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Dergiler / Psikoloji Çalışmaları / 2026 / Cilt: 46 Sayı: 1

İstatistiki Öğrenme Ve Türkçede Morfosentaktik Işleme Arasındaki Ilişki: Kendi Hızında Okuma Deneyi

Association between Statistical Learning and Morphosyntactic Processing in Turkish: A Self-Paced Reading Experiment

Talat Bulut(İSTANBUL MEDİPOL ÜNİVERSİTESİ)0000-0003-0904-9399
Nazmiye Evra Günhan Şenol(İSTANBUL MEDİPOL ÜNİVERSİTESİ)0000-0002-6098-0405
Sayfa
39–64
Yayın Tarihi
2026-04-20
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Sonuçlar işitsel ve görsel İÖ skorlarında benzer öğrenme düzeyleri görüldüğünü ve bunlar arasında anlamlı bir korelasyon olmadığını ortaya koymuştur.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Önceki çalışmalar istatistiki öğrenme (İÖ) becerilerinin dil becerileri ile ilişkili olduğunu göstermiştir.
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Temel Bulgular & Veri:Bu çalışmada İÖ’nün modaliteye özgülüğü, diğer genel bilişsel becerilerden bağımsız olup olmadığı, okuduğunu anlama üzerindeki etkisi ve morfosentaktik işleme ile etkileşimi araştırılmıştır.
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Sonuç & Katkı:Sonuçlar işitsel ve görsel İÖ skorlarında benzer öğrenme düzeyleri görüldüğünü ve bunlar arasında anlamlı bir korelasyon olmadığını ortaya koymuştur.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:İstatistiki öğrenme · dil · morfosentaktik işleme

Özet

Önceki çalışmalar istatistiki öğrenme (İÖ) becerilerinin dil becerileri ile ilişkili olduğunu göstermiştir. Bu çalışmada İÖ’nün modaliteye özgülüğü, diğer genel bilişsel becerilerden bağımsız olup olmadığı, okuduğunu anlama üzerindeki etkisi ve morfosentaktik işleme ile etkileşimi araştırılmıştır. Elli yetişkin katılımcıya kendi hızında okuma deneyi ve çeşitli bilişsel testler uygulanmıştır. Kendi hızında okuma deneyinde, morfosentaktik karmaşıklık açısından farklılık gösteren Türkçe cümleler (aktif kanonik, aktif çalkalanmış ve edilgen cümleler) kullanılmıştır. Ayrıca, çalışma kapsamında işitsel (sentezlenmiş heceler) ve görsel (uzaylı resimleri) uyaran üçlüleri kullanılarak, üçlüler arasında geçiş olasılığı daha yüksek olan, alışma ve test aşamalarından oluşan işitsel ve görsel İÖ testleri geliştirilmiştir. Katılımcıların kısa süreli/çalışan bellek becerileri ve sözel olmayan zeka puanları da sırasıyla ileri/geri sayı dizisi testleri ve Raven’ın Standart Progresif Matrisleri kullanılarak ölçülmüştür. Sonuçlar işitsel ve görsel İÖ skorlarında benzer öğrenme düzeyleri görüldüğünü ve bunlar arasında anlamlı bir korelasyon olmadığını ortaya koymuştur. Ayrıca, İÖ puanlarının, sözel olmayan zeka ve kısa süreli/işleyen bellek puanları ile anlamlı bir korelasyon göstermediği tespit edilmiştir. Doğrusal karışık-etki modelleri hem işitsel hem de görsel İÖ’nün morfosentaktik işleme ile ilişkili olduğunu, ancak bu ilişkinin modaliteye göre farklılaştığını göstermiştir. Bulgular, İÖ’nün modaliteye özgü olduğunu ve diğer genel bilişsel becerilerden büyük ölçüde bağımsız olduğunu göstermektedir. Bulgular ayrıca, daha yüksek İÖ becerisine sahip bireylerin, cümle işleme sırasında morfosentaktik bilgilerin olasılıksal örüntülerini, daha düşük İÖ becerisine sahip olan bireylere kıyasla daha verimli kullanabildiklerini göstermektedir. Önceki çalışmalardan farklı olarak morfolojik açıdan zengin bir dilden elde edilen bu bulgular, İÖ ile dil arasındaki ilişkiye yönelik yeni kanıtlar ortaya koymaktadır. Bulgular, İÖ ile genel olarak dil işleme, özel olarak da morfosentaktik işleme arasındaki bağlantıyı güçlendirmekte, klinik popülasyonlarda dil müdahaleleri için önemli çıkarımlar sunmaktadır.

