Skip to main content
SpringerLink
Log in

Discovering the Neuroanatomical Correlates of Music with Machine Learning

  • Chapter
  • First Online:
Handbook of Artificial Intelligence for Music

Abstract

Music is ubiquitous in our lives yet unique to humans. Over the past decades, a growing body of literature has revealed the neural and computational underpinnings of music processing including not only sensory perception (e.g., pitch, rhythm, and timbre) but also local/non-local structural processing (e.g., melody and harmony). This chapter reviews the neural correlates of unsupervised learning with regard to the computational and neuroanatomical architectures of music processing. Further, we offer a novel theoretical perspective on the brain’s unsupervised learning machinery that considers computational and neurobiological constraints, highlighting the connections between neuroscience and machine learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abla, D., & Okanoya, K. (2008). Statistical segmentation of tone sequences activates the left inferior frontal cortex: A near-infrared spectroscopy study. Neuropsychologia, 46(11), 2787–2795. https://doi.org/10.1016/j.neuropsychologia.2008.05.012.

    Article  Google Scholar 

  2. Adhikari, B. M., Norgaard, M., Quinn, K. M., Ampudia, J., Squirek, J., & Dhamala, M. (2016). The brain network underpinning novel melody creation. Brain Connectivity, 6(10), 772–785. https://doi.org/10.1089/brain.2016.0453.

    Article  Google Scholar 

  3. Allen, R., & Reber, A. S. (1980). Very long term memory for tacit knowledge. Cognition, 8(2), 175–185. https://doi.org/10.1016/0010-0277(80)90011-6.

  4. Altmann, G. (2017). Abstraction and generalization in statistical learning: Implications for the relationship between semantic types and episodic tokens. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711). https://doi.org/10.1098/rstb.2016.0060.

  5. Altmann, G. T. (1999). Rule learning by seven-month-old infants and neural networks. Science, 284(5416), 875a–875. https://doi.org/10.1126/science.284.5416.875a.

    Article  Google Scholar 

  6. Amabile, T. (1996). Creativity in context. CO: Westview Press.

    Google Scholar 

  7. Amunts, K., Lenzen, M., Friederici, A. D., Schleicher, A., Morosan, P., Palomero-Gallagher, N., & Zilles, K. (2010). Broca’s region: Novel organizational principles and multiple receptor mapping. PLOS Biology, 8(9), e1000489. https://doi.org/10.1371/journal.pbio.1000489.

  8. Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X.-J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Sciences, 16(12), 584–592. https://doi.org/10.1016/j.tics.2012.10.008.

    Article  Google Scholar 

  9. Assaneo, M. F., Ripollés, P., Orpella, J., Lin, W. M., de Diego-Balaguer, R., & Poeppel, D. (2019). Spontaneous synchronization to speech reveals neural mechanisms facilitating language learning. Nature Neuroscience, 22(4), 627–632. https://doi.org/10.1038/s41593-019-0353-z.

    Article  Google Scholar 

  10. Axelrod, V., Rees, G., Lavidor, M., & Bar, M. (2015). Increasing propensity to mind-wander with transcranial direct current stimulation. Proceedings of the National Academy of Sciences, 112(11), 3314–3319. https://doi.org/10.1073/pnas.1421435112.

  11. Babayan, B. M., Watilliaux, A., Viejo, G., Paradis, A.-L., Girard, B., & Rondi-Reig, L. (2017). A hippocampo-cerebellar centred network for the learning and execution of sequence-based navigation. Scientific Reports, 7(1), 17812. https://doi.org/10.1038/s41598-017-18004-7.

    Article  Google Scholar 

  12. Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W. Y., Franklin, M. S., & Schooler, J. W. (2012). Inspired by distraction: Mind wandering facilitates creative incubation. Psychological Science, 23(10), 1117–1122. https://doi.org/10.1177/0956797612446024.

    Article  Google Scholar 

  13. Balaguer, R. D. D., Toro, J. M., Rodriguez-fornells, A., Psicologia, F. De, Barcelona, U. De, & Hospital, H. M. (2007). Different neurophysiological mechanisms underlying word and rule extraction from speech. (11). https://doi.org/10.1371/journal.pone.0001175.

  14. Bar, M. (2009). The proactive brain: Memory for predictions. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364(1521), 1235–1243. https://doi.org/10.1098/rstb.2008.0310.

  15. Batterink, L. J., & Paller, K. A. (2017). Online neural monitoring of statistical learning. Cortex. https://doi.org/10.1016/j.cortex.2017.02.004.

    Article  Google Scholar 

  16. Beaty, R. E., Benedek, M., Barry Kaufman, S., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5, 10964. https://doi.org/10.1038/srep10964.

  17. Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95. https://doi.org/10.1016/j.tics.2015.10.004.

    Article  Google Scholar 

  18. Beaty, R. E., Christensen, A. P., Benedek, M., Silvia, P. J., & Schacter, D. L. (2017). Creative constraints: Brain activity and network dynamics underlying semantic interference during idea production. NeuroImage, 148(January), 189–196. https://doi.org/10.1016/j.neuroimage.2017.01.012.

    Article  Google Scholar 

  19. Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences, 7, 201713532. https://doi.org/10.1073/pnas.1713532115.

    Article  Google Scholar 

  20. Benavides-Varela, S., Gómez, D. M., Macagno, F., Bion, R. A. H., Peretz, I., & Mehler, J. (2011). Memory in the neonate brain. PLOS ONE, 6(11), e27497. https://doi.org/10.1371/journal.pone.0027497.

  21. Bengtsson, S. L., Csíkszentmihályi, M., & Ullén, F. (2007). Cortical regions involved in the generation of musical structures during improvisation in pianists. Journal of Cognitive Neuroscience, 19(5), 830–842. https://doi.org/10.1162/jocn.2007.19.5.830.

    Article  Google Scholar 

  22. Berkowitz, A. L., & Ansari, D. (2008). Generation of novel motor sequences: The neural correlates of musical improvisation. NeuroImage, 41(2), 535–543. https://doi.org/10.1016/j.neuroimage.2008.02.028.

  23. Besold, T., Schorlemmer, M., & Smaill, A. (2015). Computational creativity research: Towards creative machines (Atlantis T). Atlantis Press.

    Google Scholar 

  24. Bischoff-Grethe, A., Proper, S. M., Mao, H., Daniels, K. A., & Berns, G. S. (2000). Conscious and unconscious processing of nonverbal predictability in Wernicke’s area. The Journal of Neuroscience, 20(5), 1975–1981. https://doi.org/10.1523/jneurosci.5501-05.2006.

    Article  Google Scholar 

  25. Blackwood, N., ffytche, D., Simmons, A., Bentall, R., Murray, R., & Howard, R. (2004). The cerebellum and decision making under uncertainty. Cognitive Brain Research, 20(1), 46–53. https://doi.org/10.1016/j.cogbrainres.2003.12.009.

  26. Boccia, M., Piccardi, L., Palermo, L., Nori, R., & Palmiero, M. (2015). Where do bright ideas occur in ourbrain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity. Frontiers in Psychology, 6(AUG), 1–12. https://doi.org/10.3389/fpsyg.2015.01195.

  27. Boyd, L. A., Edwards, J. D., Siengsukon, C. S., Vidoni, E. D., Wessel, B. D., & Linsdell, M. A. (2009). Motor sequence chunking is impaired by basal ganglia stroke. Neurobiology of Learning and Memory, 92(1), 35–44. https://doi.org/10.1016/j.nlm.2009.02.009.

  28. Brent, M. R. (1999). Speech segmentation and word discovery: A computational perspective. Trends in Cognitive Sciences, 3(8), 294–301. https://doi.org/10.1016/S1364-6613(99)01350-9.

    Article  Google Scholar 

  29. Carrus, E., Pearce, M. T., & Bhattacharya, J. (2013). Melodic pitch expectation interacts with neural responses to syntactic but not semantic violations. Cortex, 49(8), 2186–2200. https://doi.org/10.1016/j.cortex.2012.08.024.

  30. Cleeremans, A., Destrebecqz, A., & Boyer, M. (1998). Implicit learning: News from the front. Trends in Cognitive Sciences, 2(10), 406–416. https://doi.org/10.1016/S1364-6613(98)01232-7.

    Article  Google Scholar 

  31. Cohen, N., & Eichenbaum, H. (1993). Memory, amnesia, and the hippocampal system. Cambridge, MA: MIT Press.

    Google Scholar 

  32. Collins, A., & Koechlin, E. (2012). Reasoning, learning, and creativity: Frontal lobe function and human decision-making. PLoS Biology, 10(3). https://doi.org/10.1371/journal.pbio.1001293.

  33. Conway, C. M., & Christiansen, M. H. (2006). Pages from Revista—Corpo a Corpo—2007-11-8.pdf. 17(10), 905–912.

    Google Scholar 

  34. Cunillera, T., Càmara, E., Toro, J. M., Marco-pallares, J., Sebastián-galles, N., Ortiz, H., et al. (2009). NeuroImage time course and functional neuroanatomy of speech segmentation in adults. NeuroImage, 48(3), 541–553. https://doi.org/10.1016/j.neuroimage.2009.06.069.

