Dr Marcus Pearce
Senior Lecturer
Email: marcus.pearce@qmul.ac.ukTelephone: +44 20 7882 6207Room Number: Peter Landin, CS 406Website: http://webprojects.eecs.qmul.ac.uk/marcusp/Office Hours: Friday 09:00-10:30
Teaching
Music Perception and Cognition (Postgraduate/Undergraduate)
Music is a fundamental part of being human and exists only in the mind of the listener. This module will provide students with advanced training in current understanding of how musical sound is processed by the mind and brain. This is crucial for developing creative tools for musicians and intuitive interfaces for music lovers as well as for using technology in the creative production of new music.
Research
Research Interests:
- Music Cognition
- Empirical Aesthetics and Neuroaesthetics
- Emotion and Music
- Auditory Perception
- Statistical Learning
- Expectation and Prediction
Publications
- Tenderini MS, de Leeuw E, Eilola TM et al. (2022), Reduced Cross-Modal Affective Priming in the L2 of Late Bilinguals Depends on L2 Exposure $nameOfConferenceDOI: 10.1037/xlm0000889
- Quiroga-Martinez DR, Hansen NC, Højlund A et al. (2021), Musicianship and melodic predictability enhance neural gain in auditory cortex during pitch deviance detection $nameOfConferenceDOI: 10.1002/hbm.25638
- Goldman A, Harrison PMC, Jackson T et al. (2021), Reassessing Syntax-Related ERP Components Using Popular Music Chord Sequences $nameOfConference
- Krishnan S, Carey D, Dick F et al. (2021), Effects of statistical learning in passive and active contexts on reproduction and recognition of auditory sequences. $nameOfConferenceDOI: 10.1037/xge0001091
- Hansen NC, Kragness HE, Vuust P et al. (2021), Predictive Uncertainty Underlies Auditory Boundary Perception $nameOfConference
- de Fleurian R, Pearce MT (2021), The Relationship Between Valence and Chills in Music: A Corpus Analysis $nameOfConference
- Hall ETR, Pearce MT (2021), A model of large-scale thematic structure $nameOfConference
- Clemente A, Pearce MT, Skov M et al. (2021), Evaluative judgment across domains: Liking balance, contour, symmetry and complexity in melodies and visual designs $nameOfConference
- Clemente A, Pearce MT, Nadal M (2021), Musical Aesthetic Sensitivity $nameOfConferenceDOI: 10.1037/aca0000381
- de Fleurian R, Pearce MT (2021), Chills in Music: A Systematic Review $nameOfConferenceDOI: 10.1037/bul0000341
- Politimou N, Douglass-Kirk P, Pearce M et al. (2020), Melodic expectations in 5- and 6-year-old children $nameOfConference
- Harrison PMC, Bianco R, Chait M et al. (2020), PPM-Decay: A computational model of auditory prediction with memory decay $nameOfConference
- Zioga I, Harrison PMC, Pearce MT et al. (2020), Auditory but not audiovisual cues lead to higher neural sensitivity to the statistical regularities of an unfamiliar musical style $nameOfConferenceDOI: 10.1162/jocn_a_01614
- Ycart A, Liu L, Benetos E et al. (2020), Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription $nameOfConferenceDOI: 10.5334/tismir.57
- Bianco R, Harrison PMC, Hu M et al. (2020), Long-term implicit memory for sequential auditory patterns in humans $nameOfConferenceDOI: 10.7554/eLife.56073
- Ycart A, Liu L, Benetos E et al. (2020), Musical Features for Automatic Music Transcription Evaluation $nameOfConference
- Quiroga-Martinez DR, Hansen NC, Højlund A et al. (2020), Decomposing neural responses to melodic surprise in musicians and non-musicians: Evidence for a hierarchy of predictions in the auditory system $nameOfConference
- Clemente A, Vila-Vidal M, Pearce MT et al. (2020), A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments. $nameOfConference
- Harrison PMC, Pearce MT (2020), A computational cognitive model for the analysis and generation of voice leadings $nameOfConference
