison, P., Koelsch, S., Pearce, M., Friederici, A., & Meyer, L. (2023). Cognitive and sensory expectations independently shape musical expectancy and pleasure. Philosophical Transactions of the Royal Society B: Biological Sciences, 379, 20220420. https://doi.org/10.1098/rstb.2022.0420

2022

• Krishnan, S., Carey, D., Dick, F., & Pearce, M. T. (2022). Effects of statistical learning in passive and active contexts on reproduction and recognition of auditory sequences. Journal of Experimental Psychology: General, 151, 555-577. https://doi.org/10.1037/xge0001091
• Tenderini, M. S., de Leeuw, E., Eilola, T. M., & Pearce, M. T. (2022). Reduced cross-modal affective priming in the L2 of late bilinguals depends on L2 exposure. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48, 284-303. https://doi.org/10.1037/xlm0000889
• Clemente, A., Pearce, M. T., & Nadal, M. (2022). Musical aesthetic sensitivity. Psychology of Aesthetics, Creativity and the Arts, 16, 58-73. https://doi.org/10.1037/aca0000381
• Agres, K., Tay, T. Y., & Pearce, M. T. (2022). Comparing Musicians and Non-musicians’ Expectations in Music and Vision. In Proceedings of the 17th International Audio Mostly Conference (pp. 74-79). https://doi.org/10.1145/3561212.3561251
• Hansen, N. C., Højlund, A., Møller, C., Pearce, M. T., & Vuust, P. (2022). Musicians show more integrated neural processing of contextually relevant acoustic features. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.907540
• Kaplan, T., Cannon, J., Jamone, L., & Pearce, M. T. (2022). Modeling enculturated bias in entrainment to rhythmic patterns. PLOS Computational Biology, 18(9), e1010579. https://doi.org/10.1371/journal.pcbi.1010579

2021

• de Fleurian, R., & Pearce, M. T. (2021). Chills in Music: A systematic review. Psychological Bulletin, 147, 890-920. https://doi.org/10.1037/bul0000341
• Hansen, N.C., Kragness, H., Vuust, P., Trainor, L., & Pearce, M. T. (2021). Predictive uncertainty underlies auditory boundary perception. Psychological Science, 32, 1416-1425. https://doi.org/10.1177/0956797621997349
• Politimou, N., Douglass-Kirk, P., Pearce, M. T., Stewart, L., & Franco, F. (2021). Melodic expectations in 5- to 6-year-old children. Journal of Experimental Child Psychology, 203, 105020. https://doi.org/10.1016/j.jecp.2020.105020
• Clemente, A., Pearce, M. T., Skov, M., & Nadal, M. (2021). Evaluative judgment across domains: Liking balance, contour, symmetry and complexity in melodies and visual designs. Brain and Cognition, 151, 105729. https://doi.org/10.1016/j.bandc.2021.105729
• Hall, E. & Pearce, M. T. (2021). A model of large-scale thematic structure. Journal of New Music Research, 50, 220-241. https://doi.org/10.1080/09298215.2021.1930062
• Goldman, A., Harrison, P. M. C., Jackson, T. & Pearce, M. T. (2021). Reassessing syntax-related ERP components using popular music chord sequences: A model-based approach. Music Perception, 39, 118-144. https://doi.org/10.1525/mp.2021.39.2.118
• de Fleurian, R., & Pearce, M. T. (2021). The relationship between valence and chills in music: A corpus analysis. i-Perception, 12, 1-11. https://doi.org/10.1177/20416695211024680
• Nadal, M., & Pearce, M. T. (2021). The cognitive neuroscience of aesthetic experience. In A. Chatterjee & E. R. Cardillo (Eds.) Brain, beauty and art. Oxford University Press. https://doi.org/10.1093/oso/9780197513620.003.0008
• Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M. T., Brattico, E., & Vuust, P. (2021). Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection. Human Brain Mapping, 42, 5595-5608. https://doi.org/10.1002/hbm.25638