    Article  Google Scholar 

  35. Cunillera, T., Toro, J. M., Sebastián-gallés, N., & Rodríguez-fornells, A. (2006). The effects of stress and statistical cues on continuous speech segmentation: An event-related brain potential study. 3. https://doi.org/10.1016/j.brainres.2006.09.046.

  36. Daikoku, T. (2018a). Entropy. Uncertainty, and the depth of implicit knowledge on musical creativity: Computational study of improvisation in melody and rhythm. 12(December), 1–11. https://doi.org/10.3389/fncom.2018.00097.

    Article  Google Scholar 

  37. Daikoku, T. (2018b). Musical creativity and depth of implicit knowledge: Spectral and temporal individualities in improvisation. Frontiers in Computational Neuroscience, 12(November), 1–27. https://doi.org/10.3389/fncom.2018.00089.

    Article  Google Scholar 

  38. Daikoku, T. (2018c). Neurophysiological markers of statistical learning in music and language: Hierarchy, entropy, and uncertainty. Brain Sciences, 8(6). https://doi.org/10.3390/brainsci8060114.

  39. Daikoku, T. (2018d). Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge. PLoS ONE, 13(5). https://doi.org/10.1371/journal.pone.0196493.

  40. Daikoku, T. (2019). Depth and the uncertainty of statistical knowledge on musical creativity fluctuate over a composer’s lifetime. Frontiers in computational neuroscience, 13, 27.

    Google Scholar 

  41. Daikoku, T. (2019). Tonality tunes the statistical characteristics in music: Computational approaches on statistical learning. Frontiers in Computational Neuroscience, 13, 70. https://www.frontiersin.org/article/10.3389/fncom.2019.00070.

  42. Daikoku, T., Okano, T., & Yumoto, M. (2017). Relative difficulty of auditory statistical learning based on tone transition diversity modulates chunk length in the learning strategy. In Proceedings of the Biomagnetic, Sendai, Japan, 22–24 May (p.75). https://doi.org/10.1016/j.nlm.2014.11.001.

  43. Daikoku, T., Yatomi, Y., & Yumoto, M. (2014). Implicit and explicit statistical learning of tone sequences across spectral shifts. Neuropsychologia, 63. https://doi.org/10.1016/j.neuropsychologia.2014.08.028.

  44. Daikoku, T., Yatomi, Y., & Yumoto, M. (2015). Statistical learning of music- and language-like sequences and tolerance for spectral shifts. Neurobiology of Learning and Memory, 118. https://doi.org/10.1016/j.nlm.2014.11.001.

  45. Daikoku, T., Yatomi, Y., & Yumoto, M. (2016). Pitch-class distribution modulates the statistical learning of atonal chord sequences. Brain and Cognition, 108. https://doi.org/10.1016/j.bandc.2016.06.008.

  46. Daikoku, T., & Yumoto, M. (2017). Single, but not dual, attention facilitates statistical learning of two concurrent auditory sequences. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-10476-x.

  47. Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69(4), 680–694. https://doi.org/10.1016/j.neuron.2011.01.020.

    Article  Google Scholar 

  48. de Manzano, Ö., & Ullén, F. (2012). Activation and connectivity patterns of the presupplementary and dorsal premotor areas during free improvisation of melodies and rhythms. NeuroImage, 63(1), 272–280. https://doi.org/10.1016/j.neuroimage.2012.06.024.

  49. de Manzano, Ö., & Ullén, F. (2012). Goal-independent mechanisms for free response generation: Creative and pseudo-random performance share neural substrates. NeuroImage, 59(1), 772–780. https://doi.org/10.1016/j.neuroimage.2011.07.016.

  50. Dehaene, S., Meyniel, F., Wacongne, C., Wang, L., & Pallier, C. (2015). Perspective the neural representation of sequences: From transition probabilities to algebraic patterns and linguistic trees. Neuron, 88(1), 2–19. https://doi.org/10.1016/j.neuron.2015.09.019.

    Article  Google Scholar 

  51. Dhakal, K., Norgaard, M., Adhikari, B. M., Yun, K. S., & Dhamala, M. (2019). Higher node activity with less functional connectivity during musical improvisation. Brain Connectivity. https://doi.org/10.1089/brain.2017.0566.

    Article  Google Scholar 

  52. Diekelmann, S., & Born, J. (2010). The memory function of sleep. Nature Reviews Neuroscience, 11(2), 114–126. https://doi.org/10.1038/nrn2762.

    Article  Google Scholar 

  53. Dienes, Z., Altmann, G., & Gao, S. (1999). Mapping model across domains a neural feedback: Network of implicit of transfer of implicit knowledge. Cognitive Science, 23(1), 53–82.

    Article  Google Scholar 

  54. Ding, N., Melloni, L., Zhang, H., Tian, X., & Poeppel, D. (2016). Cortical tracking of hierarchical linguistic structures in connected speech. Nature Neuroscience, 19(1), 158–164. https://doi.org/10.1038/nn.4186.

    Article  Google Scholar 

  55. Ding, N., Patel, A. D., Chen, L., Butler, H., Luo, C., & Poeppel, D. (2017). Temporal modulations in speech and music. Neuroscience & Biobehavioral Reviews, 81, 181–187. https://doi.org/10.1016/j.neubiorev.2017.02.011.

  56. Donchin, E., & Coles, M. G. H. (1988). Is the P300 component a manifestation of context updating? Behavioral and Brain Sciences, 11(3).

    Google Scholar 

  57. Dosenbach, N. U. F., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99–105. https://doi.org/10.1016/j.tics.2008.01.001.

    Article  Google Scholar 

  58. Doya, K., Ishii, S., Pouget, A., & Rao, R. P. N. (2007). Bayesian brain: Probabilistic approaches to neural coding. Book, 326. https://doi.org/10.7551/mitpress/9780262042383.001.0001.

  59. Durrant, S. J., Cairney, S. A., & Lewis, P. A. (2013). Overnight consolidation aids the transfer of statistical knowledge from the medial temporal lobe to the striatum. Cerebral Cortex, 23(10), 2467–2478. https://doi.org/10.1093/cercor/bhs244.

    Article  Google Scholar 

  60. Durrant, S. J., Taylor, C., Cairney, S., & Lewis, P. A. (2011). Sleep-dependent consolidation of statistical learning. Neuropsychologia, 49(5), 1322–1331. https://doi.org/10.1016/j.neuropsychologia.2011.02.015.

    Article  Google Scholar 

  61. Ellenbogen, J. M., Hu, P. T., Payne, J. D., Titone, D., & Walker, M. P. (2007). Human relational memory requires time and sleep. Proceedings of the National Academy of Sciences, 104(18), 7723–7728. https://doi.org/10.1073/pnas.0700094104.

  62. Ellenbogen, J. M., Payne, J. D., & Stickgold, R. (2006). The role of sleep in declarative memory consolidation: Passive, permissive, active or none? Current Opinion in Neurobiology, 16(6), 716–722. https://doi.org/10.1016/j.conb.2006.10.006.

    Article  Google Scholar 

  63. Ellis, R. (2009). Implicitand explicit learning, knowledge and instruction. In R. Ellis, S. Loewen, C. Elder, R. Erlam, J. Philip, & H. Reinders (Eds.), Implicit and explicit knowledge in second language learning, testing and teaching (Vol. 27, pp. 3–25). https://doi.org/10.1002/9780470756492.ch11.

  64. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1016/0364-0213(90)90002-E.

    Article  Google Scholar 

  65. Elmer, S., Albrecht, J., Valizadeh, S. A., & François, C. (2018). Theta coherence asymmetry in the dorsal stream of musicians facilitates word learning. (March), 1–13. https://doi.org/10.1038/s41598-018-22942-1.

  66. Farthouat, J., Franco, A., Mary, A., Delpouve, J., Wens, V., Op de Beeck, M., & Peigneux, P. (2017). Auditory magnetoencephalographic frequency-tagged responses mirror the ongoing segmentation processes underlying statistical learning. Brain Topography, 30(2), 220–232. https://doi.org/10.1007/s10548-016-0518-y.

    Article  Google Scholar 

  67. Fazio, P., Cantagallo, A., Craighero, L., D’Ausilio, A., Roy, A. C., Pozzo, T., & Fadiga, L. (2009). Encoding of human action in Broca’s area. Brain, 132(7), 1980–1988. https://doi.org/10.1093/brain/awp118.

    Article  Google Scholar 

  68. Feher, O., Ljubičić, I., Suzuki, K., Okanoya, K., & Tchernichovski, O. (2016). Statistical learning in songbirds: From self-tutoring to song culture. In K. Suzuki, K. Okanoya, & O. Tchernichovski (Eds.), Statistical learning in songbirds: from self-tutoring to song culture. Philosophical Transactions of the Royal Society B: Biological Sciences.

    Google Scholar 

  69. Feldman, H., & Friston, K. (2010). Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience, 4, 215. https://www.frontiersin.org/article/10.3389/fnhum.2010.00215.

  70. Fink, A., & Benedek, M. (2014). EEG alpha power and creative ideation. Neuroscience and Biobehavioral Reviews, 44(100), 111–123. https://doi.org/10.1016/j.neubiorev.2012.12.002.

