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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/10.1016/j.cognition.2023.105405

Kaplan, T., Jamone, L., & Pearce, M. T. (2023). Probabilistic modelling of microtiming perception. Cognition, 239, 105532. https://doi.org/10.1016/j.cognition.2023.105405

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.cognition.2023.105405

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, 2022042020220420. https://doi.org/10.1016/j.cognition.2023.105405


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. In press. 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. In press.

Clemente, A., Pearce, M. T., & Nadal, M. (2022). Musical aesthetic sensitivity. Psychology of Aesthetics, Creativity and the Arts. In press. 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


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

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de Fleurian, R., & Pearce, M. T. (2020). Chills in music: An integrative review. PsyArXiv. https://doi.org/10.31234/osf.io/yc6d8

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., Hoejlund, 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. http://doi.org/10.5334/tismir.57

Quiroga-Martinez, D. R., Hansen, N. C., Hoejlund, 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


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: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198804123.013.3

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

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

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, 16(42), 20813-20814. https://doi.org/10.1073/pnas.1913244116

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

Gelding, R. W, Harrison, P. M. C., Silas, S., Johnson, B. W., Thompson, W. F., & Müllensiefen, D. (2019). Developing an efficient test of musical imagery ability: Applying modern psychometric techniques to the Pitch Imagery Arrow Task. PsyArXiv. https://doi.org/10.31234/osf.io/8gvhz

Larrouy-Maestri, P., Harrison, P. M. C., & Müllensiefen, D. (2019). The mistuning perception test: A new measurement instrument. Behavior Research Methods. https://doi.org/10.3758/s13428-019-01225-1

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

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

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., Hoejlund, 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


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. (2018). Statistics and Experimental Design for Psychologists: A model comparison approach (Book review). PsyPAG Quarterly, (108), 41-44.

Harrison, P. M. C., & Müllensiefen, D. (2018). Development and validation of the Computerised Adaptive Beat Alignment Test (CA-BAT). Scientific Reports, 8(12395), 1–19. https://doi.org/10.1038/s41598-018-30318-8

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.

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

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

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

Rohrmeier, M. & Pearce, M. T. (2018). Musical syntax I: Theoretical perspectives. In R. Bader (ed.) Springer Handbook of Systematic Musicology (pp. 473-486). Berlin: Springer-Verlag. 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). Berlin: Springer-Verlag. https://doi.org/10.1007/978-3-662-55004-5_26


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

Harrison, P. M. C. (2017). Jordan B. L. Smith, Elaine Chew, & Gérard Assayag (editors), Mathemusical conversations: Mathematics and computation in music performance and composition. Empirical Musicology Review, 12(1-2), 109-114.

Harrison, P. M. C., Collins, T., & Müllensiefen, D. (2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Scientific Reports, 7, 3618. doi:10.1038/s41598-017-03586-z

Pearce, M. T., & Eerola, T. (2017). Predictive modelling of music perception in historical audiences. Journal of Interdisciplinary Music Studies, 8, 91-120.

Eerola, T., & Pearce, M. T. (2017). Modelling historical audiences: What can be inferred? Journal of Interdisciplinary Music Studies, 8, 132-140.


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

Harrison, P. M. C., Musil, J. J., & Müllensiefen, D. (2016). Modelling melodic discrimination tests: descriptive and explanatory approaches. Journal of New Music Research, 45(3), 265-280. https://doi.org/10.1080/09298215.2016.1197953

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). Switzerland: Springer International. 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


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: Oxford University Press.

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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


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

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

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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 (ISMIR), 4-8 Nov 2013, Brazil.

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

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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