Context drives our interpretations of music as surprising, frightening, or awe-inspiring. However, it remains unclear how formal musical training affects our ability to hierarchically integrate complex tonal information to efficiently predict, remember, and segment music. We scrambled naturalistic music at multiple timescales to manipulate coherent tonal context while controlling for multiple acoustic cues. Memory (Experiment 1; n = 108, age range = 19–41 years) and prediction (Experiment 2; n = 108, age range = 20–41 years) improved with more intact context for both musicians and nonmusicians. Listeners’ event boundaries were influenced by the amount of tonal context but also reflected nested phrase structure, and musicians were more sensitive to longer-timescale “hyperphrase” structure (Experiment 3; n = 95, age range = 20–42 years) and could better identify the amount of scrambling (Experiment 4; n = 108, age range = 19–41 years). These results indicate that listeners integrate tonal context across complex phrases to efficiently encode, predict, and segment naturalistic music and that in general, training has surprisingly little impact on this integration.
AbramsD. A.BhataraA.RyaliS.BalabanE.LevitinD. J.MenonV. (2011). Decoding temporal structure in music and speech relies on shared brain resources but elicits different fine-scale spatial patterns. Cerebral Cortex, 21(7), 1507–1518.
2.
BoltzM. (1991). Some structural determinants of melody recall. Memory & Cognition, 19, 239–251.
3.
BrudererM. J.McKinneyM. F. (2008). Perceptual evaluation of models for music segmentation [Conference session]. In Proceedings of the 4th Conference on Interdisciplinary Musicology (CIM08), Thessaloniki, Greece (p. 32).
4.
BrudererM. J.McKinneyM. F.KohlrauschA. (2009). The perception of structural boundaries in melody lines of Western popular music. Musicae Scientiae, 13(2), 273–313.
5.
BürknerP.-C. (2018). Advanced Bayesian multilevel modeling with the R Package brms. The R Journal, 10(1), 395–411. https://doi.org/10.32614/RJ-2018-017
6.
BurunatI.LevitinD. J.ToiviainenP. (2024). Breaking (musical) boundaries by investigating brain dynamics of event segmentation during real-life music-listening. Proceedings of the National Academy of Sciences, 121(36), Article e2319459121.
7.
CarlsenJ. C. (1981). Some factors which influence melodic expectancy. Psychomusicology: A Journal of Research in Music Cognition, 1(1), 12–29.
8.
ChiappeP.SchmucklerM. A. (1997). Phrasing influences the recognition of melodies. Psychonomic Bulletin & Review, 4(2), 254–259.
9.
CohenA. J. (2000). Development of tonality induction: Plasticity, exposure, and training. Music Perception, 17(4), 437–459.
10.
CuddyL. L.CohenA. J.MewhortD. J. K. (1981). Perception of structure in short melodic sequences. Journal of Experimental Psychology: Human Perception and Performance, 7(4), 869–883. https://doi.org/10.1037/0096-1523.7.4.869
11.
CuddyL. L.LunneyC. A. (1995). Expectancies generated by melodic intervals: Perceptual judgments of melodic continuity. Perception & Psychophysics, 57(4), 451–462.
12.
DeliegeI. (1987). Grouping conditions in listening to music. Music Perception, 4(4), 325–360.
DeutschD. (1980). The processing of structured and unstructured tonal sequences. Perception & Psychophysics, 28(5), 381–389.
15.
DeutschD.LapidisR.HenthornT. (2008). The speech-to-song illusion. The Journal of the Acoustical Society of America, 124(4_Supplement), 2471.
16.
Di LibertoG. M.PelofiC.ShammaS.De CheveignéA. (2020). Musical expertise enhances the cortical tracking of the acoustic envelope during naturalistic music listening. Acoustical Science and Technology, 41(1), 361–364.
17.
DingN.MelloniL.ZhangH.TianX.PoeppelD. (2016). Cortical tracking of hierarchical linguistic structures in connected speech. Nature Neuroscience, 19(1), 158–164.
18.
DowlingW. J.HollombeA. W. (1977). The perception of melodies distorted by splitting into several octaves: Effects of increasing proximity and melodic contour. Perception & Psychophysics, 21(1), 60–64.
