References
- 1Anwyl-Irvine, A. L., Massonnié, J., Flitton, A., Kirkham, N., & Evershed, J. K. (2020). Gorilla in our midst: An online behavioral experiment builder. Behavior Research Methods, 52(1), 388–407. DOI: 10.3758/s13428-019-01237-x
- 2Arciuli, J., & Simpson, I. C. (2011). Statistical learning in typically developing children: the role of age and speed of stimulus presentation. Developmental Science, 14(3), 464–473. DOI: 10.1111/j.1467-7687.2009.00937.x
- 3Assaneo, M. F., & Poeppel, D. (2018). The coupling between auditory and motor cortices is rate-restricted: Evidence for an intrinsic speech-motor rhythm. Science Advances, 4(2). DOI: 10.1126/sciadv.aao3842
- 4Assaneo, 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 2019 22: 4, 22(4), 627–632. DOI: 10.1038/s41593-019-0353-z
- 5Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. DOI: 10.1016/j.jml.2007.12.005
- 6Baddeley, A., Gathercole, S., & Papagno, C. (1998). The Phonological Loop as a Language Learning Device. Psychological Review, 105(1), 158–173. DOI: 10.1037/0033-295X.105.1.158
- 7Baker, C. I., Olson, C. R., & Behrmann, M. (2004). Role of Attention and Perceptual Grouping in Visual Statistical Learning. Psychological Science, 16, 460–466. DOI: 10.1111/j.0956-7976.2004.00702.x
- 8Baldo, J. V., & Cronkers, N. F. (2006). The role of inferior parietal and inferior frontal cortex in working memory. Neuropsychology, 20(5), 529–538. DOI: 10.1037/0894-4105.20.5.529
- 9Bates, D., Mächler, M., Bolker, B. M., & Walker, S. C. (2014). Fitting Linear Mixed-Effects Models using lme4. Journal of Statistical Software, 67(1). DOI: 10.18637/jss.v067.i01
- 10Batterink, L. J., & Paller, K. A. (2017). Online neural monitoring of statistical learning. Cortex, 90, 31–45. DOI: 10.1016/j.cortex.2017.02.004
- 11Batterink, L. J., Paller, K. A., & Reber, P. J. (2019).
Understanding the Neural Bases of Implicit and Statistical Learning . In Topics in Cognitive Science, 11(3), 482–503, Wiley-Blackwell. DOI: 10.1111/tops.12420 - 12Batterink, L. J., Reber, P. J., & Paller, K. A. (2015a). Functional differences between statistical learning with and without explicit training. Learning & Memory, 22(11), 544–556. DOI: 10.1101/lm.037986.114
- 13Batterink, L. J., Reber, P. J., Neville, H. J., & Paller, K. A. (2015b). Implicit and explicit contributions to statistical learning. Journal of Memory and Language, 83, 62–78. DOI: 10.1016/j.jml.2015.04.004
- 14Boersma, P., & Weenink, D. (2023). Praat: doing phonetics by computer [Computer program] (6.3.10).
- 15Bogaerts, L., Richter, C. G., Landau, A. N., & Frost, R. (2020). Beta-band activity is a signature of statistical learning. Journal of Neuroscience, 40(39), 7523–7530. DOI: 10.1523/JNEUROSCI.0771-20.2020
- 16Bogaerts, L., Siegelman, N., Christiansen, M. H., & Frost, R. (2022). Is there such a thing as a ‘good statistical learner’? Trends in Cognitive Sciences, 26(1), 25–37. DOI: 10.1016/j.tics.2021.10.012
- 17Bood, R. J., Nijssen, M., Van Der Kamp, J., & Roerdink, M. (2013). The power of auditory-motor synchronization in sports: Enhancing running performance by coupling cadence with the right beats. PloS One, 8(8),
e70758 . DOI: 10.1371/journal.pone.0070758 - 18Conway, C. M. (2020). How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning. Neuroscience & Biobehavioral Reviews, 112, 279–299. DOI: 10.1016/j.neubiorev.2020.01.032
- 19Conway, C. M., & Christiansen, M. H. (2005). Modality-constrained statistical learning of tactile, visual, and auditory sequences. Journal of Experimental Psychology: Learning Memory and Cognition, 31(1), 24–39. DOI: 10.1037/0278-7393.31.1.24
- 20Deschamps, I., Courson, M., Anthony, S. D., & Tremblay, P. (2020). The phonological loop: is speech special? Experimental Brain Research, 238, 2307–2321. DOI: 10.1007/s00221-020-05886-9
- 21Ding, 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. DOI: 10.1016/j.neubiorev.2017.02.011
- 22Doelling, K. B., & Assaneo, F. M. (2021). Neural oscillations are a start toward understanding brain activity rather than the end. PLOS Biology, 19(5),
e3001234 . DOI: 10.1371/journal.pbio.3001234 - 23Du, Y., & Zatorre, R. J. (2017). Musical training sharpens and bonds ears and tongue to hear speech better. Proceedings of the National Academy of Sciences of the United States of America, 114(51), 13579–13584. DOI: 10.1073/pnas.1712223114