    Article  Google Scholar 

  71. Fink, A., Grabner, R. H., Benedek, M., & Neubauer, A. C. (2006). Divergent thinking training is related to frontal electroencephalogram alpha synchronization. European Journal of Neuroscience, 23(8), 2241–2246. https://doi.org/10.1111/j.1460-9568.2006.04751.x.

    Article  Google Scholar 

  72. Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., & Neubauer, A. C. (2009). The creative brain: Investigation of brain activity during creative problem solving by means of EEG and FMRI. Human Brain Mapping, 30(3), 734–748. https://doi.org/10.1002/hbm.20538.

  73. Fischer, S., Drosopoulos, S., Tsen, J., & Born, J. (2006). Implicit learning–explicit knowing: A role for sleep in memory system interaction. Journal of Cognitive Neuroscience, 18. https://doi.org/10.1162/089892906775990598.

  74. Fitch, W. T., & Hauser, M. D. (2004). Computational constraints on syntactic processing in a nonhuman primate. Science, 303(5656), 377–380. https://doi.org/10.1126/science.1089401.

  75. Flaherty, A. W. (2005). Frontotemporal and dopaminergic control of idea generation and creative drive. The Journal of Comparative Neurology, 493(1), 147–153. https://doi.org/10.1002/cne.20768.

    Article  Google Scholar 

  76. Fontolan, L., Morillon, B., Liegeois-Chauvel, C., & Giraud, A. L. (2014). The contribution of frequency-specific activity to hierarchical information processing in the human auditory cortex. Nature Communications, 5(May), 1–10. https://doi.org/10.1038/ncomms5694.

    Article  Google Scholar 

  77. François, C., Chobert, J., Besson, M., & Schön, D. (2013). Music training for the development of speech segmentation. (September), 2038–2043. https://doi.org/10.1093/cercor/bhs180.

  78. François, C., Cunillera, T., Garcia, E., Laine, M., & Rodriguez-Fornells, A. (2017). Neurophysiological evidence for the interplay of speech segmentation and word-referent mapping during novel word learning. Neuropsychologia, 98, 56–67. https://doi.org/10.1016/j.neuropsychologia.2016.10.006.

    Article  Google Scholar 

  79. Francois, C., & Schön, D. (2011). Musical expertise boosts implicit learning of both musical and linguistic structures. Cerebral Cortex, 21(10), 2357–2365. https://doi.org/10.1093/cercor/bhr022.

    Article  Google Scholar 

  80. François, C., & Schön, D. (2014). Neural sensitivity to statistical regularities as a fundamental biological process that underlies auditory learning: The role of musical practice. Hearing Research, 308, 122–128. https://doi.org/10.1016/j.heares.2013.08.018.

    Article  Google Scholar 

  81. Frankland, P. W., & Bontempi, B. (2005). The organization of recent and remote memories. Nature Reviews Neuroscience, 6, 119. https://doi.org/10.1038/nrn1607.

  82. Frens, M., & Donchin, O. (2009). Forward models and state estimation in compensatory eye movements. Frontiers in Cellular Neuroscience, 3, 13. https://www.frontiersin.org/article/10.3389/neuro.03.013.2009.

  83. Friederici, A. D. (2004). Processing local transitions versus long-distance syntactic hierarchies. Trends in Cognitive Sciences, 8(6), 245–247. https://doi.org/10.1016/j.tics.2004.04.013.

  84. Friederici, A. D. (2011). The brain basis of language processing: From structure to function. Physiological Reviews, 91(4), 1357–1392. https://doi.org/10.1152/physrev.00006.2011.

    Article  Google Scholar 

  85. Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I., & Anwander, A. (2006). The brain differentiates human and non-human grammars: Functional localization and structural connectivity. Proceedings of the National Academy of Sciences, 103(7), 2458–2463. https://doi.org/10.1073/pnas.0509389103.

  86. Friederici, A. D., Chomsky, N., Berwick, R. C., Moro, A., & Bolhuis, J. J. (2017). Language, mind and brain. Nature Human Behaviour, 1(10), 713–722. https://doi.org/10.1038/s41562-017-0184-4.

    Article  Google Scholar 

  87. Friedman, D., Cycowicz, Y. M., & Gaeta, H. (2001). The novelty P3: An event-related brain potential (ERP) sign of the brain’s evaluation of novelty. Neuroscience & Biobehavioral Reviews, 25(4), 355–373. https://doi.org/10.1016/S0149-7634(01)00019-7.

  88. Friedrich, R., & Friederici, A. D. (2009). Mathematical logic in the human brain: Syntax. PLOS ONE, 4(5), e5599. https://doi.org/10.1371/journal.pone.0005599.

  89. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787.

    Article  Google Scholar 

  90. Friston, K., Fitzgerald, T., Rigoli, F., Schwartenbeck, P., Doherty, J. O., & Pezzulo, G. (2016). Neuroscience and biobehavioral reviews active inference and learning. Neuroscience and Biobehavioral Reviews, 68, 862–879. https://doi.org/10.1016/j.neubiorev.2016.06.022.

    Article  Google Scholar 

  91. Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive Neuroscience, 6(4), 187–224. https://doi.org/10.1080/17588928.2015.1020053.

    Article  Google Scholar 

  92. Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., & Dolan, R. J. (2014). The anatomy of choice: Dopamine and decision-making Subject collections The anatomy of choice: dopamine and decision-making. Retrieved from http://rstb.royalsocietypublishing.org/content/369/1655/20130481.full.html#related-urls%5Cnhttp://rstb.royalsocietypublishing.org/content/369/1655/20130481.full.html%23ref-list-1%5Cnhttp://dx.doi.org/10.1098/rstb.2013.0481.

    Google Scholar 

  93. Frost, R., Armstrong, B. C., Siegelman, N., & Christiansen, M. H. (2015). Domain generality versus modality specificity: The paradox of statistical learning. Trends in Cognitive Sciences, 19(3), 117–125. https://doi.org/10.1016/j.tics.2014.12.010.

    Article  Google Scholar 

  94. Frost, R. L. A., & Monaghan, P. (2016). Simultaneous segmentation and generalisation of non-adjacent dependencies from continuous speech. Cognition. https://doi.org/10.1016/j.cognition.2015.11.010.

    Article  Google Scholar 

  95. Furl, N., Kumar, S., Alter, K., Durrant, S., Shawe-Taylor, J., & Griffiths, T. D. (2011). Neural prediction of higher-order auditory sequence statistics. NeuroImage, 54(3), 2267–2277. https://doi.org/10.1016/j.neuroimage.2010.10.038.

    Article  Google Scholar 

  96. Gao, W., Zhu, H., Giovanello, K. S., Smith, J. K., Shen, D., Gilmore, J. H., & Lin, W. (2009). Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proceedings of the National Academy of Sciences, 106(16), 6790–6795. https://doi.org/10.1073/pnas.0811221106.

    Article  Google Scholar 

  97. Gardner, H. (1993). Creating minds: An anatomy of creativity seen through the lives of Freud, Einstein, Picasso, Stravinsky, Eliot, Graham, and Gandhi. Retrieved from https://books.google.de/books?id=NAyZhZTivckC.

  98. Garrido, M. I., Friston, K. J., Kiebel, S. J., Stephan, K. E., Baldeweg, T., & Kilner, J. M. (2008). The functional anatomy of the MMN: A DCM study of the roving paradigm. NeuroImage, 42(2), 936–944. https://doi.org/10.1016/j.neuroimage.2008.05.018.

  99. Garrido, M. I., Kilner, J. M., Kiebel, S. J., & Friston, K. J. (2007). Evoked brain responses are generated by feedback loops. Proceedings of the National Academy of Sciences, 104(52), 20961–20966. https://doi.org/10.1073/pnas.0706274105.

  100. Giraud, A. L., & Poeppel, D. (2012). Cortical oscillations and speech processing: Emerging computational principles and operations. Nature Neuroscience, 15(4), 511–517. https://doi.org/10.1038/nn.3063.

    Article  Google Scholar 

  101. Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30(1), 535–574. https://doi.org/10.1146/annurev.neuro.29.051605.113038.

    Article  Google Scholar 

  102. Gomez, R. (2017). Do infants retain the statistics of a statistical learning experience? Insights from a developmental cognitive neuroscience perspective. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160054. https://doi.org/10.1098/rstb.2016.0054.

    Article  Google Scholar 

  103. Gómez, R. L. (2002). Variability and detection of invariant structure. Psychological Science, 13(5), 431–436. https://doi.org/10.1111/1467-9280.00476.

    Article  Google Scholar 

  104. Gómez, R. L., & Edgin, J. O. (2016). The extended trajectory of hippocampal development: Implications for early memory development and disorder. Developmental Cognitive Neuroscience, 18, 57–69. https://doi.org/10.1016/j.dcn.2015.08.009.

    Article  Google Scholar 

  105. Gómez, R. L., & Gerken, L. (2000). Infant artificial language learning and language acquisition. Trends in Cognitive Sciences, 4(5), 178–186. https://doi.org/10.1016/S1364-6613(00)01467-4.

    Article  Google Scholar 

  106. Gomez, R. L., & Gerken, L. A. (1999). Artificial grammar learning by one-year-olds leads to specific and abstract knowledge. Cognition, 70(2), 109–136.

    Article  Google Scholar 

  107. Graybiel, A. M. (1998). the basal ganglia and chunking of action repertoires. Neurobiology of Learning and Memory, 70(1), 119–136. https://doi.org/10.1006/nlme.1998.3843.