19.
DowlingW. J.TillmannB. (2014). Memory improvement while hearing music. Music Perception, 32(1), 11–32.
FarboodM. M.HeegerD. J.MarcusG.HassonU.LernerY. (2015). The neural processing of hierarchical structure in music and speech at different timescales. Frontiers in Neuroscience, 9, Article 157. https://doi.org/10.3389/fnins.2015.00157
22.
FaulF.ErdfelderE.LangA.-G.BuchnerA. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.
23.
FedorenkoE.McDermottJ. H.Norman-HaignereS.KanwisherN. (2012). Sensitivity to musical structure in the human brain. Journal of Neurophysiology, 108(12), 3289–3300.
24.
FranklandB. W.CohenA. J. (2004). Parsing of melody: Quantification and testing of the local grouping rules of Lerdahl and Jackendoff’s A Generative Theory of Tonal Music. Music Perception, 21(4), 499–543. https://doi.org/10.1525/mp.2004.21.4.499
25.
GranotR. Y.JacobyN. (2011). Musically puzzling I: Sensitivity to overall structure in the sonata form?Musicae Scientiae, 15(3), 365–386.
26.
GranotR. Y.JacobyN. (2012). Musically puzzling II: Sensitivity to overall structure in a Haydn E-minor sonata. Musicae Scientiae, 16(1), 67–80.
27.
GreenbergS. N.TokarevJ.EstesZ. (2012) Affective orientation influences memory for emotional and neutral words. American Journal of Psychology, 125(1), 71–80.
28.
HuronD. (2006). Sweet anticipation: Music and the psychology of expectation. The MIT Press.
29.
KarnoM.KonečniV. J. (1992). The effects of structural interventions in the first movement of Mozart’s Symphony in G Minor K. 550 on aesthetic preference. Music Perception, 10(1), 63–72.
30.
KoelschS.GunterT.FriedericiA. D.SchrögerE. (2000). Brain indices of music processing: “Nonmusicians” are musical. Journal of Cognitive Neuroscience, 12(3), 520–541.
31.
KrumhanslC. L. (1996). A perceptual analysis of Mozart’s Piano Sonata K. 282: Segmentation, tension, and musical ideas. Music Perception, 13(3), 401–432.
32.
KrumhanslC. L.KesslerE. J. (1982). Tracing the dynamic changes in perceived tonal organization in a spatial representation of musical keys. Psychological Review, 89(4), 334–368. https://doi.org/10.1037/0033-295X.89.4.334
33.
KrumhanslC. L.ShepardR. N. (1979). Quantification of the hierarchy of tonal functions within a diatonic context. Journal of Experimental Psychology: Human Perception and Performance, 5(4), 579–594.
34.
LalitteP.BigandE. (2006). Music in the moment? Revisiting the effect of large scale structures. Perceptual and Motor Skills, 103(3), 811–828.
35.
LenthR. (2025). Emmeans: Estimated marginal means, aka least-squares means. R package version 1.11.1-00001. https://rvlenth.github.io/emmeans/
36.
LerdahlF.JackendoffR. (1983). An overview of hierarchical structure in music. Music Perception, 1(2), 229–252.
37.
LernerY.HoneyC. J.SilbertL. J.HassonU. (2011). Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. Journal of Neuroscience, 31(8), 2906–2915.
38.
LevitinD. J.MenonV. (2003). Musical structure is processed in “language” areas of the brain: A possible role for Brodmann Area 47 in temporal coherence. NeuroImage, 20(4), 2142–2152.
39.
LörchL. (2022). Chunking in tonal contexts: Information compression during serial recall of visually presented musical notes. Psychology of Music, 50(3), 691–708.
40.
MargulisE. H. (2005). A model of melodic expectation. Music Perception, 22(4), 663–714.
41.
McFeeB.NietoO.FarboodM. M.BelloJ. P. (2017). Evaluating hierarchical structure in music annotations. Frontiers in Psychology, 8, Article 1337. https://doi.org/10.3389/fpsyg.2017.01337
MeyerL. B. (1957). Meaning in music and information theory. The Journal of Aesthetics and Art Criticism, 15(4), 412–424.