- 24Emberson, L. L., Conway, C. M., & Christiansen, M. H. (2011). Timing is everything: Changes in presentation rate have opposite effects on auditory and visual implicit statistical learning. Quarterly Journal of Experimental Psychology, 64(5), 1021–1040. DOI: 10.1080/17470218.2010.538972
- 25Erickson, L. C., Kaschak, M. P., Thiessen, E. D., & Berry, C. A. S. (2016). Individual Differences in Statistical Learning: Conceptual and Measurement Issues. Collabra, 2(1). DOI: 10.1525/collabra.41
- 26Erickson, L. C., & Thiessen, E. D. (2015). Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition. Developmental Review, 37, 66–108. DOI: 10.1016/j.dr.2015.05.002
- 27Evans, S., & Davis, M. H. (2015). Hierarchical Organization of Auditory and Motor Representations in Speech Perception: Evidence from Searchlight Similarity Analysis. Cerebral Cortex, 25(12), 4772–4788. DOI: 10.1093/cercor/bhv136
- 28Farthouat, J., Franco, A., Mary, A., Delpouve, J., Wens, V., Op De Beeck, M., De Tiège, X., & Peigneux, P. (2017). Auditory Magnetoencephalographic Frequency-Tagged Responses Mirror the Ongoing Segmentation Processes Underlying Statistical Learning. Brain Topography, 30, 220–232. DOI: 10.1007/s10548-016-0518-y
- 29Forest, T. A., Siegelman, N., & Finn, A. S. (2022). Attention Shifts to More Complex Structures With Experience. Psychological Science, 33(12), 2059–2072. DOI: 10.1177/09567976221114055
- 30Frost, R., Armstrong, B. C., & Christiansen, M. H. (2019). Statistical learning research: A critical review and possible new directions. Psychological Bulletin, 145(12), 1128–1153. DOI: 10.1037/bul0000210
- 31Frost, 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. DOI: 10.1016/j.tics.2014.12.010
- 32Giraud, A. L., & Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience, 15(4), 511–517. DOI: 10.1038/nn.3063
- 33Goujon, A., Didierjean, A., & Thorpe, S. (2015). Investigating implicit statistical learning mechanisms through contextual cueing. Trends in Cognitive Sciences, 19(9), 524–533. DOI: 10.1016/j.tics.2015.07.009
- 34Hunt, R. H., & Aslin, R. N. (2001). Statistical learning in a serial reaction time task: Access to separable statistical cues by individual learners. Journal of Experimental Psychology: General, 130(4), 658–680. DOI: 10.1037/0096-3445.130.4.658
- 35Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59(4), 434–446. DOI: 10.1016/j.jml.2007.11.007
- 36Janacsek, K., Fiser, J., & Nemeth, D. (2012). The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan. Developmental Science, 15(4), 496–505. DOI: 10.1111/j.1467-7687.2012.01150.x
- 37Janacsek, K., & Nemeth, D. (2013). Implicit sequence learning and working memory: Correlated or complicated? Cortex, 49(8), 2001–2006. DOI: 10.1016/j.cortex.2013.02.012
- 38Krogh, L., Vlach, H. A., & Johnson, S. P. (2013). Statistical learning across development: flexible yet constrained, 3(598). DOI: 10.3389/fpsyg.2012.00598
- 39Lakatos, P., Shah, A. S., Knuth, K. H., Ulbert, I., Karmos, G., & Schroeder, C. E. (2005). An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. Journal of Neurophysiology, 94(3), 1904–1911. DOI: 10.1152/jn.00263.2005
- 40Lammertink, I., Boersma, P., Wijnen, F., & Rispens, J. (2019). Auditory statistical learning in children: Novel insights from an online measure. Applied Psycholinguistics, 40, 279–302. DOI: 10.1017/S0142716418000577
- 41Liebenthal, E., & Möttönen, R. (2018). An interactive model of auditory-motor speech perception. Brain and Language, 187, 33–40. DOI: 10.1016/j.bandl.2017.12.004
- 42Lopez-Barroso, D., De Diego-Balaguer, R., Cunillera, T., Camara, E., Münte, T. F., & Rodriguez-Fornells, A. (2011). Language Learning under Working Memory Constraints Correlates with Microstructural Differences in the Ventral Language Pathway. Cerebral Cortex, 21(12), 2742–2750. DOI: 10.1093/cercor/bhr064
- 43Lukics, K. S., & Lukács, Á. (2022). Modality, presentation, domain and training effects in statistical learning. Scientific Reports, 12(1), 1–14. DOI: 10.1038/s41598-022-24951-7
- 44Mares, C., Echavarría Solana, R., & Assaneo, M. F. (2023). Auditory-motor synchronization varies among individuals and is critically shaped by acoustic features. Communications Biology, 6(1), 658. DOI: 10.1038/s42003-023-04976-y