  108. Green, A. E., Spiegel, K. A., Giangrande, E. J., Weinberger, A. B., Gallagher, N. M., & Turkeltaub, P. E. (2017). Thinking cap plus thinking zap: Tdcs of frontopolar cortex improves creative analogical reasoning and facilitates conscious augmentation of state creativity in verb generation. Cerebral Cortex, 27(4), 2628–2639. https://doi.org/10.1093/cercor/bhw080.

    Article  Google Scholar 

  109. Hansen, N. C., & Pearce, M. T. (2014). Predictive uncertainty in auditory sequence processing. Frontiers in Psychology, 5(SEP), 1–17. https://doi.org/10.3389/fpsyg.2014.01052.

  110. Harrison, L. M., Duggins, A., & Friston, K. J. (2006). Encoding uncertainty in the hippocampus. Neural Networks, 19(5), 535–546. https://doi.org/10.1016/j.neunet.2005.11.002.

    Article  MATH  Google Scholar 

  111. Harrison, L. M., Duggins, A., & Friston, K. J. (2006). Encoding uncertainty in the hippocampus. 19, 535–546. https://doi.org/10.1016/j.neunet.2005.11.002.

    Article  Google Scholar 

  112. Hasson, U. (2017). The neurobiology of uncertainty: Implications for statistical learning. Philosophical Transactions of the Royal Society B, 372, 20160048.

    Article  Google Scholar 

  113. Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298(5598), 1569–1579. https://doi.org/10.1126/science.298.5598.1569.

  114. Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9, 467. https://doi.org/10.1038/nrn2374.

  115. Hélie, S., & Sun, R. (2010). Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, 117. https://doi.org/10.1037/a0019532.

  116. Hickok, G. (2012). The cortical organization of speech processing: Feedback control and predictive coding the context of a dual-stream model. Journal of Communication Disorders, 45(6), 393–402. https://doi.org/10.1016/j.jcomdis.2012.06.004.

    Article  Google Scholar 

  117. Hochreiter, S., & Urgen Schmidhuber, J. (1997). Ltsm. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.

  118. Hofstadter, D. (1979). Gödel, Escher, Bach: An eternal golden braid. Critica, 12(36).

    Google Scholar 

  119. Huffman, R. F., & Henson, O. W. (1990). The descending auditory pathway and acousticomotor systems: connections with the inferior colliculus. Brain Research Reviews, 15(3), 295–323. https://doi.org/10.1016/0165-0173(90)90005-9.

  120. Ito, M. (2008). Control of mental activities by internal models in the cerebellum. Nature Reviews Neuroscience, 9, 304. https://doi.org/10.1038/nrn2332.

  121. Janata, P., Birk, J. L., Van Horn, J. D., Leman, M., Tillmann, B., & Bharucha, J. J. (2002). The cortical topography of tonal structures underlying western music. Science, 298(5601), 2167–2170. https://doi.org/10.1126/science.1076262.

  122. Jauk, E., Neubauer, A. C., Dunst, B., Fink, A., & Benedek, M. (2015). Gray matter correlates of creative potential: A latent variable voxel-based morphometry study. NeuroImage, 111, 312–320. https://doi.org/10.1016/j.neuroimage.2015.02.002.

    Article  Google Scholar 

  123. Ji, D., & Wilson, M. A. (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nature Neuroscience, 10(1), 100–107. https://doi.org/10.1038/nn1825.

    Article  Google Scholar 

  124. Jokisch, D., & Jensen, O. (2007). Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream. The Journal of Neuroscience, 27(12), 3244–3251. https://doi.org/10.1523/JNEUROSCI.5399-06.2007.

  125. Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114(1), 1–37. https://doi.org/10.1037/0033-295X.114.1.1.

    Article  Google Scholar 

  126. Jung-Beeman, M., Bowden, E. M., Haberman, J., Frymiare, J. L., Arambel-Liu, S., Greenblatt, R., Kounios, J. (2004). Neural activity when people solve verbal problems with insight. PLOS Biology, 2(4), e97. https://doi.org/10.1371/journal.pbio.0020097.

  127. Kagan, J. (1972). Motives and development. Journal of Personality and Social Psychology, 22(1), 51–66. Retrieved from https://europepmc.org/abstract/MED/5013358.

  128. Karlaftis, V. M., Giorgio, J., Vértes, P. E., Wang, R., Shen, Y., Tino, P., & Kourtzi, Z. (2019). Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning. Nature Human Behaviour, 3(3), 297–307. https://doi.org/10.1038/s41562-018-0503-4.

  129. Karuza, E. A., Li, P., Weiss, D. J., Bulgarelli, F., Zinszer, B. D., & Aslin, R. N. (2016). Sampling over nonuniform distributions: A neural efficiency account of the primacy effect in statistical learning. Journal of Cognitive Neuroscience, 28(10), 1484–1500. https://doi.org/10.1162/jocn_a_00990.

    Article  Google Scholar 

  130. Kaufman, J. C., Kaufman, S. B., & Plucker, J. A. (2013). Contemporary theories of intelligence (D. Reisberg, Ed.). https://doi.org/10.1093/oxfordhb/9780195376746.013.0051.

  131. Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55(1), 271–304. https://doi.org/10.1146/annurev.psych.55.090902.142005.

    Article  Google Scholar 

  132. Khilkevich, A., Canton-Josh, J., DeLord, E., & Mauk, M. D. (2018). A cerebellar adaptation to uncertain inputs. Science Advances, 4(5), eaap9660–eaap9660. https://doi.org/10.1126/sciadv.aap9660.

  133. Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of time-scales and the brain. PLoS Computational Biology, 4(11). https://doi.org/10.1371/journal.pcbi.1000209.

  134. Kim, R., Seitz, A., Feenstra, H., & Shams, L. (2009). Testing assumptions of statistical learning: Is it long-term and implicit? Neuroscience Letters, 461(2), 145–149. https://doi.org/10.1016/j.neulet.2009.06.030.

    Article  Google Scholar 

  135. Kim, S. G., Kim, J. S., & Chung, C. K. (2011). The effect of conditional probability of chord progression on brain response: An MEG study. PLoS ONE, 6(2). https://doi.org/10.1371/journal.pone.0017337.

  136. Klimesch, W. (2012). α-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16(12), 606–617. https://doi.org/10.1016/j.tics.2012.10.007.

    Article  Google Scholar 

  137. Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719. https://doi.org/10.1016/j.tins.2004.10.007.

    Article  Google Scholar 

  138. Koechlin, E., & Jubault, T. (2006). Broca’s area and the hierarchical organization of human behavior. Neuron, 50(6), 963–974. https://doi.org/10.1016/j.neuron.2006.05.017.

    Article  Google Scholar 

  139. Koelsch, S. (2012). Brain and music. Retrieved from https://books.google.co.uk/books?id=b9OXDpmE9dwC.

  140. Koelsch, S. (2014). Brain correlates of music-evoked emotions. Nature Reviews Neuroscience, 15(3), 170–180. https://doi.org/10.1038/nrn3666.

    Article  Google Scholar 

  141. Koelsch, S., Busch, T., Jentschke, S., & Rohrmeier, M. (2016). Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences. Scientific Reports, 6(February), 1–11. https://doi.org/10.1038/srep19741.

    Article  Google Scholar 

  142. Koelsch, S., Kasper, E., Sammler, D., Schulze, K., Gunter, T., & Friederici, A. D. (2004). Music, language and meaning: Brain signatures of semantic processing. Nature Neuroscience, 7(3), 302–307. https://doi.org/10.1038/nn1197.

    Article  Google Scholar 

  143. Koelsch, S., Vuust, P., & Friston, K. (2018). Predictive processes and the peculiar case of music. Trends in Cognitive Sciences, 23(1), 63–77. https://doi.org/10.1016/j.tics.2018.10.006.

    Article  Google Scholar 

  144. Konkel, A., Warren, D., Duff, M., Tranel, D., & Cohen, N. (2008). Hippocampal amnesia impairs all manner of relational memory. Frontiers in Human Neuroscience, 2, 15. Retrieved from https://www.frontiersin.org/article/10.3389/neuro.09.015.2008.

  145. Kouneiher, F., Charron, S., & Koechlin, E. (2009). Motivation and cognitive control in the human prefrontal cortex. Nature Neuroscience, 12, 939. https://doi.org/10.1038/nn.2321.

  146. Krebs, R. M., Schott, B. H., Schütze, H., & Düzel, E. (2009). The novelty exploration bonus and its attentional modulation. Neuropsychologia, 47(11), 2272–2281. https://doi.org/10.1016/j.neuropsychologia.2009.01.015.

  147. Kuhl, P. K. (2000). A new view of language acquisition. Proceedings of the National Academy of Sciences, 97(22), 11850–11857. https://doi.org/10.1073/pnas.97.22.11850.

  148. Kuhl, P. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience. https://doi.org/10.1038/nrn1533.

    Article  Google Scholar 

  149. Kuhl, P. K., Tsao, F.-M., & Liu, H.-M. (2003). Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning. Proceedings of the National Academy of Sciences of the United States of America, 100(15), 9096–9101. https://doi.org/10.1073/pnas.1532872100.

