2021
Clemente, A., Pearce, M. T., Skov, M., & Nadal, M. (2021). Evaluative Judgment Across Domains: Liking Balance, Curvature, Symmetry, and Complexity in Musical Motifs and Visual Designs. Brain and Cognition, 151, 105729. https://doi.org/10.1016/j.bandc.2021.105729
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., & Nadal, M. (2021). Musical aesthetic sensitivity. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication. https://doi.org/10.1037/aca0000381
2020
de Fleurian, R., & Pearce, M. T. (2020). The relationship between valence and chills in music: A corpus analysis. PsyArXiv. https://doi.org/10.31234/osf.io/v3rhe
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
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: 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
2018
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
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
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.
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
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
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.), The Oxford Handbook of Neuroaesthetics (pp. 319-338). Oxford: 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
Duffy, S., & Healey, P. G. T. (2014). The Conversational Organisation of Musical Contributions. Psychology of Music, 42(6), 888–893.
2013
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.
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). Austin, Texas: Cognitive Science Society.
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.
Song, Y., Dixon, S. & Pearce, M. (2013). Using Tags to Select Stimuli in the Study of Music and Emotion. Proceedings of the 3rd International Conference on Music and Emotion (ICME), 11-15 Jun 2013, Finland.
2012
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
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. http://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/j.neuropsychologia.2012.02.034
Pearce, M. T. & Wiggins, G. A. (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. http://doi.org/10.1037/a0027235
Song, Y., Dixon, S., & Pearce, M. T. (2012). Evaluation of Musical Features for Emotion Classification. In proceedings of the 13th International Society for Music Information Retrieval (ISMIR), 8-12 Oct 2012, Portugal.
Song, Y., Dixon, S., & Pearce, M. T. (2012). A Survey of Music Recommendation Systems and Future Perspectives. In proceedings of the 9th International Symposium on Computer Music Modelling and Retrieval (CMMR), 19-22 Jun 2012, London
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