Abstract

Previous research has shown that statistical learning (SL) abilities correlate with language abilities. This study investigated the modality specificity of SL, its independence from other general cognitive abilities, its effect on reading comprehension, and its interaction with morphosyntactic processing. Fifty adults participated in a self-paced reading experiment and several cognitive tests. The self-paced reading experiment included Turkish sentences varying in morphosyntactic complexity (active canonical, active scrambled, and passive sentences). Also, auditory and visual SL tests with familiarization and test phases were developed employing triplets of auditory (synthesized syllables) and visual (alien pictures) stimuli with higher transitional probability within than between triplets. The participants’ short-term/working memory abilities and nonverbal intelligence scores were also measured using forward/backward digit span tests and Raven’s Standard Progressive Matrices, respectively. The results revealed similar learning rates and no significant correlation between auditory and visual SL scores. In addition, no significant correlation was found between SL scores and non-verbal intelligence or short-term/working memory scores. Finally, linear mixed-effects models showed that both visual and auditory SL influenced morphosyntactic processing, although in different ways. The findings suggest that SL is modality-specific and largely independent of other general cognitive abilities. The findings also suggest that individuals with higher SL abilities may utilize probabilistic patterns of morphosyntactic information during sentence processing more efficiently than those with lower SL abilities. These results provide new evidence for the link between SL and language from a morphologically rich language. Overall, these findings reinforce the link between SL and language processing in general and morphosyntactic processing in particular and have implications for language interventions in clinical populations.

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Bu Makaleye Atıf Yap

Bulut, T., Şenol, N. E. G. (2026). İstatistiki Öğrenme Ve Türkçede Morfosentaktik Işleme Arasındaki Ilişki: Kendi Hızında Okuma Deneyi. Psikoloji Çalışmaları, Cilt: 46 Sayı: 1, 39–64. https://doi.org/10.26650/SP2023-1618172

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Kaynakça (82)