44.
MilneA. E.BiancoR.PooleK. C.ZhaoS.OxenhamA. J.BilligA. J.ChaitM. (2021). An online headphone screening test based on dichotic pitch. Behavior Research Methods, 53(4), 1551–1562.
45.
NarmourE. (1990). The analysis and cognition of basic melodic structures: The implication-realization model. University of Chicago Press.
46.
NewtsonD. (1976). Foundations of attribution: The perception of ongoing behavior. In HarveyJ. H.IckesW. J.KiddR. F. (Eds.), New directions in attribution research (pp. 223–248). Erlbaum.
47.
PearceM. T.MüllensiefenD.WigginsG. A. (2010). Melodic grouping in music information retrieval: New methods and applications. In RasZ. W.WieczorkowskaAlicja A. (Eds.), Advances in music information retrieval (pp. 364–388). Springer.
48.
PearceM. T.WigginsG. A. (2006). Expectation in melody: The influence of context and learning. Music Perception, 23(5), 377–405.
49.
PeretzI.ColtheartM. (2003). Modularity of music processing. Nature Neuroscience, 6(7), 688–691.
50.
RegnaultP.BigandE.BessonM. (2001). Different brain mechanisms mediate sensitivity to sensory consonance and harmonic context: Evidence from auditory event-related brain potentials. Journal of Cognitive Neuroscience, 13(2), 241–255.
51.
RothsteinW. (1989). Phrase rhythm in tonal music. Schirmer.
52.
SchellenbergE. G. (1997). Simplifying the implication-realization model of melodic expectancy. Music Perception, 14, 295–318.
53.
SchmucklerM. A. (1989). Expectation in music: Investigation of melodic and harmonic processes. Music Perception, 7(2), 109–149.
54.
SearsD. R. W.VerbetenJ. E.PercivalH. M. (2023). Does order matter? Harmonic priming effects for scrambled tonal chord sequences. Journal of Experimental Psychology: Human Perception and Performance, 49(7), 999–1015.
55.
TanN.AielloR.BeverT. G. (1981). Harmonic structure as a determinant of melodic organization. Memory & Cognition, 9(5), 533–539.
56.
TemperleyD. (1999). What’s key for key? The Krumhansl-Schmuckler key-finding algorithm reconsidered. Music Perception, 17(1), 65–100.
57.
TemperleyD. (2007). Music and probability. MIT Press.
58.
TillmannB.BharuchaJ. J.BigandE. (2000). Implicit learning of tonality: A self-organizing approach. Psychological Review, 107(4), 885–913.
59.
TillmannB.BigandE. (2001). Global context effect in normal and scrambled musical sequences. Journal of Experimental Psychology: Human Perception and Performance, 27(5), 1185–1196.
60.
TingK. M. (2010). Precision and recall. In SammutC.WebbG. I. (Eds.), Encyclopedia of machine learning. Springer.
61.
TrainorL. J.DesjardinsR. N.RockelC. (1999). A comparison of contour and interval processing in musicians and nonmusicians using event-related potentials. Australian Journal of Psychology, 51(3), 147–153.
62.
TrehubS. E. (2003). The developmental origins of musicality. Nature Neuroscience, 6(7), 669–673.
63.
TruebloodJ. S.BrownS. D.HeathcoteA.BusemeyerJ. R. (2013). Not just for consumers: Context effects are fundamental to decision making. Psychological Science, 24(6), 901–908.
64.
ValdesoloP.DeStenoD. (2006). Manipulations of emotional context shape moral judgment. Psychological Science, 17(6), 476–477.
65.
WilliamsJ. A.MargulisE. H.NastaseS. A.ChenJ.HassonU.NormanK. A.BaldassanoC. (2022). High-order areas and auditory cortex both represent the high-level event structure of music. Journal of Cognitive Neuroscience, 34(4), 699–714.
66.
ZacksJ. M.SwallowK. M. (2007). Event segmentation. Current Directions in Psychological Science, 16(2), 80–84.
67.
ZhangJ.JiangC.ZhouL.YangY. (2016). Perception of hierarchical boundaries in music and its modulation by expertise. Neuropsychologia, 91, 490–498.
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