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Domain Generality and Specificity of Statistical Learning and its Relation with Reading Ability

  • Yi-Hui Hung
  • Stephen J. Frost
  • Kenneth R. Pugh
Part of the Literacy Studies book series (LITS, volume 16)

Abstract

Reading is to map orthographic units onto an existing phonologic-semantic system of its corresponding spoken language. Neuroimaging studies have shown that the print-speech co-activation in perisylvian networks for print-speech conversion is a universal neural signature of skilled readers. In addition to large commonality, small language differences are suggested: phonological knowledge from spoken language is useful for orthographic learning in transparent writing systems whereas visuospatial processing is more demanded for opaque writing systems, like Chinese. An emerging research suggests that reading acquisition may also reflect a general statistical learning (SL) capacity to implicitly assimilate the systematic structures of a linguistic environment. It is unclear whether visual and auditory SL play similar roles in learning to read different writing systems and whether the experience of learning of any given orthographic system changes the way one detects and computes statistical patterns. To understand the bidirectional relations between SL and reading experience, future research could examine the relative contribution of visual and auditory SL to individual differences in learning different writing systems, e.g., English vs. Chinese and track the changes of SL after learning different writing systems. Such studies will also shed light on the debate in universal account of learning difficulty in dyslexia.

Keywords

Statistical learning Plasticity Language experience Individual differences Causes Consequences 

References

  1. Apel, K., Wolter, J. A., & Masterson, J. J. (2006). Effects of phonotactic and orthotactic probabilities during fast mapping on 5-year-olds’ learning to spell. Developmental Neuropsychology, 29(1), 21–42. https://doi.org/10.1207/s15326942dn2901_3Google Scholar
  2. Arciuli, J., & Simpson, I. C. (2012). Statistical learning is related to reading ability in children and adults. Cognitive Science, 36(2), 286–304. https://doi.org/10.1111/j.1551-6709.2011.01200.xGoogle Scholar
  3. Binder, J. R., Medler, D. A., Westbury, C. F., Liebenthal, E., & Buchanan, L. (2006). Tuning of the human left fusiform gyrus to sublexical orthographic structure. NeuroImage, 33(2), 739–748. https://doi.org/10.1016/j.neuroimage.2006.06.053Google Scholar
  4. Blomert, L. (2011). The neural signature of orthographic-phonological binding in successful and failing reading development. NeuroImage, 57(3), 695–703. https://doi.org/10.1016/j.neuroimage.2010.11.003Google Scholar
  5. Boets, B., Wouters, J., van Wieringen, A., Smedt, B. de, & Ghesquière, P. (2008). Modelling relations between sensory processing, speech perception, orthographic and phonological ability, and literacy achievement. Brain and Language, 106(1), 29–40. https://doi.org/10.1016/j.bandl.2007.12.004Google Scholar
  6. Bogaerts, L., Szmalec, A., Maeyer, M. de, Page, M. P. A., & Duyck, W. (2016). The involvement of long-term serial-order memory in reading development: A longitudinal study. Journal of Experimental Child Psychology, 145, 139–156. https://doi.org/10.1016/j.jecp.2015.12.008Google Scholar
  7. Bosseler, A. N., Teinonen, T., Tervaniemi, M., & Huotilainen, M. (2016). Infant directed speech enhances statistical learning in newborn infants: An ERP study. PloS One, 11(9), e0162177.  https://doi.org/10.1371/journal.pone.0162177Google Scholar
  8. Brem, S., Bach, S., Kucian, K., Guttorm, T. K., Martin, E., Lyytinen, H., … Richardson, U. (2010). Brain sensitivity to print emerges when children learn letter-speech sound correspondences. Proceedings of the National Academy of Sciences of the United States of America, 107(17), 7939–7944.  https://doi.org/10.1073/pnas.0904402107Google Scholar
  9. Bulf, H., Johnson, S. P., & Valenza, E. (2011). Visual statistical learning in the newborn infant. Cognition, 121(1), 127–132. https://doi.org/10.1016/j.cognition.2011.06.010Google Scholar
  10. Caravolas, M., Lervag, A., Defior, S., Seidlova Malkova, G., & Hulme, C. (2013). Different patterns, but equivalent predictors, of growth in reading in consistent and inconsistent orthographies. Psychological Science, 24(8), 1398–1407. https://doi.org/10.1177/0956797612473122Google Scholar
  11. Carlisle, J. F., & Feldman, L. B. (1995). Morphological awareness and early reading achievement. In L. B. Feldmann (Ed.), Morphological aspects of language processing (pp. 189–209). Hillsdale, NJ: Erlbaum.Google Scholar
  12. Carr, K. W., 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 of the United States of America, 111(40), 14559–14564.  https://doi.org/10.1073/pnas.1406219111Google Scholar
  13. Cattinelli, I., Borghese, N. A., Gallucci, M., & Paulesu, E. (2013). Reading the reading brain: A new meta-analysis of functional imaging data on reading. Journal of Neurolinguistics, 26(1), 214–238. https://doi.org/10.1016/j.jneuroling.2012.08.001Google Scholar
  14. Chan, S.-t., Tang, S.-w., Tang, K.-w., Lee, W.-k., Lo, S.-s., & Kwong, K. K. (2009). Hierarchical coding of characters in the ventral and dorsal visual streams of Chinese language processing. NeuroImage, 48(2), 423–435. https://doi.org/10.1016/j.neuroimage.2009.06.078Google Scholar
  15. Chang, L.-Y., Chen, Y.-C., & Perfetti, C. A. (2017). GraphCom: A multidimensional measure of graphic complexity applied to 131 written languages. Behavior Research Methods. https://doi.org/10.3758/s13428-017-0881-yGoogle Scholar
  16. Chang, L.-Y., Plaut, D. C., & Perfetti, C. A. (2016). Visual complexity in orthographic learning: Modeling learning across writing system variations. Scientific Studies of Reading, 20(1), 64–85. https://doi.org/10.1080/10888438.2015.1104688Google Scholar
  17. Chen, M. J., & Weekes, B. S. (2004). Effects of semantic radicals on Chinese character categorization and character decision. Chinese Journal of Psychology, 46, 179–195.  https://doi.org/10.6129/CJPGoogle Scholar
  18. Christiansen, M. H., Conway, C. M., & Onnis, L. (2012). Similar neural correlates for language and sequential learning: Evidence from event-related brain potentials. Language and Cognitive Processes, 27(2), 231–256. https://doi.org/10.1080/01690965.2011.606666Google Scholar
  19. Coltheart, M. (1978). Lexical access in simple reading tasks. In G. Underwood (Ed.), Strategies of information processing (pp. 151–216). London: Academic Press.Google Scholar
  20. Coltheart, M. (1983). Child development: Phonological awareness: A preschool precursor of success in reading. Nature, 301(5899), 370. https://doi.org/10.1038/301370a0Google Scholar
  21. Conway, C. M., & Christiansen, M. H. (2009). Seeing and hearing in space and time: Effects of modality and presentation rate on implicit statistical learning. European Journal of Cognitive Psychology, 21(4), 561–580. https://doi.org/10.1080/09541440802097951Google Scholar
  22. Cunningham, A. E., & Stanovich, K. E. (1993). Children’s literacy environments and early word recognition subskills. Reading and Writing, 5(2), 193–204. https://doi.org/10.1007/BF01027484Google Scholar
  23. Davis, M. H., & Gaskell, M. G. (2009). A complementary systems account of word learning: Neural and behavioural evidence. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364(1536), 3773–3800.  https://doi.org/10.1098/rstb.2009.0111Google Scholar
  24. Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56(2), 384–398. https://doi.org/10.1016/j.neuron.2007.10.004Google Scholar
  25. Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: A proposal. Trends in Cognitive Sciences, 9(7), 335–341. https://doi.org/10.1016/j.tics.2005.05.004Google Scholar
  26. Dehaene, S., Pegado, F., Braga, L. W., Ventura, P., Nunes Filho, G., Jobert, A., … Cohen, L. (2010). How learning to read changes the cortical networks for vision and language. Science, 330(6009), 1359–1364.  https://doi.org/10.1126/science.1194140Google Scholar
  27. Doyon, J. (2008). Motor sequence learning and movement disorders. Current Opinion in Neurology, 21(4), 478–483.  https://doi.org/10.1097/WCO.0b013e328304b6a3Google Scholar
  28. Ellis, N. C., & Schmidt, R. (1998). Rules or associations in the acquisition of morphology? The frequency by regularity interaction in human and PDP learning of morphosyntax. Language and Cognitive Processes, 13(2-3), 307–336. https://doi.org/10.1080/016909698386546Google Scholar
  29. Feldman, L. B., & Siok, W. W. T. (1997). The role of component function in visual recognition of Chinese characters. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(3), 776–781. https://doi.org/10.1037/0278-7393.23.3.776Google Scholar
  30. Fiebach, C. J., Ricker, B., Friederici, A. D., & Jacobs, A. M. (2007). Inhibition and facilitation in visual word recognition: Prefrontal contribution to the orthographic neighborhood size effect. NeuroImage, 36(3), 901–911. https://doi.org/10.1016/j.neuroimage.2007.04.004Google Scholar
  31. Fiez, J. A., Balota, D. A., Raichle, M. E., & Petersen, S. E. (1999). Effects of lexicality, frequency, and spelling-to-sound consistency on the functional anatomy of reading. Neuron, 24(1), 205–218. https://doi.org/10.1016/S0896-6273(00)80833-8Google Scholar
  32. Fiser, J., & Aslin, R. N. (2002). Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences of the United States of America, 99(24), 15822–15826.  https://doi.org/10.1073/pnas.232472899Google Scholar
  33. Fiser, J., & Aslin, R. N. (2005). Encoding multielement scenes: Statistical learning of visual feature hierarchies. Journal of Experimental Psychology, 134(4), 521–537. https://doi.org/10.1037/0096-3445.134.4.521Google Scholar
  34. Foorman, B. R., Francis, D. J., Fletcher, J. M., Schatschneider, C., & Mehta, P. (1998). The role of instruction in learning to read: Preventing reading failure in at-risk children. Journal of Educational Psychology, 90(1), 37–55. https://doi.org/10.1037/0022-0663.90.1.37Google Scholar
  35. Frost, R. (2012). A universal approach to modeling visual word recognition and reading: Not only possible, but also inevitable. Behavioral and Brain Sciences, 35(5), 310–329.Google Scholar
  36. 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.2014.12.010Google Scholar
  37. Frost, R., Siegelman, N., Narkiss, A., & Afek, L. (2013). What predicts successful literacy acquisition in a second language? Psychological Science, 24(7), 1243–1252. https://doi.org/10.1177/0956797612472207Google Scholar
  38. Frost, S. J., Landi, N., Mencl, W. E., Sandak, R., Fulbright, R. K., Tejada, E. T., … Pugh, K. R. (2009). Phonological awareness predicts activation patterns for print and speech. Annals of Dyslexia, 59(1), 78–97. https://doi.org/10.1007/s11881-009-0024-yGoogle Scholar
  39. Frost, S. J., Mencl, W. E., Sandak, R., Moore, D. L., Rueckl, J. G., Katz, L., … Pugh, K. R. (2005). A functional magnetic resonance imaging study of the tradeoff between semantics and phonology in reading aloud. Neuroreport, 16(6), 621–624.Google Scholar
  40. Gabay, Y., Thiessen, E. D., & Holt, L. L. (2015). Impaired statistical learning in developmental dyslexia. Journal of Speech, Language, and Hearing Research, 58(3), 934–945. https://doi.org/10.1044/2015_JSLHR-L-14-0324Google Scholar
  41. Glushko, R. J. (1979). The organization and activation of orthographic knowledge in reading aloud. Journal of Experimental Psychology: Human Perception and Performance, 5(4), 674–691. https://doi.org/10.1037/0096-1523.5.4.674Google Scholar
  42. Goswami, U., Wang, H.-L. S., Cruz, A., Fosker, T., Mead, N., & Huss, M. (2011). Language-universal sensory deficits in developmental dyslexia: English, Spanish, and Chinese. Journal of Cognitive Neuroscience, 23(2), 325–337.  https://doi.org/10.1162/jocn.2010.21453Google Scholar
  43. Gottardo, A., Chiappe, P., Siegel, L. S., & Stanovich, K. E. (1999). Patterns of word and nonword processing in skilled and less-skilled readers. Reading and Writing, 11(5/6), 465–487. https://doi.org/10.1023/A:1008034802088Google Scholar
  44. Grainger, J., & Jacobs, A. M. (1996). Orthographic processing in visual word recognition: A multiple read-out model. Psychological Review, 103(3), 518–565. https://doi.org/10.1037/0033-295X.103.3.518Google Scholar
  45. Graves, W. W., Desai, R., Humphries, C., Seidenberg, M. S., & Binder, J. R. (2010). Neural systems for reading aloud: A multiparametric approach. Cerebral Cortex, 20(8), 1799–1815.  https://doi.org/10.1093/cercor/bhp245Google Scholar
  46. Herbster, A. N., Mintun, M. A., Nebes, R. D., & Becker, J. T. (1997). Regional cerebral blood flow during word and nonword reading. Human Brain Mapping, 5(2), 84–92. https://doi.org/10.1002/(SICI)1097-0193(1997)5:2\textless84::AID-HBM2\textgreater3.0.CO;2-IGoogle Scholar
  47. Hoeft, F., Meyler, A., Hernandez, A., Juel, C., Taylor-Hill, H., Martindale, J. L., … Gabrieli, J. D. E.. Functional and morphometric brain dissociation between dyslexia and reading ability. Proceedings of the National Academy of Sciences of the United States of America, 104(10), 4234–4239.  https://doi.org/10.1073/pnas.0609399104Google Scholar
  48. Howard, J. H., JR, Howard, D. V., Japikse, K. C., & Eden, G. F. (2006). Dyslexics are impaired on implicit higher-order sequence learning, but not on implicit spatial context learning. Neuropsychologia, 44(7), 1131–1144. https://doi.org/10.1016/j.neuropsychologia.2005.10.015Google Scholar
  49. Hsu, C.-H., Lee, C.-Y., & Marantz, A. (2011). Effects of visual complexity and sublexical information in the occipitotemporal cortex in the reading of Chinese phonograms: A single-trial analysis with meg. Brain and Language, 117(1), 1–11. https://doi.org/10.1016/j.bandl.2010.10.002Google Scholar
  50. Hu, W., Lee, H. L., Zhang, Q., Liu, T., Geng, L. B., Seghier, M. L., … Price, C. J. (2010). Developmental dyslexia in Chinese and English populations: Dissociating the effect of dyslexia from language differences. Brain, 133(Pt 6), 1694–1706.  https://doi.org/10.1093/brain/awq106Google Scholar
  51. Huang, C.-R., & Chen, K.-J. (1998). Academia Sinica balanced corpus (3 ed.). Taipei, Taiwan: Academia Sinica.Google Scholar
  52. Hung, Y.-H., Hung, D. L., Tzeng, O. J.-L., & Wu, D. H. (2014). Tracking the temporal dynamics of the processing of phonetic and semantic radicals in Chinese character recognition by meg. Journal of Neurolinguistics, 29, 42–65. https://doi.org/10.1016/j.jneuroling.2013.12.003Google Scholar
  53. Hung, Y. H., Frost, S. J., Molfese, P., Malins, J. G., Landi, N., Mencl, W. E., ... & Pugh, K. R. (2018). Common neural basis of motor sequence learning and word recognition and its relation with individual differences in reading skill. Scientific Studies of Reading, 1–12.Google Scholar
  54. Hunt, 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. https://doi.org/10.1037/0096-3445.130.4.658Google Scholar
  55. Janacsek, 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. https://doi.org/10.1111/j.1467-7687.2012.01150.xGoogle Scholar
  56. Joanisse, M. F., & Seidenberg, M. S. (2005). Imaging the past: Neural activation in frontal and temporal regions during regular and irregular past-tense processing. Cognitive, Affective, & Behavioral Neuroscience, 5(3), 282–296.  https://doi.org/10.3758/CABN.5.3.282Google Scholar
  57. Joubert, S., Beauregard, M., Walter, N., Bourgouin, P., Beaudoin, G., Leroux, J.-M., … Lecours, A. R. (2004). Neural correlates of lexical and sublexical processes in reading. Brain and Language, 89(1), 9–20. https://doi.org/10.1016/S0093-934X(03)00403-6Google Scholar
  58. Karuza, E. A., Newport, E. L., Aslin, R. N., Starling, S. J., Tivarus, M. E., & Bavelier, D. (2013). The neural correlates of statistical learning in a word segmentation task: An fMRI study. Brain and Language, 127(1), 46–54. https://doi.org/10.1016/j.bandl.2012.11.007Google Scholar
  59. Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition, 83(2), B35–42.Google Scholar
  60. Kubovy, M., & Schutz, M. (2010). Audio-visual objects. Review of Philosophy and Psychology, 1(1), 41–61. https://doi.org/10.1007/s13164-009-0004-5Google Scholar
  61. Kuhl, P. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews. Neuroscience, 5(11), 831–843.  https://doi.org/10.1038/nrn1533Google Scholar
  62. Lee, C.-Y., Tsai, J.-L., Chan, W.-H., Hsu, C.-H., Hung, D. L., & Tzeng, O. J. L. (2007). Temporal dynamics of the consistency effect in reading Chinese: An event-related potentials study. Neuroreport, 18(2), 147–151.  https://doi.org/10.1097/WNR.0b013e328010d4e4Google Scholar
  63. Lee, C.-Y., Tsai, J.-L., Kuo, W.-J., Yeh, T.-C., Wu, Y.-T., Ho, L.-T., … Hsieh, J.-C. (2004). Neuronal correlates of consistency and frequency effects on Chinese character naming: An event-related fMRI study. NeuroImage, 23(4), 1235–1245. https://doi.org/10.1016/j.neuroimage.2004.07.064Google Scholar
  64. Lee, C.-Y., Tsai, J. L., Su, E. C. I., Tzeng, O. J. L., & Hung, D. L. (2005). Consistency, regularity, and frequency effects in naming Chinese characters. Language and Linguistics, 6(1), 75–107.Google Scholar
  65. Lee, S.-H., Booth, J. R., & Chou, T.-L. (2015). Developmental changes in the neural influence of sublexical information on semantic processing. Neuropsychologia, 73, 25–34. https://doi.org/10.1016/j.neuropsychologia.2015.05.001Google Scholar
  66. Leppänen, P. H. T., Richardson, U., Pihko, E., Eklund, K. M., Guttorm, T. K., Aro, M., & Lyytinen, H. (2002). Brain responses to changes in speech sound durations differ between infants with and without familial risk for dyslexia. Developmental Neuropsychology, 22(1), 407–422. https://doi.org/10.1207/S15326942dn2201_4Google Scholar
  67. Liberman, I. Y., Shankweiler, D., Fischer, F., & Carter, B. (1974). Explicit syllable and phoneme segmentation in the young child. Journal of Experimental Child Psychology, 18(2), 201–212. https://doi.org/10.1016/0022-0965(74)90101-5Google Scholar
  68. Lum, J. A. G., Ullman, M. T., & Conti-Ramsden, G. (2013). Procedural learning is impaired in dyslexia: Evidence from a meta-analysis of serial reaction time studies. Research in Developmental Disabilities, 34(10), 3460–3476. https://doi.org/10.1016/j.ridd.2013.07.017Google Scholar
  69. Martin, A., Schurz, M., Kronbichler, M., & Richlan, F. (2015). Reading in the brain of children and adults: A meta-analysis of 40 functional magnetic resonance imaging studies. Human Brain Mapping, 36(5), 1963–1981.  https://doi.org/10.1002/hbm.22749Google Scholar
  70. Massaro, D. W., & Cohen, M. M. (1994). Visual, orthographic, phonological, and lexical influences in reading. Journal of Experimental Psychology: Human Perception and Performance, 20(6), 1107–1128. https://doi.org/10.1037/0096-1523.20.6.1107Google Scholar
  71. McBride-Chang, C., Zhou, Y., Cho, J.-R., Aram, D., Levin, I., & Tolchinsky, L. (2011). Visual spatial skill: A consequence of learning to read? Journal of Experimental Child Psychology, 109(2), 256–262. https://doi.org/10.1016/j.jecp.2010.12.003Google Scholar
  72. McNorgan, C., Randazzo-Wagner, M., & Booth, J. R. (2013). Cross-modal integration in the brain is related to phonological awareness only in typical readers, not in those with reading difficulty. Frontiers in Human Neuroscience, 7, 388.  https://doi.org/10.3389/fnhum.2013.00388Google Scholar
  73. Mechelli, A., Crinion, J. T., Long, S., Friston, K. J., Lambon Ralph, M. A., Patterson, K., … Price, C. J. (2005). Dissociating reading processes on the basis of neuronal interactions. Journal of Cognitive Neuroscience, 17(11), 1753–1765. https://doi.org/10.1162/089892905774589190Google Scholar
  74. Metsala, J. L., Stanovich, K. E., & Brown, G. D. A. (1998). Regularity effects and the phonological deficit model of reading disabilities: A meta-analytic review. Journal of Educational Psychology, 90(2), 279–293. https://doi.org/10.1037/0022-0663.90.2.279Google Scholar
  75. Mitchel, A. D., & Weiss, D. J. (2011). Learning across senses: Cross-modal effects in multisensory statistical learning. Journal of Experimental Psychology. Learning, Memory, and Cognition, 37(5), 1081–1091. https://doi.org/10.1037/a0023700Google Scholar
  76. Nakamura, K., Kuo, W.-J., Pegado, F., Cohen, L., Tzeng, O. J. L., & Dehaene, S. (2012). Universal brain systems for recognizing word shapes and handwriting gestures during reading. Proceedings of the National Academy of Sciences of the United States of America, 109(50), 20762–20767.  https://doi.org/10.1073/pnas.1217749109Google Scholar
  77. Patterson, K. E. (1981). Neuropsychological approaches to the study of reading. British Journal of Psychology, 72(2), 151–174. https://doi.org/10.1111/j.2044-8295.1981.tb02174.xGoogle Scholar
  78. Paulesu, E., Danelli, L., & Berlingeri, M. (2014). Reading the dyslexic brain: Multiple dysfunctional routes revealed by a new meta-analysis of PET and fMRI activation studies. Frontiers in Human Neuroscience, 8, 830.  https://doi.org/10.3389/fnhum.2014.00830Google Scholar
  79. Pavlidou, E. V., Kelly, M. L., & Williams, J. M. (2010). Do children with developmental dyslexia have impairments in implicit learning? Dyslexia, 16(2), 143–161.  https://doi.org/10.1002/dys.400Google Scholar
  80. Perfetti, C. A., & Hart, L. (2002). The lexical quality hypothesis. In C. Elbro, L. T. Verhoeven, & P. Reitsma (Eds.), Precursors of functional literacy (pp. 189–213). Amsterdam/Philadelphia: John Benjamins Publishing Company.Google Scholar
  81. Perfetti, C. A., Liu, Y. G., Fiez, J. A., Nelson, J., Bolger, D. J., & Tan, L.-H. (2007). Reading in two writing systems: Accommodation and assimilation of the brain’s reading network. Bilingualism: Language and Cognition, 10(2), 131. https://doi.org/10.1017/S1366728907002891Google Scholar
  82. 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.2006.03.006Google Scholar
  83. Prasada, S., & Pinker, S. (1993). Generalisation of regular and irregular morphological patterns. Language and Cognitive Processes, 8(1), 1–56. https://doi.org/10.1080/01690969308406948Google Scholar
  84. Preston, J. L., Molfese, P. J., Frost, S. J., Mencl, W. E., Fulbright, R. K., Hoeft, F., … Pugh, K. R. (2016). Print-speech convergence predicts future reading outcomes in early readers. Psychological Science, 27(1), 75–84. https://doi.org/10.1177/0956797615611921Google Scholar
  85. Price, C. J., Moore, C. J., Humphreys, G. W., & Wise, R. J. (1997). Segregating semantic from phonological processes during reading. Journal of Cognitive Neuroscience, 9(6), 727–733.  https://doi.org/10.1162/jocn.1997.9.6.727Google Scholar
  86. Pugh, K. R., Frost, S. J., Rothman, D. L., Hoeft, F., Del Tufo, S. N., Mason, G. F., … Fulbright, R. K. (2014). Glutamate and choline levels predict individual differences in reading ability in emergent readers. The Journal of Neuroscience, 34(11), 4082–4089.  https://doi.org/10.1523/JNEUROSCI.3907-13.2014Google Scholar
  87. Pugh, K. R., Frost, S. J., Sandak, R., Landi, N., Moore, D., Della Porta, G., … Mencl, W. E. (2010). Mapping the word reading circuitry in skilled and disabled readers. In P. Cornelissen, P. Hansen, M. Kringelbach, & K. Pugh (Eds.), The neural basis of reading (pp. 281–305). Oxford: Oxford University Press.Google Scholar
  88. Pugh, K. R., Frost, S. J., Sandak, R., Landi, N., Rueckl, J. G., Constable, R. T., … Mencl, W. E. (2008). Effects of stimulus difficulty and repetition on printed word identification: An fMRI comparison of nonimpaired and reading-disabled adolescent cohorts. Journal of Cognitive Neuroscience, 20(7), 1146–1160.  https://doi.org/10.1162/jocn.2008.20079Google Scholar
  89. Pugh, K. R., Landi, N., Preston, J. L., Mencl, W. E., Austin, A. C., Sibley, D., … Frost, S. J. (2013). The relationship between phonological and auditory processing and brain organization in beginning readers. Brain and Language, 125(2), 173–183. https://doi.org/10.1016/j.bandl.2012.04.004Google Scholar
  90. Pugh, K. R., Mencl, W. E., Jenner, A. R., Katz, L., Frost, S. J., Lee, J. R., … Shaywitz, B. A. (2000). Functional neuroimaging studies of reading and reading disability (developmental dyslexia). Mental Retardation and Developmental Disabilities Research Reviews, 6(3), 207–213. https://doi.org/10.1002/1098-2779(2000)6:3\textless207::AID-MRDD8\textgreater3.0.CO;2-P
  91. Rueckl, J. G., Paz-Alonso, P. M., Molfese, P. J., Kuo, W.-J., Bick, A., Frost, S. J., … Frost, R. (2015). Universal brain signature of proficient reading: Evidence from four contrasting languages. Proceedings of the National Academy of Sciences of the United States of America, 112(50), 15510–15515.  https://doi.org/10.1073/pnas.1509321112Google Scholar
  92. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 1926–1928.  https://doi.org/10.1126/science.274.5294.1926Google Scholar
  93. 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)00075-4Google Scholar
  94. Sahin, N. T., Pinker, S., & Halgren, E. (2006). Abstract grammatical processing of nouns and verbs in Broca’s area: Evidence from fMRI. Cortex, 42(4), 540–562. https://doi.org/10.1016/S0010-9452(08)70394-0Google Scholar
  95. Sandak, R., Mencl, W. E., Frost, S. J., & Pugh, K. R. (2004). The neurobiological basis of skilled and impaired reading: Recent findings and new directions. Scientific Studies of Reading, 8(3), 273–292. https://doi.org/10.1207/s1532799xssr0803_6Google Scholar
  96. Shankweiler, D., Mencl, W. E., Braze, D., Tabor, W., Pugh, K. R., & Fulbright, R. K. (2008). Reading differences and brain: Cortical integration of speech and print in sentence processing varies with reader skill. Developmental Neuropsychology, 33(6), 745–775. https://doi.org/10.1080/87565640802418688Google Scholar
  97. Siegelman, N., Bogaerts, L., Christiansen, M. H., & Frost, R. (2017). Towards a theory of individual differences in statistical learning. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 372(1711).  https://doi.org/10.1098/rstb.2016.0059Google Scholar
  98. Siok, W. T., Niu, Z., Jin, Z., Perfetti, C. A., & Tan, L. H. (2008). A structural-functional basis for dyslexia in the cortex of Chinese readers. Proceedings of the National Academy of Sciences of the United States of America, 105(14), 5561–5566.  https://doi.org/10.1073/pnas.0801750105Google Scholar
  99. Stanovich, K. E., & Siegel, L. S. (1994). Phenotypic performance profile of children with reading disabilities: A regression-based test of the phonological-core variable-difference model. Journal of Educational Psychology, 86(1), 24–53. https://doi.org/10.1037//0022-0663.86.1.24Google Scholar
  100. Stockall, L., & Marantz, A. (2006). A single route, full decomposition model of morphological complexity: MEG evidence. The Mental Lexicon, 1(1), 85–123. https://doi.org/10.1075/ml.1.1.07stoGoogle Scholar
  101. Strain, E., & Herdman, C. M. (1999). Imageability effects in word naming: An individual differences analysis. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 53, 347–359.Google Scholar
  102. Szwed, M., Qiao, E., Jobert, A., Dehaene, S., & Cohen, L. (2014). Effects of literacy in early visual and occipitotemporal areas of Chinese and French readers. Journal of Cognitive Neuroscience, 26(3), 459–475.  https://doi.org/10.1162/jocn_a_00499Google Scholar
  103. Taft, M., & Zhu, X. (1997). Submorphemic processing in reading Chinese. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(3), 761–775. https://doi.org/10.1037/0278-7393.23.3.761Google Scholar
  104. Tan, L. H., Laird, A. R., Li, K., & Fox, P. T. (2005). Neuroanatomical correlates of phonological processing of Chinese characters and alphabetic words: A meta-analysis. Human Brain Mapping, 25(1), 83–91.  https://doi.org/10.1002/hbm.20134Google Scholar
  105. Taraban, R., & McClelland, J. L. (1987). Conspiracy effects in word pronunciation. Journal of Memory and Language, 26(6), 608–631. https://doi.org/10.1016/0749-596X(87)90105-7Google Scholar
  106. Thiessen, 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. https://doi.org/10.1037/a0030801Google Scholar
  107. 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.2009.21131Google Scholar
  108. Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6(7), 767–773. https://doi.org/10.1038/nn1065Google Scholar
  109. van Zuijen, T. L., Plakas, A., Maassen, B. A. M., Maurits, N. M., & van der Leij, A. (2013). Infant ERPs separate children at risk of dyslexia who become good readers from those who become poor readers. Developmental Science, 16(4), 554–563.  https://doi.org/10.1111/desc.12049Google Scholar
  110. Vasuki, P. R. M., Sharma, M., Ibrahim, R. K., & Arciuli, J. (2017). Musicians’ online performance during auditory and visual statistical learning tasks. Frontiers in Human Neuroscience, 11, 114.  https://doi.org/10.3389/fnhum.2017.00114Google Scholar
  111. Vinckier, F., Dehaene, S., Jobert, A., Dubus, J. P., Sigman, M., & Cohen, L. (2007). Hierarchical coding of letter strings in the ventral stream: Dissecting the inner organization of the visual word-form system. Neuron, 55(1), 143–156. https://doi.org/10.1016/j.neuron.2007.05.031Google Scholar
  112. Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994). Development of reading-related phonological processing abilities: New evidence of bidirectional causality from a latent variable longitudinal study. Developmental Psychology, 30(1), 73–87. https://doi.org/10.1037//0012-1649.30.1.73Google Scholar
  113. Westbury, C., & Buchanan, L. (2002). The probability of the least likely non-length-controlled bigram affects lexical decision reaction times. Brain and Language, 81(1–3), 66–78.  https://doi.org/10.1006/brln.2001.2507Google Scholar
  114. Zhao, J., Bi, H. Y., & Wang, Y. M. (2011). Development of phonetic radical neighborhood effect and consistency effect in Chinese character naming. Chinese Journal Ergonomics, 17(1), 1–4.Google Scholar
  115. Zhao, J., Li, Q.-L., & Bi, H.-Y. (2012). The characteristics of Chinese orthographic neighborhood size effect for developing readers. PloS One, 7(10), e46922.  https://doi.org/10.1371/journal.pone.0046922Google Scholar
  116. Zhao, J., Wang, X., Frost, S. J., Sun, W., Fang, S.-Y., Mencl, W. E., … Rueckl, J. G. (2014). Neural division of labor in reading is constrained by culture: A training study of reading Chinese characters. Cortex, 53, 90–106. https://doi.org/10.1016/j.cortex.2014.01.003Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yi-Hui Hung
    • 1
    • 2
  • Stephen J. Frost
    • 3
  • Kenneth R. Pugh
    • 4
    • 5
    • 6
  1. 1.Haskins LaboratoriesNew HavenUSA
  2. 2.Yale UniversityNew HavenUSA
  3. 3.Haskins LaboratoriesNew HavenUSA
  4. 4.Haskins LaboratoriesNew HavenUSA
  5. 5.Yale UniversityNew HavenUSA
  6. 6.University of ConnecticutStorrsUSA

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