2020

• Harrison, P. M. C., & Pearce, M. T. (2020). Simultaneous consonance in music perception and composition. Psychological Review, 127, 216-244. https://doi.org/10.1037/rev0000169
• Zioga, I., Harrison, P. M. C., Pearce, M. T., Bhattacharya, J., & Luft, C. D. (2020). From learning to creativity: Identifying the behavioural and neural correlates of learning to predict human judgements of musical creativity. NeuroImage, 206, 116311. https://doi.org/10.1016/j.neuroimage.2019.116311
• Bianco, R., Harrison, P. M. C., Hu, M., Bolger, C., Picken, S., Pearce, M. T., & Chait, M. (2020). Long-term implicit memory for sequential auditory patterns in humans. Elife, 9, e56073. https://doi.org/10.7554/eLife.56073
• Harrison, P. M. C., & Pearce, M. T. (2020). A computational cognitive model for the analysis and generation of voice leadings. Music Perception, 37, 208-224. https://doi.org/10.1525/mp.2020.37.3.208
• Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M. T., Brattico, E., & Vuust, P. (2020). Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non‐musicians. European Journal of Neuroscience, 51, 2200-2269. https://doi.org/10.1111/ejn.14667
• Ycart, A., Liu, L., Benetos, E., & Pearce, M. T. (2020). Investigating the perceptual validity of evaluation metrics for automatic piano music transcription. Transactions of the International Society for Music Information Retrieval, 3, 68-81. https://doi.org/10.5334/tismir.57
• Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M. T., 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, 215, 116816. https://doi.org/10.1016/j.neuroimage.2020.116816
• Clemente, A., Vila-Vidal, M, Pearce, M. T., Aguiló, G., Corradi, C., & Nadal, M. (2020). A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments. Behavior Research Methods, 52, 1491–1509. https://doi.org/10.3758/s13428-019-01329-8
• Zioga, I., Harrison, P. M. C., Pearce, M. T., Bhattacharya, J., & Luft, C. D. B. (2020). Auditory but Not Audiovisual Cues Lead to Higher Neural Sensitivity to the Statistical Regularities of an Unfamiliar Musical Style. Journal of Cognitive Neuroscience, 32, 2241-2259. https://doi.org/10.1162/jocn_a_01614
• Harrison, P. M. C., Bianco, R., Chait, M., & Pearce, M. T. (2020). PPM-Decay: A computational model of auditory prediction with memory decay. PLOS Computational Biology, 16(11), e1008304. https://doi.org/10.1371/journal.pcbi.1008304

2019

• Morrison, S. J., Demorest, S. M., & Pearce, M. T. (2019). Cultural Distance: A Computational Approach to Exploring Cultural Influences on Music Cognition. In M. Thaut and D. Hodges (eds.), Oxford Handbook of Music and the Brain (pp. 42-65). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198804123.013.3
• Sears, D. R. W., Pearce, M. T., Spitzer, J., Caplin, W. E., & McAdams, S. (2019). Expectations for tonal cadences: Sensory and cognitive priming effects. Quarterly Journal of Experimental Psychology, 72, 1422-1438. https://doi.org/10.1177/1747021818814472
• Omigie, D., Pearce, M. T., 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. Journal of Cognitive Neuroscience, 31, 855-873. https://doi.org/10.1162/jocn_a_01388
• Cameron, D. J., Zioga, I., Lindsen, J. P., Pearce, M. T., Wiggins, G., Potter, K., & Bhattacharya, J. (2019). Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms. Experimental Brain Research, 237, 1981-1991. https://doi.org/10.1007/s00221-019-05557-4
• Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M. T., Brattico, E., & Vuust, P. (2019). Reduced prediction error responses in high-as compared to low-uncertainty musical contexts. Cortex, 120, 181-200. https://doi.org/10.1016/j.cortex.2019.06.010
• de Fleurian, R., Harrison, P. M., Pearce, M. T., & Quiroga-Martinez, D. R. (2019). Reward prediction tells us less than expected about musical pleasure. Proceedings of the National Academy of Sciences, 116(42), 20813-20814. https://doi.org/10.1073/pnas.1913244116
• Gold, B., Pearce, M. T., Mas-Herrero, E., Dagher, A., & Zatorre, R. J. (2019). Predictability and uncertainty in the pleasure of music: a reward for learning? Journal of Neuroscience, 39(47), 9397-9409. https://doi.org/10.1523/JNEUROSCI.0428-19.2019
• Cheung, V., Harrison, P. M. C., Meyer, L., Pearce, M. T., Haynes, J-D, & Koelsch, S. (2019). Uncertainty and surprise jointly predict musical pleasure and amygdala, hippocampus, and auditory cortex activity. Current Biology, 29(23), 4084-4092.e4. https://doi.org/10.1016/j.cub.2019.09.067
• Sauvé, S. & Pearce, M. T. (2019). Information-theoretic modelling of perceived musical complexity. Music Perception, 37, 165-178. https://doi.org/10.1525/mp.2019.37.2.165