- Quiroga-Martinez DR, Hansen NC, Højlund A et al. (2020), Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians $nameOfConferenceDOI: 10.1101/754333
- Quiroga-Martinez DR, C Hansen N, Højlund A et al. (2019), Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians. $nameOfConferenceDOI: 10.1111/ejn.14667
- Harrison P, Pearce M (publicationYear), Simultaneous consonance in music perception and composition $nameOfConferenceDOI: 10.1037/rev0000169
- Pearce M, Sauvé S (2019), Information-theoretic Modeling of Perceived Musical Complexity $nameOfConference
- Cheung V, HARRISON PMC, Meyer L et al. (2019), Uncertainty and Surprise Jointly Predict Musical Pleasure and Amygdala, Hippocampus, and Auditory Cortex Activity $nameOfConference
- Zioga I, Harrison P, Pearce M et al. (2019), From learning to creativity: Identifying the behavioural and neural correlates of learning to predict human judgements of musical creativity $nameOfConference
- Gold B, Pearce M, Mas-Herrero E et al. (2019), Predictability and uncertainty in the pleasure of music: a reward for learning? $nameOfConference
- de Fleurian R, Harrison PMC, Pearce MT et al. (2019), Reward prediction tells us less than expected about musical pleasure $nameOfConference
- Cameron DJ, Zioga I, Lindsen JP et al. (2019), Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms $nameOfConference
- Quiroga-Martinez DR, Hansen NC, Højlund A et al. (2019), Reduced prediction error responses in high-as compared to low-uncertainty musical contexts $nameOfConference
- Omigie D, Pearce M, Lehongre K et al. (2019), Intracranial Recordings and Computational Modeling of Music Reveal the Time Course of Prediction Error Signaling in Frontal and Temporal Cortices. $nameOfConferenceDOI: 10.1162/jocn_a_01388
- Sears DRW, Pearce MT, Spitzer J et al. (2018), Expectations for tonal cadences: Sensory and cognitive priming effects. $nameOfConference
- Duffy S, Pearce M (2018), What makes rhythms hard to perform? An investigation using Steve Reich's Clapping Music. $nameOfConference
- Quiroga-Martinez DR, Hansen NC, Højlund A et al. (2018), Reduced prediction error responses in high- as compared to low-uncertainty musical contexts $nameOfConferenceDOI: 10.1101/422949
- PEARCE MT (2018), Statistical Learning and Probabilistic Prediction in Music Cognition: Mechanisms of Stylistic Enculturation $nameOfConferenceDOI: 10.1111/nyas.13654
- Sauvé SA, Sayed A, Dean RT et al. (2018), Effects of Pitch and Timing Expectancy on Musical Emotion $nameOfConferenceDOI: 10.1037/pmu0000203
- Harrison PMC, Pearce MT (2018), An energy-based generative sequence model for testing sensory theories of Western harmony $nameOfConference
- Rohrmeier M, Pearce M (2018), Musical Syntax I: Theoretical Perspectives $nameOfConference
- Pearce M, Rohrmeier M (2018), Musical Syntax II: Empirical Perspectives $nameOfConference
- Sears DRW, PEARCE MT, Caplin WE et al. (2017), Simulating melodic and harmonic expectations for tonal cadences using probabilistic models $nameOfConference
- Cameron D, Potter K, Wiggins G et al. (2017), Perception of Rhythmic Similarity is Asymmetrical, and Is Influenced by Musical Training, Expressive Performance, and Musical Context $nameOfConference
- van der Weij B, Pearce MT, Honing H (2017), A Probabilistic Model of Meter Perception: Simulating Enculturation. $nameOfConference
- Pearce M, Müllensiefen D (2017), Compression-based Modelling of Musical Similarity Perception $nameOfConference
- Agres K, Abdallah S, Pearce M (2017), Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory $nameOfConferenceDOI: 10.1111/cogs.12477
- Halpern AR, Zioga I, Shankleman M et al. (2017), That note sounds wrong! Age-related effects in processing of musical expectation. $nameOfConference
- 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 $nameOfConference