    Article  Google Scholar 

  150. Kuhl, P. K., Williams, K. A., Lacerda, F., Stevens, K. N., & Lindblom, B. (1992). Linguistic experience alters phonetic perception in infants by 6 months of age. Science, 255(5044), 606–608. https://doi.org/10.1126/science.1736364.

  151. Kumaran, D., Hassabis, D., & McClelland, J. L. (2016). What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends in Cognitive Sciences, 20(7), 512–534. https://doi.org/10.1016/j.tics.2016.05.004.

    Article  Google Scholar 

  152. Kutas, M., & Federmeier, K. D. (2010). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62(1), 621–647. https://doi.org/10.1146/annurev.psych.093008.131123.

    Article  Google Scholar 

  153. Landauer, T. K., & Dumais, S. T. (1997). A solution to Platos problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211–240. https://doi.org/10.1037//0033-295X.104.2.211.

    Article  Google Scholar 

  154. Langner, G., & Ochse, M. (2006). The neural basis of pitch and harmony in the auditory system. Musicae Scientiae, 10(1_suppl), 185–208. https://doi.org/10.1177/102986490601000109.

  155. Laura, J. B., Paul, J. R., Helen, J. N., & Ken A. P. (2015). Implicit and explicit contributions to statistical learning. Journal of Memory and Language, 83, 62–78. https://doi.org/10.1016/j.jml.2015.04.004.Implicit.

  156. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436. https://doi.org/10.1038/nature14539.

  157. Leong, V., & Goswami, U. (2015). Acoustic-emergent phonology in the amplitude envelope of child-directed speech. PLOS ONE, 10(12), e0144411. https://doi.org/10.1371/journal.pone.0144411.

  158. Lerdahl, F., Jackendoff, R., & Jackendoff, R. S. (1983). A generative theory of tonal music. Retrieved from https://books.google.de/books?id=38YcngEACAAJ.

  159. Lesage, E., Morgan, B. E., Olson, A. C., Meyer, A. S., & Miall, R. C. (2012). Cerebellar rTMS disrupts predictive language processing. Current Biology: CB, 22(18), R794–R795. https://doi.org/10.1016/j.cub.2012.07.006.

    Article  Google Scholar 

  160. Lewis, P. A., & Durrant, S. J. (2011). Overlapping memory replay during sleep builds cognitive schemata. Trends in Cognitive Sciences, 15(8), 343–351. https://doi.org/10.1016/j.tics.2011.06.004.

    Article  Google Scholar 

  161. Limb, C. J., & Braun, A. R. (2008). Neural substrates of spontaneous musical performance: An fMRI study of jazz improvisation. PLOS ONE, 3(2), e1679. https://doi.org/10.1371/journal.pone.0001679.

  162. Liu, S., Erkkinen, M. G., Healey, M. L., Xu, Y., Swett, K. E., Chow, H. M., & Braun, A. R. (2015). Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process. Human Brain Mapping, 36(9), 3351–3372. https://doi.org/10.1002/hbm.22849.

    Article  Google Scholar 

  163. Liu, Z., Zhang, J., Xie, X., Rolls, E. T., Sun, J., Zhang, K., Feng, J. (2018). Neural and genetic determinants of creativity. NeuroImage, 174(November 2017), 164–176. https://doi.org/10.1016/j.neuroimage.2018.02.067.

  164. Lopata, J. A., Nowicki, E. A., & Joanisse, M. F. (2017). Creativity as a distinct trainable mental state: An EEG study of musical improvisation. Neuropsychologia, 99(March), 246–258. https://doi.org/10.1016/j.neuropsychologia.2017.03.020.

    Article  Google Scholar 

  165. Lopez-Barroso, D., Catani, M., Ripolles, P., Dell’Acqua, F., Rodriguez-Fornells, A., & de Diego-Balaguer, R. (2013). Word learning is mediated by the left arcuate fasciculus. Proceedings of the National Academy of Sciences, 110(32), 13168–13173. https://doi.org/10.1073/pnas.1301696110.

    Article  Google Scholar 

  166. Lu, K., & Vicario, D. S. (2014). Statistical learning of recurring sound patterns encodes auditory objects in songbird forebrain. 111(40), 14553–14558. https://doi.org/10.1073/pnas.1412109111.

    Article  Google Scholar 

  167. Lu, K., & Vicario, D. S. (2017). Familiar but unexpected: Effects of sound context statistics on auditory responses in the songbird forebrain. 37(49), 12006–12017. https://doi.org/10.1523/JNEUROSCI.5722-12.2017.

    Article  Google Scholar 

  168. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, and Computers, 28(2), 203–208. https://doi.org/10.3758/BF03204766.

    Article  Google Scholar 

  169. Lustenberger, C., Boyle, M. R., Foulser, A. A., Mellin, J. M., & Fröhlich, F. (2015). Functional role of frontal alpha oscillations in creativity. Cortex, 67, 74–82. https://doi.org/10.1016/j.cortex.2015.03.012.

    Article  Google Scholar 

  170. Makuuchi, M., Bahlmann, J., Anwander, A., & Friederici, A. D. (2009). Segregating the core computational faculty of human language from working memory. Proceedings of the National Academy of Sciences, 106(20), 8362–8367. https://doi.org/10.1073/pnas.0810928106.

  171. Marr, D. (1971). Simple memory: A theory for archicortex. Philosophical Transactions of the Royal Society of London. B, Biological Sciences, 262(841), 23–81. https://doi.org/10.1098/rstb.1971.0078.

  172. Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007). Wandering minds: The default network and stimulus-independent thought. Science, 315(5810), 393–395. https://doi.org/10.1126/science.1131295.

  173. Maye, J., Weiss, D., & Aslin, R. (2008). Statistical phonetic learning in infants: Facilitation and feature generalization. Developmental Science. https://doi.org/10.1111/j.1467-7687.2007.00653.x.

    Article  Google Scholar 

  174. Maye, J., Werkerb, J., & Gerkenc, L. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. 82, B101–B111. https://dx.doi.org/10.1016/S0010-0277(01)00157-3.

  175. Mayseless, N., Eran, A., & Shamay-Tsoory, S. G. (2015). Generating original ideas: The neural underpinning of originality. NeuroImage, 116, 232–239. https://doi.org/10.1016/j.neuroimage.2015.05.030.

  176. McClelland, J., McNaughton, B., & O’Reilly, R. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419–457.

    Article  Google Scholar 

  177. McNealy, K., Mazziotta, J. C., & Dapretto, M. (2006). Cracking the language code: Neural mechanisms underlying speech parsing. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.5501-05.2006.

    Article  Google Scholar 

  178. Michael, K., Yishay, M., Dana, R., Ronitt, R., Robert, S., & Linda, S. (1994). On the learnability of discrete distributions. In Proceedings of the Twenty-Sixth Annual ACM Symposium on Theory of Computing (STOC ’94) (pp. 273–282). https://doi.org/10.1145/195058.195155.

  179. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167–202. https://doi.org/10.1146/annurev.neuro.24.1.167.

    Article  Google Scholar 

  180. Miranda, R. A., & Ullman, M. T. (2007). Double dissociation between rules and memory in music: An event-related potential study. NeuroImage, 38(2), 331–345. https://doi.org/10.1016/j.neuroimage.2007.07.034.

    Article  Google Scholar 

  181. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529. https://doi.org/10.1038/nature14236.

  182. Moberget, T., Gullesen, E. H., Andersson, S., Ivry, R. B., & Endestad, T. (2014). Generalized role for the cerebellum in encoding internal models: Evidence from semantic processing. The Journal of Neuroscience, 34(8), 2871–2878. https://doi.org/10.1523/JNEUROSCI.2264-13.2014.

  183. Moldwin, T., Schwartz, O., & Sussman, E. S. (2017). Statistical learning of melodic patterns influences the brain’s response to wrong notes. 2114–2122. https://doi.org/10.1162/jocn.

  184. Mölle, M., Marshall, L., Gais, S., & Born, J. (2002). Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. The Journal of Neuroscience, 22(24), 10941–10947. https://doi.org/10.1523/JNEUROSCI.22-24-10941.2002.

  185. Monroy, C. D., Gerson, S. A., Domínguez-Martínez, E., Kaduk, K., Hunnius, S., & Reid, V. (2017). Sensitivity to structure in action sequences: An infant event-related potential study. Neuropsychologia, (May), 0–1. https://doi.org/10.1016/j.neuropsychologia.2017.05.007.

  186. Monroy, C. D., Gerson, S. A., & Hunnius, S. (2018). Translating visual information into action predictions: Statistical learning in action and nonaction contexts. Memory and Cognition, 46(4), 600–613. https://doi.org/10.3758/s13421-018-0788-6.

    Article  Google Scholar 

  187. Monroy, C. D., Meyer, M., Schröer, L., Gerson, S. A., & Hunnius, S. (2017). The infant motor system predicts actions based on visual statistical learning. NeuroImage, 185(December 2017), 947–954. https://doi.org/10.1016/j.neuroimage.2017.12.016.

  188. Monroy, C., Meyer, M., Gerson, S., & Hunnius, S. (2017). Statistical learning in social action contexts. PLoS ONE, 12(5), 1–20. https://doi.org/10.1371/journal.pone.0177261.