  1. Arciuli, 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. https://doi.org/10.1111/j.1467-7687.2009.00937.x google 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.x google scholar
  3. Aslin, R. N., Saffran, J. R., & Newport, E. L. (1998). Computation of conditional probability statistics by 8-month-old infants. Psychological Science, 9(4), 321–324. https://doi.org/10.1111/1467-9280.00063 google scholar
  4. Aydın, Ö. (2007). The comprehension of Turkish relative clauses in second language acquisition and agrammatism. Applied Psycholinguistics, 28(2), 295–315. https://doi.org/10.1017/S0142716407070154 google scholar
  5. Aydın, Ö., & Zagvozdkina, V. (2019). İlgi tümceciklerinin işlemlemesinde özne - nesne bakışımsızlığı. Dilbilimde Güncel Tartışmalar, 47–57. google scholar
  6. Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001 google scholar
  7. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01 google scholar
  8. Boersma, P., & Weenink, D. (2018). Praat: Doing phonetics by computer [Computer program] Version 6.0. https://www.fon.hum.uva.nl/praat/ google scholar
  9. Bogaerts, 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. https://doi.org/10.1016/j.tics.2021.10.012 google scholar
  10. Brown, V. A. (2021). An introduction to linear mixed-effects modeling in R. Advances in Methods and Practices in Psychological Science, 4(1). https://doi.org/10.1177/2515245920960351 google scholar
  11. Brysbaert, M., & Stevens, M. (2018). Power analysis and effect size in mixed effects models: A tutorial. Journal of Cognition, 1(1), 1–20. https://doi.org/10.5334/joc.10 google scholar
  12. 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.2011.06.010 google scholar
  13. Bulut, T., & Bahar, E. (2023). Afazili bireylerde dil becerileri ve istatistiki öğrenme becerisi arasındaki ilişkinin incelenmesi. Dilbilim Araştırmaları Dergisi, 34(2), 285–304. https://doi.org/10.18492/dad.1336925 google scholar
  14. Bulut, T., Yarar, E., & Wu, D. (2019). Comprehension of turkish relative clauses: Evidence from eye-tracking and corpus analysis. Dil, Konuşma ve Yutma Araştırmaları Dergisi, 2(3), 211–246. google scholar
  15. Christiansen, M. H., Louise Kelly, M., Shillcock, R. C., & Greenfield, K. (2010). Impaired artificial grammar learning in agrammatism. Cognition, 116(3), 382–393. https://doi.org/10.1016/j.cognition.2010.05.015 google scholar
  16. Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12(5), 769–786. https://doi.org/10.3758/BF03196772 google scholar
  17. 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(3), 356–371. https://doi.org/10.1016/j.cognition.2009.10.009 google scholar
  18. 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(1), 24–39. https://doi.org/10.1037/0278-7393.31.1.24 google scholar
  19. Duman, T. Y., Altınok, N., Özgirgin, N., & Bastiaanse, R. (2011). Sentence comprehension in Turkish Broca’s aphasia: An integration problem. Aphasiology, 25(8), 908–926. https://doi.org/10.1080/02687038.2010.550629 google scholar
  20. 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(5), 1021–1040. https://doi.org/10.1080/17470218.2010.538972 google scholar
  21. Emberson, L. L., Misyak, J. B., Schwade, J. A., Christiansen, M. H., & Goldstein, M. H. (2019). Comparing statistical learning across perceptual modalities in infancy: An investigation of underlying learning mechanism(s). Developmental Science, 22(6), 1–10. https://doi.org/10.1111/desc.12847 google scholar
  22. Erdocia, K., Laka, I., Mestres-Missé, A., & Rodriguez-Fornells, A. (2009). Syntactic complexity and ambiguity resolution in a free word order language: Behavioral and electrophysiological evidences from Basque. Brain and Language, 109(1), 1–17. https://doi.org/10.1016/j.bandl.2008.12.003 google scholar
  23. Evans, J. L., Saffran, J. R., & Robe-Torres, K. (2009). Statistical learning in children with specific language impairment. Journal of Speech, Language, and Hearing Research, 52(2), 321–335. https://doi.org/10.1044/1092-4388(2009/07-0189) google scholar
  24. 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 google scholar