2018

• Pearce, M. T. (2018). Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation. Annals of the New York Academy of Sciences, 1423, 378-395. https://doi.org/10.1111/nyas.13654
• Agres, K., Abdallah, S., & Pearce, M. T. (2018). Information-theoretic properties of auditory sequences dynamically influence expectation and memory. Cognitive Science, 42, 43-76. https://doi.org/10.1111/cogs.12477
• Sears, D., Pearce, M. T., Caplin, W. E., & McAdams, S. (2018). Simulating melodic and harmonic expectations for tonal cadences using probabilistic models. Journal of New Music Research, 47, 29-52. https://doi.org/10.1080/09298215.2017.1367010
• Sauvé, S.A., Sayed, A., Dean, R. T., & Pearce, M. T. (2018). Effects of pitch and timing expectancy on musical emotion. Psychomusicology, 28, 17-39. https://doi.org/10.1037/pmu0000203
• Rohrmeier, M. & Pearce, M. T. (2018). Musical syntax I: Theoretical perspectives. In R. Bader (ed.) Springer Handbook of Systematic Musicology (pp. 473-486). Springer. https://doi.org/10.1007/978-3-662-55004-5_25
• Pearce, M. T. & Rohrmeier, M. (2018). Musical syntax II: Empirical perspectives. In R. Bader (ed.) Springer Handbook of Systematic Musicology (pp. 487-505). Springer. https://doi.org/10.1007/978-3-662-55004-5_26
• Duffy, S. & Pearce, M. T. (2018). What makes rhythms hard to perform? An investigation using Steve Reich’s Clapping Music. PLOS One, 13(10): e0205847. https://doi.org/10.1371/journal.pone.0205847
• Harrison, P. M. C., & Pearce, M. T. (2018). Dissociating sensory and cognitive theories of harmony perception through computational modeling. In Proceedings of ICMPC15/ESCOM10, 23-28 July, Graz, Austria.
• Harrison, P. M. C., & Pearce, M. T. (2018). An energy-based generative sequence model for testing sensory theories of Western harmony. In Proceedings of the 19th International Society for Music Information Retrieval Conference, September 23-27, Paris, France.

2017

• Pearce, M. T. & Müllensiefen, D. (2017). Compression-based Modelling of Musical Similarity Perception. Journal of New Music Research, 46, 135-155. https://doi.org/10.1080/09298215.2017.1305419
• Van der Weij, B., Pearce, M. T., & Honing H. (2017). A probabilistic model of meter perception: simulating enculturation. Frontiers in Psychology, 8, 824. https://doi.org/10.3389/fpsyg.2017.00824
• Cameron, D., Potter, K., Wiggins, G., & Pearce, M. T. (2017). Perception of rhythmic similarity is asymmetrical, and is influenced by musical training, expressive performance, and musical context. Timing and Time Perception, 5, 211-227. https://doi.org/10.1163/22134468-00002085
• Halpern, A., Zioga, I., Shankleman, M., Lindsen, J., Pearce, M. T., & Bhattacharya, J. (2017). That note sounds wrong! Age-related effects in processing of musical expectation. Brain and Cognition, 113, 1-9. https://doi.org/10.1016/j.bandc.2016.12.006
• Pearce, M. T., & Eerola, T. (2017). Predictive modelling of music perception in historical audiences. Journal of Interdisciplinary Music Studies, 8, 91-120. https://doi.org/10.4407/jims.2016.12.004
• Eerola, T., & Pearce, M. T. (2017). Modelling historical audiences: What can be inferred? Journal of Interdisciplinary Music Studies, 8, 132-140.https://doi.org/10.4407/jims.2016.12.004