- 45Marvel, C. L., & Desmond, J. E. (2012). From storage to manipulation: How the neural correlates of verbal working memory reflect varying demands on inner speech. Brain and Language, 120(1), 42–51. DOI: 10.1016/j.bandl.2011.08.005
- 46Misyak, J. B., & Christiansen, M. H. (2012). Statistical Learning and Language: An Individual Differences Study. Language Learning, 62(1), 302–331. DOI: 10.1111/j.1467-9922.2010.00626.x
- 47Miura, A., Fujii, S., Yamamoto, Y., & Kudo, K. (2015). Motor Control of Rhythmic Dance from a Dynamical Systems Perspective a Review. Journal of Dance Medicine & Science, 19(1), 11–21. DOI: 10.12678/1089-313X.19.1.11
- 48Moser, J., Batterink, L., Li Hegner, Y., Schleger, F., Braun, C., Paller, K. A., & Preissl, H. (2021). Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge. NeuroImage, 240. DOI: 10.1016/j.neuroimage.2021.118378
- 49Möttönen, R., Dutton, R., & Watkins, K. E. (2013). Auditory-Motor Processing of Speech Sounds. Cerebral Cortex, 23(5), 1190–1197. DOI: 10.1093/cercor/bhs110
- 50Orpella, J., Assaneo, M. F., Ripollés, P., Noejovich, L., López-Barroso, D., de Diego-Balaguer, R., & Poeppel, D. (2022). Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech. PLOS Biology, 20(7),
e3001712 . DOI: 10.1371/journal.pbio.3001712 - 51Park, H., Thut, G., & Gross, J. (2020). Predictive entrainment of natural speech through two fronto-motor top-down channels. Language, Cognition and Neuroscience, 35(6), 739–751. DOI: 10.1080/23273798.2018.1506589
- 52Patel, A. D., & Iversen, J. R. (2014). The evolutionary neuroscience of musical beat perception: The Action Simulation for Auditory Prediction (ASAP) hypothesis. Frontiers in Systems Neuroscience, 8, 57. DOI: 10.3389/fnsys.2014.00057
- 53Perruchet, P., & Vinter, A. (1998). PARSER: A Model for Word Segmentation. Journal of Memory and Language, 39(2), 246–263. DOI: 10.1006/jmla.1998.2576
- 54Poeppel, D., & Assaneo, M. F. (2020). Speech rhythms and their neural foundations. Nature Reviews Neuroscience, 21(6), 322–334. DOI: 10.1038/s41583-020-0304-4
- 55Provasi, J., Anderson, D. I., & Barbu-Roth, M. (2014). Rhythm perception, production, and synchronization during the perinatal period. Frontiers in Psychology, 5, 1048. DOI: 10.3389/fpsyg.2014.01048
- 56Pulvermüller, F., Huss, M., Kherif, F., Del Prado Martin, F. M., Hauk, O., & Shtyrov, Y. (2006). Motor cortex maps articulatory features of speech sounds. Proceedings of the National Academy of Sciences, 103(20), 7865–7870. DOI: 10.1073/pnas.0509989103
- 57Raviv, L., & Arnon, I. (2018). The developmental trajectory of children’s auditory and visual statistical learning abilities: modality-based differences in the effect of age. Developmental Science, 21(4),
e12593 . DOI: 10.1111/desc.12593 - 58Rimmele, J. M., Morillon, B., Poeppel, D., & Arnal, L. H. (2018). Proactive Sensing of Periodic and Aperiodic Auditory Patterns. Trends in Cognitive Sciences, 22(10), 870–882. DOI: 10.1016/j.tics.2018.08.003
- 59Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical Learning by 8-Month-Old Infants. Science, 274(5294), 1926–1928. DOI: 10.1126/science.274.5294.1926
- 60Saffran, J. R., & Kirkham, N. Z. (2017). Infant Statistical Learning. Annual Review of Psychology, 69, 181–203. DOI: 10.1146/annurev-psych-122216-011805
- 61Shen, J., Zhang, G., Yao, L., & Zhao, X. (2015). Real-time fMRI training-induced changes in regional connectivity mediating verbal working memory behavioral performance. Neuroscience, 289, 144–152. DOI: 10.1016/j.neuroscience.2014.12.071
- 62Siegelman, N. (2020). Statistical learning abilities and their relation to language. Language and Linguistics Compass, 14(3),
e12365 . DOI: 10.1111/lnc3.12365 - 63Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. Behavior Research Methods, 48, 418–432. DOI: 10.3758/s13428-016-0719-z
- 64Siegelman, N., Bogaerts, L., Kronenfeld, O., & Frost, R. (2018). Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities? Cognitive Science, 42, 692–727. DOI: 10.1111/cogs.12556
- 65Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: Theoretical perspectives and empirical evidence. Journal of Memory and Language, 81, 105–120. DOI: 10.1016/j.jml.2015.02.001
- 66Singmann, H., Bolker, B., Westfall, J., & Aust, F. (2015). Package “afex.”