  25. 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/10.1177/0956797612472207 google scholar
  26. Gabriel, A., Maillart, C., Guillaume, M., Stefaniak, N., & Meulemans, T. (2011). Exploration of serial structure procedural learning in children with language impairment. Journal of the International Neuropsychological Society, 17(2), 336–343. https://doi.org/10.1017/S1355617710001724 google scholar
  27. Gabriel, A., Meulemans, T., Parisse, C., & Maillart, C. (2015). Procedural learning across modalities in French-speaking children with specific language impairment. Applied Psycholinguistics, 36(3), 747–769. https://doi.org/10.1017/S0142716413000490 google scholar
  28. Göksel, A., & Kerslake, C. (2005). Turkish: A comprehensive grammar. Routledge. google scholar
  29. Green, P., & Macleod, C. J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498. https://doi.org/10.1111/2041-210X.12504 google scholar
  30. Hagiwara, H., Soshi, T., Ishihara, M., & Imanaka, K. (2007). A topographical study on the event-related potential correlates of scrambled word order in Japanese complex sentences. Journal of Cognitive Neuroscience, 19(2), 175–193. https://doi.org/10.1162/jocn.2007.19.2.175 google scholar
  31. İşsever, S. (2003). Information structure in Turkish: The word order–prosody interface. Lingua, 113(11), 1025–1053. https://doi.org/10.1016/S0024-3841(03)00012-3 google scholar
  32. Kahraman, B., & Hirose, Y. (2018). Online comprehension of SOV and OSV sentences in Turkish with a supporting context. In T. Levin & R. Masuda (Eds.), Proceedings of the 10th Workshop on Altaic Formal Linguistics (MIT Working Papers in Linguistics, Vol. 87, pp. 69–70). google scholar
  33. Karpicke, J. D., & Pisoni, D. B. (2004). Using immediate memory span to measure implicit learning. Memory & Cognition, 32(6), 956–964. https://doi.org/10.3758/BF03196873 google scholar
  34. Kaufman, S. B., DeYoung, C. G., Gray, J. R., Jiménez, L., Brown, J., & Mackintosh, N. (2010). Implicit learning as an ability. Cognition, 116(3), 321–340. https://doi.org/10.1016/j.cognition.2010.05.011 google scholar
  35. Kidd, E. (2012). Implicit statistical learning is directly associated with the acquisition of syntax. Developmental Psychology, 48(1), 171–184. https://doi.org/10.1037/a0025405 google scholar
  36. Kidd, E., & Arciuli, J. (2016). Individual differences in statistical learning predict children’s comprehension of syntax. Child Development, 87(1), 184–193. https://doi.org/10.1111/cdev.12461 google scholar
  37. Kidd, E., Arciuli, J., Christiansen, M. H., & Smithson, M. (2023). The sources and consequences of individual differences in statistical learning for language development. Cognitive Development, 66, 101335. https://doi.org/10.1016/j.cogdev.2023.101335 google scholar
  38. 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. https://doi.org/10.1016/S0010-0277(02)00004-5 google scholar
  39. Kornfilt, J. (2018). Some remarks on the interaction of case and word order in Turkish: Implications for acquisition. In B. Lust, M. Suner, J. Whitmann, & S. Flynn (Eds.), Heads, projections, and learnability (pp. 171–199). Psychology Press. google scholar
  40. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. https://doi.org/10.18637/jss.v082.i13 google scholar
  41. Lammertink, I., Boersma, P., Wijnen, F., & Rispens, J. (2020). Statistical learning in the visuomotor domain and its relation to grammatical proficiency in children with and without developmental language disorder: A conceptual replication and meta-analysis. Language Learning and Development, 16(4), 426–450. https://doi.org/10.1080/15475441.2020.1820340 google scholar
  42. Lenth, R. V. (2025). emmeans: Estimated marginal means, aka least-squares means (R package version 1.11.2-8). https://doi.org/10.32614/CRAN.package.emmeans google scholar
  43. MacWhinney, B., Osmán-Sági, J., & Slobin, D. I. (1991). Sentence comprehension in aphasia in two clear case-marking languages. Brain and Language, 41(2), 234–249. https://doi.org/10.1016/0093-934X(91)90154-S google scholar
  44. Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing Type I error and power in linear mixed models. Journal of Memory and Language, 94, 305–315. https://doi.org/10.1016/j.jml.2017.01.001 google scholar
  45. Maviş, İ., Arslan, S., & Aydin, Ö. (2020). Comprehension of word order in Turkish aphasia. Aphasiology, 34(8), 999–1015. https://doi.org/10.1080/02687038.2019.1622646 google scholar
  46. Maye, J., Weiss, D. J., & Aslin, R. N. (2008). Statistical phonetic learning in infants: facilitation and feature generalization. Developmental Science, 11(1), 122–134. https://doi.org/10.1111/j.1467-7687.2007.00653.x google scholar
  47. Maye, J., Werker, J. F., & Gerken, L. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. Cognition, 82(3), B101–B111. https://doi.org/10.1016/S0010-0277(01)00157-3 google scholar
  48. Misyak, J. B., & Christiansen, M. H. (2007). Extending Statistical Learning Farther and Further: Long-Distance Dependencies, and Individual Differences in Statistical Learning and Language. Proceedings of the Annual Meeting of the Cognitive Science Society, 29(29), 1307–1312. google scholar
  49. Misyak, J. B., & Christiansen, M. H. (2012). Statistical Learning and Language: An Individual Differences Study. Language Learning, 62(1), 302–331. https://doi.org/10.1111/j.1467-9922.2010.00626.x google scholar
  50. Misyak, J., Christiansen, M. H., & Tomblin, J. B. (2010a). On-line individual differences in statistical learning predict language processing. Frontiers in Psychology, 1(SEP), 31. https://doi.org/10.3389/fpsyg.2010.00031 google scholar
  51. Misyak, J. B., Christiansen, M. H., & Tomblin, J. B. (2010b). Sequential expectations: The role of prediction-based learning in language. Topics in Cognitive Science, 2(1), 138–153. https://doi.org/10.1111/j.1756-8765.2009.01072.x google scholar
  52. Newport, E. L., & Aslin, R. N. (2004). Learning at a distance I. Statistical learning of non-adjacent dependencies. Cognitive Psychology, 48(2), 127–162. https://doi.org/10.1016/S0010-0285(03)00128-2 google scholar
  53. Özge, D., Küntay, A., & Snedeker, J. (2019). Why wait for the verb? Turkish speaking children use case markers for incremental language comprehension. Cognition, 183(October 2018), 152–180. https://doi.org/10.1016/j.cognition.2018.10.026 google scholar
  54. Özge, D., Marinis, T., & Zeyrek, D. (2015). Incremental processing in head-final child language: online comprehension of relative clauses in Turkish-speaking children and adults. Language, Cognition and Neuroscience, 30(9), 1230–1243. https://doi.org/10.1080/23273798.2014.995108 google scholar
  55. Palmer, S. D., & Mattys, S. L. (2016). Speech segmentation by statistical learning is supported by domain-general processes within working memory. Quarterly Journal of Experimental Psychology, 69(12), 2390–2401. https://doi.org/10.1080/17470218.2015.1112825 google scholar
  56. Powell, M. J. D. (2009). The BOBYQA algorithm for bound constrained optimization without derivatives. Cambridge NA Report NA2009/06, University of Cambridge, Cambridge. https://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf google scholar
  57. Qi, Z., Sanchez Araujo, Y., Georgan, W. C., Gabrieli, J. D. E., & Arciuli, J. (2019). Hearing Matters More Than Seeing: A Cross-Modality Study of Statistical Learning and Reading Ability. Scientific Studies of Reading, 23(1), 101–115. https://doi.org/10.1080/10888438.2018.1485680 google scholar
  58. R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/ google scholar
  59. R Core Team. (2021). R: A language and environment for statistical computing. https://www.r-project.org/ google scholar
  60. Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for Raven’s progressive matrices and vocabulary scales. Oxford: Oxford Psychologists Press. google scholar
  61. Raviv, 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), 1–13. https://doi.org/10.1111/desc.12593 google scholar
  62. Richardson, J. T. E. (2007). Measures of Short-Term Memory: A Historical Review. Cortex, 43(5), 635–650. https://doi.org/10.1016/S0010-9452(08)70493-3 google scholar
  63. Rösler, F., Pechmann, T., Streb, J., Röder, B., & Hennighausen, E. (1998). Parsing of Sentences in a Language with Varying Word Order: Word-by-Word Variations of Processing Demands Are Revealed by Event-Related Brain Potentials. Journal of Memory and Language, 38(2), 150–176. https://doi.org/10.1006/jmla.1997.2551 google scholar
  64. 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.274.5294.1926 google scholar
  65. 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 google scholar
  66. Saffran, J. R., & Thiessen, E. D. (2003). Pattern induction by infant language learners. Developmental Psychology, 39(3), 484–494. https://doi.org/10.1037/0012-1649.39.3.484 google scholar
  67. Schipke, C. S., Knoll, L. J., Friederici, A. D., & Oberecker, R. (2012). Preschool children’s interpretation of object-initial sentences: Neural correlates of their behavioral performance. Developmental Science, 15(6), 762–774. https://doi.org/10.1111/j.1467-7687.2012.01167.x google scholar
  68. Schlesewsky, M., Bornkessel, I., & Frisch, S. (2003). The neurophysiological basis of word order variations in German. Brain and Language, 86(1), 116–128. https://doi.org/10.1016/S0093-934X(02)00540-0 google scholar
  69. Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. 418–432. https://doi.org/10.3758/s13428-016-0719-z google scholar
  70. Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: Theoretical perspectives and empirical evidence. Journal of Memory and Language, 81(3), 105–120. https://doi.org/10.1016/j.jml.2015.02.001 google scholar
  71. Slobin, D. I., & Bever, T. G. (1982). Children use canonical sentence schemas: A crosslinguistic study of word order and inflections. Cognition, 12(3), 229–265. https://doi.org/10.1016/0010-0277(82)90033-6 google scholar
  72. Tamaoka, K., Asano, M., Miyaoka, Y., & Yokosawa, K. (2014). Pre- and Post-head Processing for Single- and Double-Scrambled Sentences of a Head-Final Language as Measured by the Eye Tracking Method. Journal of Psycholinguistic Research, 43(2), 167–185. https://doi.org/10.1007/s10936-013-9244-8 google scholar
  73. Teinonen, T., Fellman, V., Näätänen, R., Alku, P., & Huotilainen, M. (2009). Statistical language learning in neonates revealed by event-related brain potentials. BMC Neuroscience, 10(1), 21. https://doi.org/10.1186/1471-2202-10-21 google scholar
  74. Thiessen, E. D. (2011). Domain General Constraints on Statistical Learning. Child Development, 82(2), 462–470. https://doi.org/10.1111/j.1467-8624.2010.01522.x google scholar
  75. Türk, U., Atmaca, F., Özateş, Ş. B., Berk, G., Bedir, S. T., Köksal, A., Başaran, B. Ö., Güngör, T., & Özgür, A. (2022). Resources for Turkish dependency parsing: introducing the BOUN Treebank and the BoAT annotation tool. Language Resources and Evaluation, 56(1), 259–307. https://doi.org/10.1007/s10579-021-09558-0 google scholar
  76. Vadinova, V., Buivolova, O., Dragoy, O., van Witteloostuijn, M., & Bos, L. S. (2020). Implicit-statistical learning in aphasia and its relation to lesion location. Neuropsychologia, 147(May), 107591. https://doi.org/10.1016/j.neuropsychologia.2020.107591 google scholar
  77. Wechsler, D. (1981). Manual for the Wechsler Adult Intelligence Scale - Revised. In Psychological Corporation. https://doi.org/Thesis_references-Converted #317 google scholar
  78. Wechsler, D. (1991). Wechsler intelligence scale for children. - Third edition. In San Antonio: Psychological Corporation. google scholar
  79. Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org google scholar
  80. Wu, D. H., & Bulut, T. (2020). The contribution of statistical learning to language and literacy acquisition. In Psychology of Learning and Motivation - Advances in Research and Theory (1st ed., Vol. 72, pp. 283–318). Elsevier Inc. https://doi.org/10.1016/bs.plm.2020.02.001 google scholar
  81. Yağız, O., & Aydın, B. (2014). Processing Active / Passive Sentences in Turkish Agrammatic Aphasics. Procedia - Social and Behavioral Sciences, 158, 254–259. https://doi.org/10.1016/j.sbspro.2014.12.084 google scholar
  82. Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. 418–432. https://doi.org/10.3758/s13428-016-0719-z google scholar