Do current statistical learning tasks capture stable individual differences in children? An investigation of task reliability across modality

  • Inbal ArnonEmail author


Do commonly used statistical-learning tasks capture stable individual differences in children? Infants, children, and adults are capable of using statistical learning (SL) to extract information about their environment. Although most studies have looked at group-level performance, a growing literature examines individual differences in SL and their relation to language-learning outcomes: Individuals who are better at SL are expected to show better linguistic abilities. Accordingly, studies have shown positive correlations between SL performance and language outcomes in both children and adults. However, these studies have often used tasks designed to explore group-level performance without modifying them, resulting in psychometric shortcomings that impact reliability in adults (Siegelman, Bogaerts, Christiansen, & Frost in Transactions of the Royal Society B, 372, 20160059, 2017a; Siegelman, Bogaerts, & Frost in Behavior Research Methods, 49, 418–432, 2017b). Even though similar measures are used to assess individual differences in children, no study to date has examined the reliability of these measures in development. This study examined the reliability of common SL measures in both children and adults. It assessed the reliability of three SL tasks (two auditory and one visual) twice (two months apart) in adults and children (mean age 8 years). Although the tasks showed moderate reliability in adults, they did not capture stable individual variation in children. None of the tasks were reliable across sessions, and all showed internal consistency measures well below psychometric standards. These findings raise significant concerns about the use of current SL measures to predict and explain individual differences in development. The article ends with a discussion of possible explanations for the difference in reliability between children and adults.


Statistical learning Individual differences Reliability Domain generality Children 


Author note

Thanks to Noam Siegelman for comments and help with the statistical analyses, and Louisa Bogaerts and Ram Frost for comments and helpful discussions. Additional thanks to Zohar Aizenbud and Amir Efrati for assistance in programming the experiments and coordinating the testing, as well as to the research assistants who collected the data: Yuval Braeman, Noa Bar, Shira Zicherman, Hilla Merhav, Amir Efrati, Amir Shufaniya, and Hana Gerchikov. Special thanks to Maytal Wiener, who collected the data for the second child study. I also thank the children, parents, and teachers at the Givat Mesu’aa and David primary school. The research was funded by an Israeli Science Foundation grant to the first author (Grant No. 584/16).


  1. Alario, F. X., & Ferrand, L. (1999). A set of 400 pictures standardized for French: Norms for name agreement, image agreement, familiarity, visual complexity, image variability, and age of acquisition. Behavior Research Methods, Instruments, & Computers, 31, 531–552. CrossRefGoogle Scholar
  2. Arciuli, J., & Simpson, I. C. (2011). Statistical learning in typically developing children: The role of age and speed of stimulus presentation. Developmental Science, 14, 464–473. CrossRefGoogle Scholar
  3. Arciuli, J., & Simpson, I. C. (2012). Statistical learning is related to reading ability in children and adults. Cognitive Science, 36, 286–304. CrossRefGoogle Scholar
  4. Batterink, L. J. (2017). Rapid statistical learning supporting word extraction from continuous speech. Psychological Science, 28, 921–928. CrossRefGoogle Scholar
  5. Batterink, L. J., & Paller, K. A. (2017). Online neural monitoring of statistical learning. Cortex, 90, 31–45. CrossRefGoogle Scholar
  6. Boersma, P., & van Heuven, V. (2001). Speak and unSpeak with PRAAT. Glot International, 5, 341–347.Google Scholar
  7. Bogaerts, L., Siegelman, N., Ben-Porat, T., & Frost, R. (2018). Is the Hebb repetition task a reliable measure of individual differences in sequence learning? Quarterly Journal of Experimental Psychology, 71, 892–905. CrossRefGoogle Scholar
  8. Bulf, H., Johnson, S. P., & Valenza, E. (2011). Visual statistical learning in the newborn infant. Cognition, 121, 127–132. CrossRefGoogle Scholar
  9. Conway, C. M., Bauernschmidt, A., Huang, S. S., & Pisoni, D. B. (2010). Implicit statistical learning in language processing: Word predictability is the key. Cognition, 114, 356–371. CrossRefGoogle Scholar
  10. Conway, 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, 24–39. CrossRefGoogle Scholar
  11. Cristia, A., Seidl, A., Junge, C., Soderstrom, M., & Hagoort, P. (2014). Predicting individual variation in language from infant speech perception measures. Child Development, 85, 1330–1345. CrossRefGoogle Scholar
  12. Ellis, E. M., Robledo Gonzalez, M., & Deák, G. O. (2014). Visual prediction in infancy: What is the association with later vocabulary? Language Learning and Development, 10, 36–50. CrossRefGoogle Scholar
  13. Emberson, 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, 1021–1040. CrossRefGoogle Scholar
  14. Erickson, 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. CrossRefGoogle Scholar
  15. Erickson, 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), 14.
  16. Franco, A., Eberlen, J., Destrebecqz, A., Cleeremans, A., & Bertels, J. (2015). Rapid serial auditory presentation: A new measure of statistical learning in speech segmentation. Experimental Psychology, 62, 346–351. CrossRefGoogle Scholar
  17. 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, 117–125. CrossRefGoogle Scholar
  18. Frost, R., Siegelman, N., Narkiss, A., & Afek, L. (2013). What predicts successful literacy acquisition in a second language? Psychological Science, 24, 1243–1252. CrossRefGoogle Scholar
  19. Gathercole, S. E., Willis, C. S., Baddeley, A. D., & Emslie, H. (1994). The children’s test of nonword repetition: A test of phonological working memory. Memory, 2, 103–127. CrossRefGoogle Scholar
  20. Glicksohn, A., & Cohen, A. (2013). The role of cross-modal associations in statistical learning. Psychonomic Bulletin & Review, 20, 1161–1169. CrossRefGoogle Scholar
  21. Havron, N., & Arnon, I. (2017). Minding the gaps: Literacy enhances lexical segmentation in children learning to read. Journal of Child Language, 44, 1516–1538. CrossRefGoogle Scholar
  22. Isbilen, E. S., McCauley, S. M., Kidd, E., & Christiansen, M. H. (2017). Testing statistical learning implicitly: A novel chunk-based measure of statistical learning. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 564–569). Austin TX: Cognitive Science Society.Google Scholar
  23. Karuza, E. A., Farmer, T. A., Fine, A. B., Smith, F. X., & Jaeger, T. F. (2014). On-line measures of prediction in a self-paced statistical learning task. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Meeting of the Cognitive Science Society (pp. 725–730). Austin, TX: Cognitive Science Society.Google Scholar
  24. Kaufman, A. (1994). Intelligent testing with the WISC-III. New York: WileyGoogle Scholar
  25. Kidd, E. (2012). Implicit statistical learning is directly associated with the acquisition of syntax. Developmental Psychology, 48, 171–184. CrossRefGoogle Scholar
  26. Kidd, E., & Arciuli, J. (2016). Individual differences in statistical learning predict children’s comprehension of syntax. Child Development, 87, 184–193. CrossRefGoogle Scholar
  27. Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition, 83, B35–B42.CrossRefGoogle Scholar
  28. Lany, J., Shoaib, A., Thompson, A., & Graf Estes, K. (2018). Infant statistical-learning ability is related to real-time language processing. Journal of Child Language, 45, 368–391. CrossRefGoogle Scholar
  29. Mainela-Arnold, E., & Evans, J. L. (2014). Do statistical segmentation abilities predict lexical–phonological and lexical–semantic abilities in children with and without SLI? Journal of Child Language, 41, 327–351. CrossRefGoogle Scholar
  30. Maital, S. L., Dromi, E., Sagi, A., & Bornstein, M. H. (2000). The Hebrew Communicative Development Inventory: Language specific properties and cross-linguistic generalizations. Journal of Child Language, 27, 43–67. CrossRefGoogle Scholar
  31. Misyak, J. B., & Christiansen, M. H. (2012). Statistical learning and language: An individual differences study. Language Learning, 62, 302–331. CrossRefGoogle Scholar
  32. Misyak, J. B., Christiansen, M. H., & Tomblin, J. B. (2010). Sequential expectations: The role of prediction-based learning in language. Topics in Cognitive Science, 2, 138–153. CrossRefGoogle Scholar
  33. Nemeth, D., & Janacsek, K. (2011). The dynamics of implicit skill consolidation in young and elderly adults. Journal of Gerontology, 66B, 15–22. CrossRefGoogle Scholar
  34. Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-HillGoogle Scholar
  35. Potter, C. E., Wang, T., & Saffran, J. R. (2017). Second language experience facilitates statistical learning of novel linguistic materials. Cognitive Science, 41, 913–927. CrossRefGoogle Scholar
  36. Raviv, L., & Arnon, I. (2017). The developmental trajectory of children’s auditory and visual statistical learning abilities: Modality-based differences in the effect of age. Developmental Science, 21, e12593:1–13. CrossRefGoogle Scholar
  37. Romberg, A. R., & Saffran, J. R. (2010). Statistical learning and language acquisition. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 906–914. Google Scholar
  38. Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928. CrossRefGoogle Scholar
  39. Saffran, J. R., Newport, E. L., Aslin, R. N., Tunick, R. A., & Barrueco, S. (1997). Incidental language learning: Listening (and learning) out of the corner of your ear. Psychological Science, 8, 101–105. CrossRefGoogle Scholar
  40. Shafto, C. L., Conway, C. M., Field, S. L., & Houston, D. M. (2012). Visual sequence learning in infancy: Domain-general and domain-specific associations with language. Infancy, 17, 247–271. CrossRefGoogle Scholar
  41. Shufaniya, A., & Arnon, I. (2018). Statistical learning is not age-invariant during childhood: Performance improves with age across modality. Cognitive Science, 42, 3100–3115. CrossRefGoogle Scholar
  42. 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
  43. Siegelman, N., Bogaerts, L., Christiansen, M. H., & Frost, R. (2017a). Towards a theory of individual differences in statistical learning. Transactions of the Royal Society B, 372, 20160059. CrossRefGoogle Scholar
  44. Siegelman, N., Bogaerts, L., & Frost, R. (2017b). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. Behavior Research Methods, 49, 418–432. CrossRefGoogle Scholar
  45. Siegelman, N., Bogaerts, L., Kronenfeld, O., & Frost, R. (2018a). Redefining “learning” in statistical learning: What does an online measure reveal about the assimilation of visual regularities? Cognitive Science, 42(Suppl. 3), 692–727. CrossRefGoogle Scholar
  46. Siegelman, N., Bogaerts, L., Elazar, A., Arciuli, J., & Frost, R. (2018b). Linguistic entrenchment: Prior knowledge impacts statistical learning performance. Cognition, 177, 198–213.Google Scholar
  47. Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 6, 174–215. Google Scholar
  48. Southgate, V., Senju, A., & Csibra, G. (2007). Action anticipation through attribution of false belief by 2-year-olds. Psychological Science, 18, 587–592. CrossRefGoogle Scholar
  49. Spencer, M., Kaschak, M. P., Jones, J. L., & Lonigan, C. J. (2015). Statistical learning is related to early literacy-related skills. Reading and Writing, 28, 467–490. CrossRefGoogle Scholar
  50. Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80, 99–103. CrossRefGoogle Scholar
  51. Török, B., Janacsek, K., Nagy, D. G., Orbán, G., & Nemeth, D. (2017). Measuring and filtering reactive inhibition is essential for assessing serial decision making and learning. Journal of Experimental Psychology: General, 146, 529–542.CrossRefGoogle Scholar
  52. Tsao, F., Liu, H., & Kuhl, P. K. (2004). Speech perception in infancy predicts language development in the second year of life: A longitudinal study. Child Development, 75, 1067–1084.CrossRefGoogle Scholar
  53. Turk-Browne, N. B., Jungé, J. A., & Scholl, B. J. (2005). The automaticity of visual statistical learning. Journal of Experimental Psychology: General, 134, 552–564. CrossRefGoogle Scholar
  54. West, G., Vadillo, M. A., Shanks, D. R., & Hulme, C. (2017). The procedural learning deficit hypothesis of language learning disorders: We see some problems. Developmental Science, 21, e12552:1–13. CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of PsychologyHebrew UniversityJerusalemIsrael

Personalised recommendations