- 67Singmann, H., & Kellen, D. (2019).
An Introduction to Mixed Models for Experimental Psychology . In D. Spieler & E. Schumacher (Eds.), New Methods in Cognitive Psychology (1st ed., pp. 4–31). Routledge. DOI: 10.4324/9780429318405-2 - 68Skipper, J. I., Devlin, J. T., & Lametti, D. R. (2017). The hearing ear is always found close to the speaking tongue: Review of the role of the motor system in speech perception. Brain and Language, 164, 77–105. DOI: 10.1016/j.bandl.2016.10.004
- 69Smalle, E. H. M., Bogaerts, L., Simonis, M., Duyck, W., Page, M. P. A., Edwards, M. G., & Szmalec, A. (2016). Can Chunk Size Differences Explain Developmental Changes in Lexical Learning? Frontiers in Psychology, 1. DOI: 10.3389/fpsyg.2015.01925
- 70Smalle, E. H. M., Daikoku, T., Szmalec, A., Duyck, W., & Onen, R. M. (2022). Unlocking adults’ implicit statistical learning by cognitive depletion. Proceedings of the National Academy of Sciences of the United States of America, 119(2),
e2026011119 . DOI: 10.1073/pnas.2026011119 - 71Smalle, E. H. M., Rogers, J., & Möttönen, R. (2015). Dissociating Contributions of the Motor Cortex to Speech Perception and Response Bias by Using Transcranial Magnetic Stimulation. Cerebral Cortex, 25(10), 3690–3698. DOI: 10.1093/cercor/bhu218
- 72Ten Oever, S., & Martin, A. E. (2021). An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions. ELife, 10. DOI: 10.7554/eLife.68066
- 73Thiessen, E. D., Kronstein, A. T., & Hufnagle, D. G. (2013). The extraction and integration framework: A two-process account of statistical learning. Psychological Bulletin, 139(4), 792–814. DOI: 10.1037/a0030801
- 74Varnet, L., Ortiz-Barajas, M. C., Erra, R. G., Gervain, J., & Lorenzi, C. (2017). A cross-linguistic study of speech modulation spectra. The Journal of the Acoustical Society of America, 142(4), 1976. DOI: 10.1121/1.5006179
- 75Wan, C. Y., Bazen, L., Baars, R., Libenson, A., Zipse, L., Zuk, J., Norton, A., & Schlaug, G. (2011). Auditory-Motor Mapping Training as an Intervention to Facilitate Speech Output in Non-Verbal Children with Autism: A Proof of Concept Study. PLOS ONE, 6(9),
e25505 . DOI: 10.1371/journal.pone.0025505 - 76Woodruff Carr, K., White-Schwoch, T., Tierney, A. T., Strait, D. L., & Kraus, N. (2014). Beat synchronization predicts neural speech encoding and reading readiness in preschoolers. Proceedings of the National Academy of Sciences, 111(40), 14559–14564. DOI: 10.1073/pnas.1406219111
- 77Woods, K. J. P., Siegel, M. H., Traer, J., & McDermott, J. H. (2017). Headphone screening to facilitate web-based auditory experiments. Attention, Perception, and Psychophysics, 79(7), 2064–2072. DOI: 10.3758/s13414-017-1361-2
- 78Zatorre, R. J., Chen, J. L., & Penhune, V. B. (2007). When the brain plays music: Auditory–motor interactions in music perception and production. Nature Reviews Neuroscience, 8(7). DOI: 10.1038/nrn2152