2016

• Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016). Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. Proceedings of the National Academy of Sciences, 113, E616-E625. https://doi.org/10.1073/pnas.1508523113
• Pearce, M. T., Zaidel, D. W., Vartanian, O., Skov, M., Leder, M., Chatterjee, A., & Nadal, M. (2016). Neuroaesthetics: the cognitive neuroscience of aesthetic experience. Perspectives in Psychological Science, 11, 265-279. https://doi.org/10.1177/1745691615621274
• Gingras, B., Pearce, M. T., Goodchild, M., Dean, R. T., Wiggins, G., & McAdams, S. (2016). Linking melodic expectation to expressive performance timing and perceived musical tension. Journal of Experimental Psychology: Human Perception and Performance, 42, 594-609. https://doi.org/10.1037/xhp0000141
• Hansen, N. C., Vuust, P. & Pearce, M. T. (2016). "If you have to ask, you'll never know": Effects of specialised stylistic expertise on predictive processing of music. PLOS One, 11(10), e0163584. https://doi.org/10.1371/journal.pone.0163584
• Song, Y., Dixon, S., Pearce, M. T., & Halpern, A. (2016). Perceived and induced emotion responses to popular music: Categorical and Dimensional Models. Music Perception, 33, 472-492. https://doi.org/10.1525/mp.2016.33.4.472
• Dean, R.T. & Pearce, M. T. (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, 11, 27–46. https://doi.org/10.18061/emr.v11i1.4900
• Schubert, E. & Pearce, M. T. (2016). A new look at musical expectancy: The veridical versus the general in the mental organization of music. In R. Kronland-Martinet, M. Aramaki, and S. Ystad (eds.), Music, Mind and Embodiment (pp. 358–370). Springer. https://doi.org/10.1007/978-3-319-46282-0_23
• Hansen, N. C., Sadakata, M., & Pearce, M. T. (2016). Nonlinear changes in the rhythm of European art music: Quantitative support for historical musicology. Music Perception, 33, 414-431. https://doi.org/10.1525/mp.2016.33.4.414

2015

• Pearce, M. T. (2015). Effects of expertise on the cognitive and neural processes involved in musical appreciation. In J.P. Huston, M. Nadal, L. Agnati, F. Mora, and C.J. Cela-Conde (eds.), Art, Aesthetics and the Brain (pp. 319-338). Oxford University Press.
• Pearce, M. T. & Halpern, A. R. (2015). Age-related patterns in emotions evoked by music. Psychology of Aesthetics, Creativity and the Arts, 9, 248-253. https://doi.org/10.1037/a0039279
• Carey, D., Rosen, S., Krishnan, S., Pearce, M.T., Shepherd, A., Aydelott, J., & Dick, F. (2015). Generality and specificity in the effects of musical expertise on perception and cognition. Cognition, 137, 81-105. https://doi.org/10.1016/j.cognition.2014.12.005

2014

• Hansen, N. C. & Pearce M. T. (2014). Predictive uncertainty in auditory sequence processing. Frontiers in Psychology, 5, 1052. https://doi.org/10.3389/fpsyg.2014.01052

2013

• Brattico, E. & Pearce, M. T. (2013). The neuroaesthetics of music. Psychology of Aesthetics, Creativity and the Arts, 7, 48-61. https://doi.org/10.1037/a0031624
• Omigie, D., Pearce, M. T., Williamson, V., & Stewart, L. (2013). Electrophysiological correlates of melodic processing in congenital amusia. Neuropsychologia, 51, 1749-1762. https://doi.org/10.1016/j.neuropsychologia.2013.05.010
• Egermann, H., Pearce, M. T., Wiggins, G., & McAdams, S. (2013). Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. Cognitive, Affective and Behavioural Neuroscience, 13, 533-553. https://doi.org/10.3758/s13415-013-0161-y
• Bailes, F., Dean, R. T., & Pearce M. T. (2013). Music cognition as mental time travel. Scientific Reports, 3, 2690. https://doi.org/10.1038/srep02690
• 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. https://doi.org/10.1371/journal.pone.0074692
• 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. https://doi.org/10.1080/09298215.2013.831457
• Cherla, S., Weyde, T., Garcez, A. d'Avila & Pearce, M. T. (2013). Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction. Paper presented at the 14th International Society for Music Information Retrieval Conference, 4 - 8 Nov 2013, Curitiba, PR, Brazil.
• Agres, K., Abdallah, S., & Pearce, M. T. (2013). An Information-Theoretic Account of Musical Expectation and Memory. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 127-132). Cognitive Science Society.