- Bhattacharya J, Halpern A, Zioga L et al. (2016), Brains of older adults process melodic expectancy differently from those of younger adults Proceedings of the 18th World Congress of Psychophysiology
- 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 $nameOfConference
- Hansen NC, Sadakata M, Pearce M (2016), Nonlinear Changes in the Rhythm of European Art Music: Quantitative Support for Historical Musicology $nameOfConference
- Gingras B, Pearce MT, Goodchild M et al. (2016), Linking melodic expectation to expressive performance timing and perceived musical tension $nameOfConferenceDOI: 10.1037/xhp0000141
- Pearce MT, Zaidel DW, Vartanian O et al. (2016), Neuroaesthetics: The Cognitive Neuroscience of Aesthetic Experience $nameOfConference
- Barascud N, Pearce MT, Griffiths TD et al. (2016), Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns $nameOfConference
- PEARCE MT, Schubert E (2016), A New Look at Musical Expectancy: The Veridical Versus the General in the Mental Organization of Music $nameOfConference
- Song Y, Dixon S, Pearce MT et al. (2016), Perceived and Induced Emotion Responses to Popular Music: Categorical and Dimensional Models $nameOfConference
- Pearce MT, Halpern AR (2015), Age-related patterns in emotions evoked by music $nameOfConferenceDOI: 10.1037/a0039279
- Carey D, Rosen S, Krishnan S et al. (2015), Generality and specificity in the effects of musical expertise on perception and cognition $nameOfConference
- Song C, Pearce M, Harte C (2015), Synpy: A python toolkit for syncopation modelling $nameOfConference
- Hansen NC, Pearce MT (2014), Predictive uncertainty in auditory sequence processing. $nameOfConference
- Whorley R, Wiggins GA, Rhodes CS et al. (2013), Multiple Viewpoint Systems: Time Complexity and the Construction of Domains for Complex Musical Viewpoints $nameOfConference
- Bailes F, Dean RT, Pearce MT (2013), Music cognition as mental time travel $nameOfConferenceDOI: 10.1038/srep02690
- Omigie D, Pearce MT, Williamson VJ et al. (2013), Electrophysiological correlates of melodic processing in congenital amusia $nameOfConference
- Cherla S, Weyde T, d’Avila Garcez A et al. (2013), A distributed model for multiple-viewpoint melodic prediction $nameOfConference
- Agres K, Abdallah S, Pearce MT (2013), An Information-Theoretic Account of Musical Expectation and Memory. $nameOfConference
- Song Y, Dixon S, Pearce M et al. (2013), Do online social tags predict perceived or induced emotional responses to music? $nameOfConference
- Omigie D, Pearce MT, Stewart L et al. (2013), Electrophysiological correlates of melodic processing in congenital amusia $nameOfConference
- Whorley R, Rhodes C, Wiggins G et al. (2013), Harmonising melodies: Why do we add the bass line first? $nameOfConference
- Egermann H, Pearce MT, Wiggins GA et al. (2013), Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. $nameOfConference
- Song C, Simpson AJR, Harte CA et al. (2013), Syncopation and the score. $nameOfConference
- Pearce M, Rohrmeier M (2012), Music Cognition and the Cognitive Sciences $nameOfConference
- Pearce MT, Wiggins GA (2012), Auditory Expectation: The Information Dynamics of Music Perception and Cognition $nameOfConference
- Pearce MT, Christensen JF (2012), Conference Report: The Neurosciences and Music - IV - Learning and Memory $nameOfConferenceDOI: 10.1037/a0027235
- Song Y, Dixon S, Pearce M (2012), Evaluation of musical features for emotion classification $nameOfConference
- Carrus E, Pearce MT, Bhattacharya J (2012), Melodic pitch expectation interacts with neural responses to syntactic but not semantic violations $nameOfConference
- Omigie D, Pearce MT, Stewart L (2012), Tracking of pitch probabilities in congenital amusia $nameOfConference
- Pearce MT, Müllensiefen D, Wiggins GA (2010), The role of expectation and probabilistic learning in auditory boundary perception: a model comparison. $nameOfConferenceDOI: 10.1068/p6507
- Whorley R, Wiggins GA, Rhodes C et al. (2010), Development of Techniques for the Computational Modelling of Harmony $nameOfConference
- Pearce MT, Müllensiefen D, Wiggins GA (2010), Melodic Grouping in Music Information Retrieval: New Methods and Applications $nameOfConference
- Pearce MT, Müllensiefen D, Wiggins GA (2010), Melodic grouping in music information retrieval: New methods and applications $nameOfConference
- Wiggins GA, Müllensiefen D, Pearce MT (2010), On the non-existence of Music: Why music theory is a figment of the imagination $nameOfConference
- Nadal M, Pearce MT (2010), The Copenhagen Neuroaesthetics conference: Prospects and pitfalls for an emerging field $nameOfConference
- Pearce MT, Müllensiefen D, Wiggins GA (2010), The role of expectation and probabilistic learning in auditory boundary perception: A model comparison $nameOfConferenceDOI: 10.1068/p6507
- Pearce MT, Herrojo Ruiz M, Kapasi S et al. (2010), Unsupervised Statistical Learning Underpins Computational, Behavioural and Neural Manifestations of Musical Expectation $nameOfConference
- Pearce MT, Ruiz MH, Kapasi S et al. (2010), Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation $nameOfConference
- Wiggins GA, Pearce MT, Müllensiefen D (2009), Computational Modelling of Music Cognition and Musical Creativity $nameOfConference
- Rohrmeier M, Honing H, Rebuschat P et al. (2009), Music Cognition: Learning and Processing $nameOfConference
- Pearce MT (2009), To beep or not to beep $nameOfConference
- Pearce MT, Müllensiefen D, Wiggins GA (2008), A comparison of statistical and rule-based models of melodic segmentation $nameOfConference
- 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). $nameOfConference
- Pearce MT, Müllensiefen D, Lewis D et al. (2007), David Temperley, Music and Probability. Cambridge, Massachusetts: MIT Press, 2007, ISBN-13: 978-0-262-20166-7 (hardcover) $40.00 $nameOfConference
- Pearce MT, Wiggins GA (2007), Evaluating cognitive models of musical composition $nameOfConference
- Whorley RP, Wiggins GA, Pearce MT (2007), Systematic Evaluation and Improvement of Statistical Models of Harmony $nameOfConference
- Potter K, Wiggins GA, Pearce MT (2007), Towards greater objectivity in music theory: Information-dynamic analysis of minimalist music $nameOfConference
- Pearce MT, Wiggins GA (2006), Expectation in Melody: The Influence of Context and Learning $nameOfConference
- Pearce MT, Wiggins GA (2006), The information dynamics of melodic boundary detection $nameOfConference
- Pearce MT, Conklin D, Wiggins GA (2005), Methods for Combining Statistical Models of Music $nameOfConference
- Pearce MT (2005), The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition $nameOfConference
- Pearce MT, Wiggins GA (2004), Improved Methods for Statistical Modelling of Monophonic Music $nameOfConference
- Pearce MT, Wiggins GA (2004), Rethinking Gestalt Influences on Melodic Expectancy $nameOfConference
- Pearce MT, Meredith D (2004), Review of the Third International Symposium on Computer Music Modelling and Retrieval $nameOfConference
- Pearce MT, Wiggins GA (2003), An empirical comparison of the performance of PPM variants on a prediction task with monphonic music $nameOfConference
- Pearce MT, Wiggins GA (2002), Aspects of a cognitive theory of creativity in musical composition $nameOfConference
- Pearce M, Wiggins GA, Meredith D (2001), Motivations and Methodologies for Automation of the Compositional Process. $nameOfConference
- Pearce MT (2001), Report on the ICCBR’01 Workshop on Creative Systems $nameOfConference
- Pearce MT, Wiggins GA (2001), Towards a framework for the evaluation of machine compositions $nameOfConference