    Article  Google Scholar 

  189. Moore, B. (2003). An introduction to the psychology of hearing.

    Google Scholar 

  190. Moscovitch, M., Cabeza, R., Winocur, G., & Nadel, L. (2016). Episodic memory and beyond: The hippocampus and neocortex in transformation. Annual Review of Psychology, 67(1), 105–134. https://doi.org/10.1146/annurev-psych-113011-143733.

    Article  Google Scholar 

  191. Mumford, D. (1992). On the computational architecture of the neocortex—II The role of cortico-cortical loops. Biological Cybernetics, 66(3), 241–251. https://doi.org/10.1007/BF00198477.

    Article  Google Scholar 

  192. Nagai, Y. (2019). Predictive learning: Its key role in early cognitive development. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1771), 20180030. https://doi.org/10.1098/rstb.2018.0030.

    Article  Google Scholar 

  193. Nastase, S., Iacovella, V., & Hasson, U. (2014). Uncertainty in visual and auditory series is coded by modality-general and modality-specific neural systems. Human Brain Mapping, 35(4), 1111–1128. https://doi.org/10.1002/hbm.22238.

    Article  Google Scholar 

  194. Norris, J. M., & Ortega, L. (2000). Effectiveness of L2 instruction: A research synthesis and quantitative meta-analysis. Language Learning, 50(3), 417–528. https://doi.org/10.1111/0023-8333.00136.

    Article  Google Scholar 

  195. O’Neill, J., Pleydell-Bouverie, B., Dupret, D., & Csicsvari, J. (2010). Play it again: Reactivation of waking experience and memory. Trends in Neurosciences, 33(5), 220–229. https://doi.org/10.1016/j.tins.2010.01.006.

  196. O’Reilly, J. X., Jbabdi, S., & Behrens, T. E. J. (2012). How can a Bayesian approach inform neuroscience? European Journal of Neuroscience, 35(7), 1169–1179. https://doi.org/10.1111/j.1460-9568.2012.08010.x.

    Article  Google Scholar 

  197. Oechslin, M. S., Meyer, M., & Jäncke, L. (2010). Absolute pitch-functional evidence of speech-relevant auditory acuity. Cerebral Cortex, 20(2), 447–455. https://doi.org/10.1093/cercor/bhp113.

    Article  Google Scholar 

  198. Ong, J. H., Burnham, D., & Stevens, C. (2016). Learning novel musical pitch via distributional learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43,. https://doi.org/10.1037/xlm0000286.

  199. Opitz, B., & Kotz, S. A. (2012). Ventral premotor cortex lesions disrupt learning of sequential grammatical structures. Cortex, 48(6), 664–673. https://doi.org/10.1016/j.cortex.2011.02.013.

  200. Overath, T., Cusack, R., Kumar, S., Von Kriegstein, K., Warren, J. D., Grube, M., & Griffiths, T. D. (2007). An information theoretic characterisation of auditory encoding. PLoS Biology, 5(11), 2723–2732. https://doi.org/10.1371/journal.pbio.0050288.

  201. Paraskevopoulos, E., Chalas, N., Kartsidis, P., Wollbrink, A., & Bamidis, P. (2017). Statistical learning of multisensory regularities is enhanced in musicians: An MEG study. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.04.002.

  202. Paraskevopoulos, E., Kuchenbuch, A., Herholz, S. C., & Pantev, C. (2012). Statistical learning effects in musicians and non-musicians: An MEG study. Neuropsychologia, 50(2), 341–349. https://doi.org/10.1016/j.neuropsychologia.2011.12.007.

    Article  Google Scholar 

  203. Park, H., Ince, R. A. A., Schyns, P. G., Thut, G., & Gross, J. (2015). Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners. Current Biology, 25(12), 1649–1653. https://doi.org/10.1016/j.cub.2015.04.049.

    Article  Google Scholar 

  204. Parr, T., & Friston, K. J. (2018). The anatomy of inference: Generative models and brain structure. Frontiers in Computational Neuroscience, 12(November). https://doi.org/10.3389/fncom.2018.00090.

  205. Parr, T., Rees, G., & Friston, K. J. (2018). Computational neuropsychology and Bayesian inference. Frontiers in Human Neuroscience, 12(February), 1–14. https://doi.org/10.3389/fnhum.2018.00061.

    Article  Google Scholar 

  206. Parsons, L. M. (2001). Exploring the functional neuroanatomy of music performance, perception, and comprehension. Annals of the New York Academy of Sciences, 930(1), 211–231. https://doi.org/10.1111/j.1749-6632.2001.tb05735.x.

    Article  Google Scholar 

  207. Pearce, M. (2006). Expectation in melody. 377–405.

    Google Scholar 

  208. Pearce, M. T., Müllensiefen, D., & Wiggins, G. A. (2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception, 39(10), 1367–1391. https://doi.org/10.1068/p6507.

    Article  Google Scholar 

  209. Pearce, M. T., Ruiz, M. H., Kapasi, S., Wiggins, G. A., & Bhattacharya, J. (2010). Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation. NeuroImage, 50(1), 302–313. https://doi.org/10.1016/j.neuroimage.2009.12.019.

    Article  Google Scholar 

  210. Pearce, M. T., & Wiggins, G. A. (2012). Auditory expectation: The information dynamics of music perception and cognition. Topics in Cognitive Science, 4(4), 625–652. https://doi.org/10.1111/j.1756-8765.2012.01214.x.

    Article  Google Scholar 

  211. Pena, M. (2002). Signal-driven computations in speech processing. Science, 298(5593), 604–607. https://doi.org/10.1126/science.1072901.

    Article  Google Scholar 

  212. Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: One phenomenon, two approaches. Trends in Cognitive Sciences, 10(5), 233–238. https://doi.org/10.1016/j.tics.2006.03.006.

    Article  Google Scholar 

  213. Perruchet, P., & Vinter, A. (1998). PARSER: A model for word segmentation. Journal of Memory and Language, 39(2), 246–263.

    Article  Google Scholar 

  214. Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active Inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology, 134, 17–35. https://doi.org/10.1016/j.pneurobio.2015.09.001.

  215. Pickering, M. J., & Clark, A. (2014). Getting ahead: Forward models and their place in cognitive architecture. Trends in Cognitive Sciences, 18(9), 451–456. https://doi.org/10.1016/j.tics.2014.05.006.

  216. Pickles, J. (2013). An introduction to the physiology of hearing.

    Google Scholar 

  217. Pinho, A. L., Ullén, F., Castelo-Branco, M., Fransson, P., & de Manzano, Ö. (2015). Addressing a paradox: Dual strategies for creative performance in introspective and extrospective networks. Cerebral Cortex, 26(7), 3052–3063. https://doi.org/10.1093/cercor/bhv130.

    Article  Google Scholar 

  218. Plante, E., Patterson, D., Dailey, N. S., Kyle, R. A., & Fridriksson, J. (2014). Dynamic changes in network activations characterize early learning of a natural language. Neuropsychologia, 62, 77–86. https://doi.org/10.1016/j.neuropsychologia.2014.07.007.

    Article  Google Scholar 

  219. Plante, E., Patterson, D., Gómez, R., Almryde, K. R., White, M. G., & Asbjørnsen, A. E. (2015). The nature of the language input affects brain activation during learning from a natural language. Journal of Neurolinguistics, 36, 17–34. https://doi.org/10.1016/j.jneuroling.2015.04.005.

  220. Prabhu, V., Sutton, C., & Sauser, W. (2008). Creativity and certain personality traits: Understanding the mediating effect of intrinsic motivation. Creativity Research Journal, 20(1), 53–66. https://doi.org/10.1080/10400410701841955.

    Article  Google Scholar 

  221. Rao, P., Vasuki, M., Sharma, M., Ibrahim, R., & Arciuli, J. (2017). Clinical neurophysiology statistical learning and auditory processing in children with music training: An ERP study. 128, 1270–1281. https://doi.org/10.1016/j.clinph.2017.04.010.

    Article  Google Scholar 

  222. Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79. https://doi.org/10.1038/4580.

  223. Raphael, C., & Stoddard, J. (2004). Functional harmonic analysis using probabilistic models. Computer Music Journal, 28(3), 45–52. https://doi.org/10.1162/0148926041790676.

    Article  Google Scholar 

  224. Rasch, B., & Born, J. (2007). Maintaining memories by reactivation. Current Opinion in Neurobiology, 17(6), 698–703. https://doi.org/10.1016/j.conb.2007.11.007.

  225. Rasch, B., & Jan, B. (2013). About sleep’s role in memory. Physiological Reviews, 93(2), 681–766. https://doi.org/10.1152/Physrev.00032.2012.

    Article  Google Scholar 

  226. Rauschecker, J. P., & Scott, S. K. (2009). Maps and streams in the auditory cortex: Nonhuman primates illuminate human speech processing. Nature Neuroscience, 12(6), 718–724. https://doi.org/10.1038/nn.2331.

    Article  Google Scholar 

  227. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior. https://doi.org/10.1016/S0022-5371(67)80149-X.

    Article  Google Scholar 

  228. Reddy, L., Poncet, M., Self, M. W., Peters, J. C., Douw, L., van Dellen, E., & Roelfsema, P. R. (2015). Learning of anticipatory responses in single neurons of the human medial temporal lobe. Nature Communications, 6, 8556. https://doi.org/10.1038/ncomms9556.