2012

• Carrus, E., Pearce, M. T., & Bhattacharya, J. (2012). Melodic pitch expectation interacts with neural responses to syntactic but not semantic violations. Cortex, 49, 2186-2200. https://doi.org/10.1016/j.cortex.2012.08.024
• Pearce, M. T. & Rohrmeier, M. (2012). Music cognition and the cognitive sciences. TopiCS in Cognitive Science, 4, 468-484. https://doi.org/10.1111/j.1756-8765.2012.01226.x
• 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. https://doi.org/10.1037/a0031229
• Omigie, D., Pearce, M. T., & Stewart, L. (2012). Tracking of pitch probabilities in congenital amusia. Neuropsychologia, 50, 1483-1493. https://doi.org/10.1016/j.neuropsychologia.2012.02.034
• Pearce, M. T. & Wiggins, G. (2012). Auditory expectation: The information dynamics of music perception and cognition. TopiCS in Cognitive Science, 4, 625-652. https://doi.org/10.1111/j.1756-8765.2012.01214.x
• Pearce, M. T., & Christensen, J.F. (2012). Conference Report: The Neurosciences and Music - IV - Learning and Memory. Psychomusicology, 22, 70-73. https://doi.org/10.1037/a0027235

2011

• Pearce, M. T. (2011). Time-series analysis of Music: Perceptual and Information Dynamics. Empirical Musicology Review, 6, 125-130. https://doi.org/10.18061/1811/51214
• Nadal, M., & Pearce, M. T. (2011). The Copenhagen Neuroaesthetics conference: Prospects and pitfalls for an emerging field. Brain and Cognition, 76, 172-183. https://doi.org/10.1016/j.bandc.2011.01.009

2010

• Pearce, M. T., Müllensiefen, D., & Wiggins, G. (2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception, 39, 1367-1391. https://doi.org/10.1068/p6507
• Pearce, M. T., Ruiz, M. H., Kapasi, S., Wiggins, G., & Bhattacharya, J. (2010). Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation. NeuroImage, 50, 302-313. https://doi.org/10.1016/j.neuroimage.2009.12.019
• Wiggins, G., 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. https://doi.org/10.1177/10298649100140S110
• Pearce, M. T., Müllensiefen, D., & Wiggins, G. (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). Springer. https://doi.org/10.1007/978-3-642-11674-2_16
• Whorley, R., Wiggins,G., 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. https://doi.org/10.1080/07494460802664080
• Wiggins, G., 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 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). Cognitive Science Society.

2008

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

2007

• Potter, K., Wiggins, G., & Pearce, M. T. (2007). Towards greater objectivity in music theory: Information-dynamic analysis of minimalist music. Musicae Scientiae, 11, 295-322. https://doi.org/10.1177/102986490701100207
• Pearce, M. T. & Wiggins, G. (2007). Evaluating cognitive models of musical composition. In A. Cardoso and G. Wiggins (Eds.), Proceedings of the 4th International Joint Workshop on Computational Creativity, (pp. 73-80). Goldsmiths, University of London.
• Pearce, M. T., Müllensiefen, D., Lewis, D. & Rhodes, C. S. (2007). David Temperley, Music and Probability. 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. (2006). Expectation in melody: The influence of context and learning. Music Perception, 23, 377-405. https://doi.org/10.1525/mp.2006.23.5.377
• Pearce, M. T. & Wiggins, G. (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 Computer Science, City University of London, UK. Examiners: Petri Toiviainen and Artur d'Avila Garcez
• Pearce, M. T., Conklin, D. & Wiggins, G. (2005). Methods for combining statistical models of music. In U. K. Wiil (Ed.), Computer Music Modelling and Retrieval (pp. 295-312). Springer.

2004

• Pearce, M. T. & Wiggins, G. (2004). Improved methods for statistical modelling of monophonic music. Journal of New Music Research, 33, 367-385. https://doi.org/10.1080/0929821052000343840
• Pearce, M. T. & Wiggins, G. (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). 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. (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). SSAISB.

2002

• Pearce, M. T., Meredith, D. & Wiggins, G. (2002). Motivations and methodologies for automation of the compositional process. Musicae Scientiae, 6, 119-147. https://doi.org/10.1177/102986490200600203
• Pearce, M. T. & Wiggins, G. (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. (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). 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.