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Annals of Dyslexia

, Volume 67, Issue 2, pp 147–162 | Cite as

Statistical learning and dyslexia: a systematic review

  • Xenia SchmalzEmail author
  • Gianmarco Altoè
  • Claudio Mulatti
Article

Abstract

The existing literature on developmental dyslexia (hereafter: dyslexia) often focuses on isolating cognitive skills which differ across dyslexic and control participants. Among potential correlates, previous research has studied group differences between dyslexic and control participants in performance on statistical learning tasks. A statistical learning deficit has been proposed to be a potential cause and/or a marker effect for early detection of dyslexia. It is therefore of practical importance to evaluate the evidence for a group difference. From a theoretical perspective, such a group difference would provide information about the causal chain from statistical learning to reading acquisition. We provide a systematic review of the literature on such a group difference. We conclude that there is insufficient high-quality data to draw conclusions about the presence or absence of an effect.

Keywords

Deficit Implicit learning Publication bias Theories of dyslexia 

Notes

Acknowledgments

We would like to thank Nic Badcock and Robert Ross for discussions about meta-analyses, and Robert Ross, Anne Castles, and Max Coltheart for helpful comments on earlier versions of this manuscript. We are further grateful to all authors who replied to our queries. This project was supported by a post-doctoral grant to XS by the Fondazione Marica De Vicenzi and Università degli Studi di Padova.

References

  1. Apfelbaum, K. S., Hazeltine, E., & McMurray, B. (2013). Statistical learning in reading: variability in irrelevant letters helps children learn phonics skills. Developmental Psychology, 49(7), 1348.CrossRefGoogle 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.CrossRefGoogle Scholar
  3. Bennett, I. J., Romano, J. C., Howard Jr., J. H., & Howard, D. V. (2008). Two forms of implicit learning in young adults with dyslexia. Annals of the New York Academy of Sciences, 1145(1), 184–198.CrossRefGoogle Scholar
  4. Borenstein, M., Hedges, L. V., Higgins, J., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester, UK: John Wiley & Sons.CrossRefGoogle Scholar
  5. Borenstein, M., Hedges, L. V., Higgins, J., & Rothstein, H. R. (2010). A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods, 1(2), 97–111.CrossRefGoogle Scholar
  6. Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafo, M. R. (2013). Confidence and precision increase with high statistical power. Nature Reviews Neuroscience, 14(8). doi: 10.1038/nrn3475-c4.
  7. Cassar, M., & Treiman, R. (1997). The beginnings of orthographic knowledge: Children’s knowledge of double letters in words. Journal of Educational Psychology, 89(4), 631.CrossRefGoogle Scholar
  8. Castles, A., & Coltheart, M. (1993). Varieties of developmental dyslexia. Cognition, 47, 149–180.CrossRefGoogle Scholar
  9. Christley, R. (2010). Power and error: increased risk of false positive results in underpowered studies. Open Epidemiology Journal, 3, 16–19.CrossRefGoogle Scholar
  10. Coltheart, M. (2015). What kinds of things cause children’s reading difficulties? Australian Journal of Learning Difficulties, 1–10.Google Scholar
  11. Cramer, A. O., van Ravenzwaaij, D., Matzke, D., Steingroever, H., Wetzels, R., Grasman, R. P.,. .. Wagenmakers, E.-J. (2015). Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies. Psychonomic Bulletin & Review, 1–8.Google Scholar
  12. Deacon, S. H., Benere, J., & Castles, A. (2012). Chicken or egg? Untangling the relationship between orthographic processing skill and reading accuracy. Cognition, 122(1), 110–117.CrossRefGoogle Scholar
  13. Deroost, N., Zeischka, P., Coomans, D., Bouazza, S., Depessemier, P., & Soetens, E. (2010). Intact first-and second-order implicit sequence learning in secondary-school-aged children with developmental dyslexia. Journal of Clinical and Experimental Neuropsychology, 32(6), 561–572.CrossRefGoogle Scholar
  14. Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in Psychology, 5.Google Scholar
  15. Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.CrossRefGoogle Scholar
  16. Fanelli, D. (2009). How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PloS One, 4(5), e5738.CrossRefGoogle Scholar
  17. Frost, R., Siegelman, N., Narkiss, A., & Afek, L. (2013). What predicts successful literacy acquisition in a second language? Psychological Science, 24(7), 1243–1252.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. Gelman, A., & Carlin, J. (2014). Beyond power calculations assessing type S (sign) and type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641–651.CrossRefGoogle Scholar
  20. Gelman, A., & Loken, E. (2014). The statistical crisis in science: data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don't hold up. American Scientist, 102, 460–465. Retrieved from http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf.
  21. Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60(4), 328–331.CrossRefGoogle Scholar
  22. Gelman, A., & Weakliem, D. (2009). Of beauty, sex and power: too little attention has been paid to the statistical challenges in estimating small effects. American Scientist, 97(4), 310–316.CrossRefGoogle Scholar
  23. Gunn, L. J., Chapeau-Blondeau, F., McDonnell, M., Davis, B., Allison, A., & Abbott, D. (2016). Too good to be true: when overwhelming evidence fails to convince. arXiv preprint arXiv:1601.00900.Google Scholar
  24. Hedenius, M., Persson, J., Alm, P. A., Ullman, M. T., Howard, J. H., Howard, D. V., & Jennische, M. (2013). Impaired implicit sequence learning in children with developmental dyslexia. Research in Developmental Disabilities, 34(11), 3924–3935.CrossRefGoogle Scholar
  25. Howard, J. H., 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.CrossRefGoogle Scholar
  26. Ioannidis, J. P. A. (2005). Why most published research findings are false. Plos Medicine, 2(8), 696–701. doi: 10.1371/journal.pmed.0020124.
  27. Ise, E., & Schulte-Körne, G. (2012). Implizites Lernen und LRS: Spielen Defizite im impliziten Lernen eine Rolle bei der Entstehung von Schwierigkeiten im Lesen und Rechtschreiben? Lernen und Lernstörungen, 1(2), 79–97. doi: 10.1024/2235-0977/a000011.
  28. Ise, E., Arnoldi, C. J., Bartling, J., & Schulte-Körne, G. (2012). Implicit learning in children with spelling disability: evidence from artificial grammar learning. Journal of Neural Transmission, 119(9), 999–1010.CrossRefGoogle Scholar
  29. Jiménez-Fernández, G., Vaquero, J. M., Jiménez, L., & Defior, S. (2011). Dyslexic children show deficits in implicit sequence learning, but not in explicit sequence learning or contextual cueing. Annals of Dyslexia, 61(1), 85–110.CrossRefGoogle Scholar
  30. John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 0956797611430953.Google Scholar
  31. Kahneman, D. (2011). Thinking, fast and slow: Macmillan.Google Scholar
  32. Kahta, S., & Schiff, R. (2016). Implicit learning deficits among adults with developmental dyslexia. Annals of Dyslexia, 1–16.Google Scholar
  33. Kelly, S. W., Griffiths, S., & Frith, U. (2002). Evidence for implicit sequence learning in dyslexia. Dyslexia, 8(1), 43–52.CrossRefGoogle Scholar
  34. Kempe, V., & MacWhinney, B. (1998). The acquisition of case marking by adult learners of Russian and German. Studies in Second Language Acquisition, 20(04), 543–587.CrossRefGoogle Scholar
  35. 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–B42.CrossRefGoogle Scholar
  36. Laasonen, M., Väre, J., Oksanen-Hennah, H., Leppämäki, S., Tani, P., Harno, H., & Cleeremans, A. (2014). Project DyAdd: implicit learning in adult dyslexia and ADHD. Annals of Dyslexia, 64(1), 1–33.CrossRefGoogle Scholar
  37. Meehl, P. E. (1990). Why summaries of research on psychological theories are often Uninterpretable. Psychological Reports, 66(1), 195–244. doi: 10.2466/Pr0.66.1.195-244.
  38. Menghini, D., Hagberg, G. E., Caltagirone, C., Petrosini, L., & Vicari, S. (2006). Implicit learning deficits in dyslexic adults: an fMRI study. NeuroImage, 33(4), 1218–1226.CrossRefGoogle Scholar
  39. Menghini, D., Finzi, A., Benassi, M., Bolzani, R., Facoetti, A., Giovagnoli, S., et al. (2010). Different underlying neurocognitive deficits in developmental dyslexia: a comparative study. Neuropsychologia, 48(4), 863–872.CrossRefGoogle Scholar
  40. Nicolson, R., & Fawcett, A. (1990). Automaticity: a new framework for dyslexia research? Cognition, 35(2), 159–182.CrossRefGoogle Scholar
  41. Nigro, L., Jiménez-Fernández, G., Simpson, I. C., & Defior, S. (2015). Implicit learning of non-linguistic and linguistic regularities in children with dyslexia. Annals of Dyslexia, 1–17.Google Scholar
  42. Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: evidence from performance measures. Cognitive Psychology, 19(1), 1–32.CrossRefGoogle Scholar
  43. Pacton, S., Perruchet, P., Fayol, M., & Cleeremans, A. (2001). Implicit learning out of the lab: the case of orthographic regularities. Journal of Experimental Psychology: General, 130(3), 401.CrossRefGoogle Scholar
  44. Pacton, S., Fayol, M., & Perruchet, P. (2005). Children's implicit learning of graphotactic and morphological regularities. Child Development, 76(2), 324–339.CrossRefGoogle Scholar
  45. Pavlidou, E. V., & Williams, J. M. (2010). Developmental dyslexia and implicit learning: evidence from an AGL transfer study. Procedia-Social and Behavioral Sciences, 2(2), 3289–3296.CrossRefGoogle Scholar
  46. Pavlidou, E. V., & Williams, J. M. (2014). Implicit learning and reading: insights from typical children and children with developmental dyslexia using the artificial grammar learning (AGL) paradigm. Research in Developmental Disabilities, 35(7), 1457–1472.CrossRefGoogle Scholar
  47. Pavlidou, E. V., Williams, J. M., & Kelly, L. M. (2009). Artificial grammar learning in primary school children with and without developmental dyslexia. Annals of Dyslexia, 59(1), 55–77.CrossRefGoogle Scholar
  48. Pavlidou, E. V., Kelly, L. M., & Williams, J. M. (2010). Do children with developmental dyslexia have impairments in implicit learning? Dyslexia, 16(2), 143–161.CrossRefGoogle Scholar
  49. Pennington, B. F. (2006). From single to multiple deficit models of developmental disorders. Cognition, 101(2), 385–413.CrossRefGoogle Scholar
  50. Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: one phenomenon, two approaches. Trends in Cognitive Sciences, 10(5), 233–238.CrossRefGoogle Scholar
  51. Petersson, K.-M., Folia, V., & Hagoort, P. (2012). What artificial grammar learning reveals about the neurobiology of syntax. Brain and Language, 120(2), 83–95.CrossRefGoogle Scholar
  52. Pollo, T. C., Kessler, B., & Treiman, R. (2009). Statistical patterns in children’s early writing. Journal of Experimental Child Psychology, 104(4), 410–426.CrossRefGoogle Scholar
  53. Pothos, E. M. (2005). The rules versus similarity distinction. Behavioral and Brain Sciences, 28(01), 1–14.Google Scholar
  54. Pothos, E. M., & Kirk, J. (2004). Investigating learning deficits associated with dyslexia. Dyslexia, 10(1), 61–76.CrossRefGoogle Scholar
  55. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6(6), 855–863.CrossRefGoogle Scholar
  56. Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118(3).Google Scholar
  57. Roever, C., & Friede, T. (2016). Package ‘bayesmeta’. Retrieved from https://cran.r-project.org/web/packages/bayesmeta/bayesmeta.pdf.
  58. Roodenrys, S., & Dunn, N. (2008). Unimpaired implicit learning in children with developmental dyslexia. Dyslexia, 14(1), 1–15.CrossRefGoogle Scholar
  59. Rosenthal, R. (1979). The “file drawer problem” and tolerance for null results. Psychological Bulletin, 86(3), 638–641.CrossRefGoogle Scholar
  60. Rothe, J., Schulte-Körne, G., & Ise, E. (2014). Does sensitivity to orthographic regularities influence reading and spelling acquisition? A 1-year prospective study. Reading and Writing, 27(7), 1141–1161.CrossRefGoogle Scholar
  61. Rothe, J., Cornell, S., Ise, E., & Schulte-Körne, G. (2015). A comparison of orthographic processing in children with and without reading and spelling disorder in a regular orthography. Reading and Writing, 28(9), 1307–1332.CrossRefGoogle Scholar
  62. Rouder, J. N., Speckman, P. L., Sun, D. C., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. doi: 10.3758/Pbr.16.2.225.
  63. Royall, R. M. (1986). The effect of sample-size on the meaning of significance tests. American Statistician, 40(4), 313–315. doi: 10.2307/2684616.
  64. Rüsseler, J., Gerth, I., & Münte, T. F. (2006). Implicit learning is intact in adult developmental dyslexic readers: evidence from the serial reaction time task and artificial grammar learning. Journal of Clinical and Experimental Neuropsychology, 28(5), 808–827.CrossRefGoogle Scholar
  65. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 1926–1928.CrossRefGoogle Scholar
  66. Schimmack, U. (2012). The ironic effect of significant results on the credibility of multiple-study articles. Psychological Methods, 17(4), 551.CrossRefGoogle Scholar
  67. Schmidt, F. L. (1992). What do data really mean? Research findings, meta-analysis, and cumulative knowledge in psychology. American Psychologist, 47(10), 1173.CrossRefGoogle Scholar
  68. Schmidt, F. L. (1996). Statistical significance testing and cumulative knowledge in psychology: implications for training of researchers. Psychological Methods, 1(2), 115–129. doi: 10.1037//1082-989x.1.2.115.
  69. Seidenberg, M., & Gonnerman, L. M. (2000). Explaining derivational morphology as the convergence of codes. Trends in Cognitive Sciences, 4(9), 353–361.CrossRefGoogle Scholar
  70. Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: theoretical perspectives and empirical evidence. Journal of Memory and Language, 81, 105–120.CrossRefGoogle Scholar
  71. Siegelman, N., Bogaerts, L., & Frost, R. (2016). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. Behavior Research Methods, 1–15.Google Scholar
  72. Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366. doi: 10.1177/0956797611417632.
  73. Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014a). P-curve and effect size correcting for publication bias using only significant results. Perspectives on Psychological Science, 9(6), 666–681.CrossRefGoogle Scholar
  74. Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014b). P-curve: a key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534–547. doi: 10.1037/a0033242.
  75. Staels, E. (2016). Cognitive causes of dyslexia: New hypotheses investigated using improved methodologies. Vrije Universiteit Brussel.Google Scholar
  76. Sterling, T. D., Rosenbaum, W., & Weinkam, J. (1995). Publication decisions revisited: the effect of the outcome of statistical tests on the decision to publish and vice versa. The American Statistician, 49(1), 108–112.Google Scholar
  77. Stoodley, C. J., Harrison, E. P., & Stein, J. F. (2006). Implicit motor learning deficits in dyslexic adults. Neuropsychologia, 44(5), 795–798.CrossRefGoogle Scholar
  78. Stoodley, C. J., Ray, N. J., Jack, A., & Stein, J. F. (2008). Implicit learning in control, dyslexic, and garden-variety poor readers. Annals of the New York Academy of Sciences, 1145(1), 173–183.CrossRefGoogle Scholar
  79. Swanson, H. L. (1996). Meta-analysis, replication, social skills, and learning disabilities. The Journal of Special Education, 30(2), 213–221.CrossRefGoogle Scholar
  80. Treiman, R., Kessler, B., Zevin, J. D., Bick, S., & Davis, M. (2006). Influence of consonantal context on the reading of vowels: evidence from children. Journal of Experimental Child Psychology, 93(1), 1–24. doi: 10.1016/J.Jecp.2005.06.008.
  81. Van Elk, M., Matzke, D., Gronau, Q. F., Guan, M., Vandekerckhove, J., & Wagenmakers, E.-J. (2015). Meta-analyses are no substitute for registered replications: a skeptical perspective on religious priming. Frontiers in Psychology, 6.Google Scholar
  82. Vicari, S., Marotta, L., Menghini, D., Molinari, M., & Petrosini, L. (2003). Implicit learning deficit in children with developmental dyslexia. Neuropsychologia, 41(1), 108–114.CrossRefGoogle Scholar
  83. Vicari, S., Finzi, A., Menghini, D., Marotta, L., Baldi, S., & Petrosini, L. (2005). Do children with developmental dyslexia have an implicit learning deficit? Journal of Neurology, Neurosurgery & Psychiatry, 76(10), 1392–1397.CrossRefGoogle Scholar
  84. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. Retrieved from http://www.jstatsoft.org/v36/i03/.
  85. Waber, D. P., Marcus, D. J., Forbes, P. W., Bellinger, D. C., Weiler, M. D., Sorensen, L. G., & Curran, T. (2003). Motor sequence learning and reading ability: is poor reading associated with sequencing deficits? Journal of Experimental Child Psychology, 84(4), 338–354.CrossRefGoogle Scholar
  86. Ziegler, J., Castel, C., Pech-Georgel, C., George, F., Alario, F.-X., & Perry, C. (2008). Developmental dyslexia and the dual route model of reading: simulating individual differences and subtypes. Cognition, 107(1), 151–178.CrossRefGoogle Scholar

Copyright information

© The International Dyslexia Association 2016

Authors and Affiliations

  • Xenia Schmalz
    • 1
    Email author
  • Gianmarco Altoè
    • 1
  • Claudio Mulatti
    • 1
  1. 1.Dipartimento di Psicologia dello Sviluppo e della SocializzazioneUniversità degli Studi di PadovaPadovaItaly

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