  229. Rinne, T., Antila, S., & Winkler, I. (2001). Mismatch negativity is unaffected by top-down predictive information. NeuroReport, 12(10). Retrieved from https://journals.lww.com/neuroreport/Fulltext/2001/07200/Mismatch_negativity_is_unaffected_by_top_down.33.aspx.

  230. Ritter, W., Sussman, E., Deacon, D., Cowan, N., Vaughan, J. R., & H. G. . (1999). Two cognitive systems simultaneously prepared for opposite events. Psychophysiology, 36(6), 835–838. https://doi.org/10.1111/1469-8986.3660835.

    Article  Google Scholar 

  231. Rogers, T. T., & McClelland, J. L. J. (2004). Semantic cognition: A parallel distributed processing approach. Attention and Performance, 425, 439. https://doi.org/10.1017/S0140525X0800589X.

    Article  Google Scholar 

  232. Rohrmeier, M. (2011). Towards a generative syntax of tonal harmony. Journal of Mathematics and Music, 5(1), 35–53. https://doi.org/10.1080/17459737.2011.573676.

    Article  Google Scholar 

  233. Rohrmeier, M. A., & Cross, I. (2014). Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning. Consciousness and Cognition, 27(1), 155–167. https://doi.org/10.1016/j.concog.2014.03.011.

    Article  Google Scholar 

  234. Rohrmeier, M., & Cross, I. (2008). Statistical properties of tonal harmony in Bach’s Chorales. In Proceedings of the 10th International Conference on Music Perception and Cognition (Vol. 6, no. 4, pp. 123–1319). Retrieved from https://icmpc10.psych.let.hokudai.ac.jp/%5Cnhttps://www.mus.cam.ac.uk/files/2009/09/bachharmony.pdf.

    Google Scholar 

  235. Rohrmeier, M., & Rebuschat, P. (2012). Implicit learning and acquisition of music. Topics in Cognitive Science, 4(4), 525–553. https://doi.org/10.1111/j.1756-8765.2012.01223.x.

    Article  Google Scholar 

  236. Roser, M. E., Fiser, J., Aslin, R. N., & Gazzaniga, M. S. (2011). Right hemisphere dominance in visual statistical learning. Journal of Cognitive Neuroscience, 23(5), 1088–1099. https://doi.org/10.1162/jocn.2010.21508.

    Article  Google Scholar 

  237. Roux, F., & Uhlhaas, P. J. (2014). Working memory and neural oscillations: Alpha-gamma versus theta-gamma codes for distinct WM information? Trends in Cognitive Sciences, 18(1), 16–25. https://doi.org/10.1016/j.tics.2013.10.010.

    Article  Google Scholar 

  238. Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning by 8-month-old infants. Science, 274(December), 1926–1928.

    Article  Google Scholar 

  239. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science. https://doi.org/10.1126/science.274.5294.1926.

    Article  Google Scholar 

  240. Saffran, J. R., Hauser, M., Seibel, R., Kapfhamer, J., Tsao, F., & Cushman, F. (2008). Grammatical pattern learning by human infants and cotton-top tamarin monkeys. Cognition, 107(2), 479–500. https://doi.org/10.1016/j.cognition.2007.10.010.

    Article  Google Scholar 

  241. Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70(1), 27–52. https://doi.org/10.1016/S0010-0277(98)00075-4.

  242. Saffran, J. R., Reeck, K., Niebuhr, A., & Wilson, D. (2005). Changing the tune: The structure of the input affects infants’ use of absolute and relative pitch. Developmental Science, 8(1), 1–7. https://doi.org/10.1111/j.1467-7687.2005.00387.x.

    Article  Google Scholar 

  243. Saffran, J. R., & Wilson, D. P. (2003). From syllables to syntax: Multilevel statistical learning by 12-month-old infants. Infancy, 4(2), 273–284. https://doi.org/10.1207/S15327078IN0402_07.

    Article  Google Scholar 

  244. Saggar, M., Quintin, E.-M., Bott, N. T., Kienitz, E., Chien, Y., Hong, D.W.-C., & Reiss, A. L. (2016). Changes in brain activation associated with spontaneous improvization and figural creativity after design-thinking-based training: A longitudinal fMRI study. Cerebral Cortex, 27(7), 3542–3552. https://doi.org/10.1093/cercor/bhw171.

    Article  Google Scholar 

  245. Sakai, K., Kitaguchi, K., & Hikosaka, O. (2003). Chunking during human visuomotor sequence learning. Experimental Brain Research, 152(2), 229–242. https://doi.org/10.1007/s00221-003-1548-8.

    Article  Google Scholar 

  246. Sanders, L. D., Newport, E. L., & Neville, H. J. (2002). Segmenting nonsense: An event-related potential index of perceived onsets in continuous speech. Nature Neuroscience, 5(7), 700–703. https://doi.org/10.1038/nn873.

    Article  Google Scholar 

  247. Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M., & Turk-Browne, N. B. (2014). The Necessity of the medial temporal lobe for statistical learning. Journal of Cognitive Neuroscience, 26(8), 1736–1747. https://doi.org/10.1162/jocn_a_00578.

    Article  Google Scholar 

  248. Schenker, H., & Jonas, O. (1956). Neue musikalische Theorien und Phantasien. Retrieved from https://books.google.de/books?id=rTuKxwEACAAJ.

  249. Schmahmann, J. D. (2004). Disorders of the cerebellum: Ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. The Journal of Neuropsychiatry and Clinical Neurosciences, 16(3), 367–378. https://doi.org/10.1176/jnp.16.3.367.

    Article  Google Scholar 

  250. Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2), 173–187. https://doi.org/10.1080/09540090600768658.

    Article  Google Scholar 

  251. Schmidhuber, J. (2010). Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Transactions on Autonomous Mental Development, 2(3), 230–247. https://doi.org/10.1109/TAMD.2010.2056368.

    Article  Google Scholar 

  252. Schwartenbeck, P., FitzGerald, T., Dolan, R. J., & Friston, K. (2013). Exploration, novelty, surprise, and free energy minimization. Frontiers in Psychology, 4(OCT), 1–5. https://doi.org/10.3389/fpsyg.2013.00710.

  253. Scott-Phillips, T., & Blythe, R. (2013). Why is combinatorial communication rare in the natural world? Talk first(Abstract): No.

    Google Scholar 

  254. Seidenberg, M. S. (1997). Language acquisition and use: Learning and applying probabilistic constraints. Science. https://doi.org/10.1126/science.275.5306.1599.

    Article  Google Scholar 

  255. Servan-Schreiber, E., & Anderson, J. R. (1990). Learning artificial grammars with competitive chunking. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(4), 592–608. https://doi.org/10.1037/0278-7393.16.4.592.

    Article  Google Scholar 

  256. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(April 1924), 623–656.

    Google Scholar 

  257. Shimizu, R. E., Wu, A. D., Samra, J. K., & Knowlton, B. J. (2017). The impact of cerebellar transcranial direct current stimulation (Tdcs) on learning fine-motor sequences. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711). https://doi.org/10.1098/rstb.2016.0050.

  258. Simonton, D. K. (2010). Creative thought as blind-variation and selective-retention: Combinatorial models of exceptional creativity. Physics of Life Reviews, 7(2), 156–179. https://doi.org/10.1016/j.plrev.2010.02.002.

  259. Sinex, D. G., Guzik, H., Li, H., & Henderson Sabes, J. (2003). Responses of auditory nerve fibers to harmonic and mistuned complex tones. Hearing Research, 182(1), 130–139. https://doi.org/https://doi.org/10.1016/S0378-5955(03)00189-8.

  260. Skoe, E., Krizman, J., Spitzer, E., & Kraus, N. (2015). Prior experience biases subcortical sensitivity to sound patterns. Journal of Cognitive Neuroscience, 27(1), 124–140. https://doi.org/10.1162/jocn_a_00691.

    Article  Google Scholar 

  261. Sloutsky, V. M. (2010). From perceptual categories to concepts: What develops? Cognitive Science, 34(7), 1244–1286. https://doi.org/10.1111/j.1551-6709.2010.01129.x.

    Article  Google Scholar 

  262. Soon, C. S., Brass, M., Heinze, H.-J., & Haynes, J.-D. (2008). Unconscious determinants of free decisions in the human brain. Nature Neuroscience, 11, 543. https://doi.org/10.1038/nn.2112.

  263. Strange, B. A., Duggins, A., Penny, W., Dolan, R. J., & Friston, K. J. (2005). Information theory, novelty and hippocampal responses: unpredicted or unpredictable? Neural Networks, 18(3), 225–230. https://doi.org/10.1016/j.neunet.2004.12.004.

  264. Sun, L., Liu, F., Zhou, L., & Jiang, C. (2018). Musical training modulates the early but not the late stage of rhythmic syntactic processing. Psychophysiology, 55(2), e12983. https://doi.org/10.1111/psyp.12983.

    Article  Google Scholar 

  265. Sussman, E., Winkler, I., & Schröger, E. (2003). Top-down control over involuntary attention switching in the auditory modality. Psychonomic Bulletin & Review, 10(3), 630–637.

    Article  Google Scholar 

  266. Swain, I., Zelazo, P., & Clifton, R. (1993). Newborn infants’ memory for speech sounds retained over 24 hours. Developmental Psychology, 29, 312–323. https://doi.org/10.1037/0012-1649.29.2.312.

    Article  Google Scholar 

  267. Taylor, K. I., Moss, H. E., Stamatakis, E. A., & Tyler, L. K. (2006). Binding crossmodal object features in perirhinal cortex. Proceedings of the National Academy of Sciences, 103(21), 8239–8244. https://doi.org/10.1073/pnas.0509704103.

  268. Teinonen, T., Fellman, V., Näätänen, R., Alku, P., & Huotilainen, M. (2009). Statistical language learning in neonates revealed by event-related brain potentials. 8. https://doi.org/10.1186/1471-2202-10-21.

  269. Thiessen, E. D. (2017). What’s statistical about learning? Insights from modelling statistical learning as a set of memory processes. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711). https://doi.org/10.1098/rstb.2016.0056.

  270. Thiessen, E. D., Kronstein, A. T., & Hufnagle, D. G. (2013). The extraction and integration framework: A two-process account of statistical learning. 139(4), 792–814. https://doi.org/10.1037/a0030801.

    Article  Google Scholar 

  271. Thiessen, E. D., & Pavlik, P. I. (2013). iMinerva: A mathematical model of distributional statistical learning. Cognitive Science. https://doi.org/10.1111/cogs.12011.

    Article  Google Scholar 

  272. Tillmann, B., Bharucha, J. J., & Bigand, E. (2000). Implicit learning of tonality: A self-organizing approach. Psychological Review, 107(4), 885–913. https://doi.org/10.1037/0033-295X.107.4.885.

    Article  Google Scholar 

  273. Tillmann, B., Bigand, E., & Pineau, M. (1998). Effects of global and local contexts on harmonic expectancy. Music Perception: An Interdisciplinary Journal, 16(1), 99–117. https://doi.org/10.2307/40285780.

  274. Tillmann, B., Koelsch, S., Escoffier, N., Bigand, E., Lalitte, P., Friederici, A. D., & von Cramon, D. Y. (2006). Cognitive priming in sung and instrumental music: Activation of inferior frontal cortex. NeuroImage, 31(4), 1771–1782. https://doi.org/10.1016/j.neuroimage.2006.02.028.

    Article  Google Scholar 

  275. Tishby, N., & Polani, D. (2011). Information theory of decisions and actions. Perception-Action Cycle, 601–636. https://doi.org/10.1007/978-1-4419-1452-1_19.

  276. Toro, J. M., Sinnett, S., & Soto-Faraco, S. (2005). Speech segmentation by statistical learning depends on attention. Cognition, 97(2), 25–34. https://doi.org/10.1016/j.cognition.2005.01.006.

    Article  Google Scholar 

  277. Tremblay, P., Baroni, M., & Hasson, U. (2013). Processing of speech and non-speech sounds in the supratemporal plane: Auditory input preference does not predict sensitivity to statistical structure. NeuroImage, 66, 318–332. https://doi.org/10.1016/j.neuroimage.2012.10.055.

  278. Tsogli, V., Jentschke, S., Daikoku, T., & Koelsch, S. (2019). When the statistical MMN meets the physical MMN. Scientific Reports, 9(1), 5563. https://doi.org/10.1038/s41598-019-42066-4.

    Article  Google Scholar 

  279. Turk-browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2009). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21(10), 1934–1945. https://doi.org/10.1162/jocn.2009.21131.

    Article  Google Scholar 

  280. Turk-Browne, N. B., Scholl, B. J., Johnson, M. K., & Chun, M. M. (2010). Implicit perceptual anticipation triggered by statistical learning. The Journal of Neuroscience, 30(33), 11177–11187. https://doi.org/10.1523/JNEUROSCI.0858-10.2010.

  281. Uddin, L. Q. (2014). Salience processing and insular cortical function and dysfunction. Nature Reviews Neuroscience, 16, 55. https://doi.org/10.1038/nrn3857.

  282. van Kesteren, M. T. R., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012). How schema and novelty augment memory formation. Trends in Neurosciences, 35(4), 211–219. https://doi.org/10.1016/j.tins.2012.02.001.

    Article  Google Scholar 

  283. Vandervert, L. R., Schimpf, P. H., & Liu, H. (2007). How working memory and the cerebellum collaborate to produce creativity and innovation. Creativity Research Journal, 19(1), 1–18. https://doi.org/10.1080/10400410709336873.

    Article  Google Scholar 

  284. Von Fange, E. K. (1959). Professional creativity. Retrieved from https://books.google.de/books?id=9PMyAAAAMAAJ.

  285. Vuvan, D. T., Zendel, B. R., & Peretz, I. (2018). Random feedback makes listeners tone-deaf. Scientific Reports, 8(1), 7283. https://doi.org/10.1038/s41598-018-25518-1.

    Article  Google Scholar 

  286. Wagner, U., Gais, S., Haider, H., Verleger, R., & Born, J. (2004). Sleep inspires insight. Nature, 427, 352. https://doi.org/10.1038/nature02223.

  287. Walker, M. P., Liston, C., Hobson, J. A., & Stickgold, R. (2002). Cognitive flexibility across the sleep–wake cycle: REM-sleep enhancement of anagram problem solving. Cognitive Brain Research, 14(3), 317–324. https://doi.org/10.1016/S0926-6410(02)00134-9.

  288. Walker, M. P., & Stickgold, R. (2006). Sleep, memory, and plasticity. Annual Review of Psychology, 57(1), 139–166. https://doi.org/10.1146/annurev.psych.56.091103.070307.

    Article  Google Scholar 

  289. Ward, A. M., Schultz, A. P., Huijbers, W., Van Dijk, K. R. A., Hedden, T., & Sperling, R. A. (2014). The parahippocampal gyrus links the default-mode cortical network with the medial temporal lobe memory system. Human Brain Mapping, 35(3), 1061–1073. https://doi.org/10.1002/hbm.22234.

    Article  Google Scholar 

  290. Werker, J. F., Yeung, H. H., & Yoshida, K. A. (2012). How do infants become experts at native-speech perception? Current Directions in Psychological Science. https://doi.org/10.1177/0963721412449459.

    Article  Google Scholar 

  291. Wiggins, G. A. (2018). Creativity, information, and consciousness: The information dynamics of thinking. Physics of Life Reviews, 1, 1–39. https://doi.org/10.1016/j.plrev.2018.05.001.

    Article  Google Scholar 

  292. Wiggins, G. A. (2019). Consolidation as re-representation: Revising the meaning of memory. Frontiers in Psychology, 1–22.

    Google Scholar 

  293. Winkler, I., & Czigler, I. (2012). Evidence from auditory and visual event-related potential (ERP) studies of deviance detection (MMN and vMMN) linking predictive coding theories and perceptual object representations. International Journal of Psychophysiology, 83(2), 132–143. https://doi.org/10.1016/j.ijpsycho.2011.10.001.

  294. Wittmann, B. C., Daw, N. D., Seymour, B., & Dolan, R. J. (2008). Striatal activity underlies novelty-based choice in humans. Neuron, 58(6), 967–973. https://doi.org/10.1016/j.neuron.2008.04.027.

    Article  Google Scholar 

  295. Wixted, J. T. (2004). The psychology and neuroscience of forgetting. Annual Review of Psychology, 55(1), 235–269. https://doi.org/10.1146/annurev.psych.55.090902.141555.

    Article  Google Scholar 

  296. Yumoto, M., & Daikoku, T. (2016). Basic function. Clinical Applications of Magnetoencephalography. https://doi.org/10.1007/978-4-431-55729-6_5.

    Article  Google Scholar 

  297. Yumoto, M., & Daikoku, T. (2018). Neurophysiological studies on auditory statistical learning [in Japanese] 聴覚刺激列の統計学習の神経生理学的研究. Japanese Journal of Cognitive Neuroscience(認知神経科学), 20(1), 38–43.

    Google Scholar 

  298. Zabelina, D. L., & Andrews-Hanna, J. R. (2016). Dynamic network interactions supporting internally-oriented cognition. Current Opinion in Neurobiology, 40, 86–93. https://doi.org/10.1016/j.conb.2016.06.014.

  299. Zubicaray, G. De, Arciuli, J., & Mcmahon, K. (2013). Putting an “End” to the Motor Cortex Representations of Action Words. 1957–1974. https://doi.org/10.1162/jocn.

Download references

Acknowledgements

This chapter was supported by Suntory Foundation. The funders had no role in decision to publish and preparation of the manuscript.

Author information

Authors and Affiliations

  1. International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 1130033, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan

    Tatsuya Daikoku

Authors
  1. Tatsuya Daikoku
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tatsuya Daikoku .

Editor information

Editors and Affiliations

  1. Interdisciplinary Centre for Computer Music Research (ICCMR), University of Plymouth, Plymouth, UK

    Eduardo Reck Miranda

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Daikoku, T. (2021). Discovering the Neuroanatomical Correlates of Music with Machine Learning. In: Miranda, E.R. (eds) Handbook of Artificial Intelligence for Music. Springer, Cham. https://doi.org/10.1007/978-3-030-72116-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72116-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72115-2

  • Online ISBN: 978-3-030-72116-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation