ERP Correlates of the Short-term Implicit Artificial Grammar Learning

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We present a study investigating the neural correlates of artificial grammar learning – a process of implicit processing of regularities in the environment. Participants observed visual stimuli that were created using a set of complex rules and then classified items from a new stimulus set as either consistent with these rules or not. Unlike previous event-related potentials (ERP) studies in this area, we used a short-term learning procedure normally used in behavioral experiments. With this short-term learning paradigm, we were able to detect ERP-components related to two different types of implicit knowledge. We found component (P600) related to the violation of the learned abstract grammatical structure. We also found early ERP-components (N200) related to the violation of learned combinations of elements in stimuli (frequency structure). It was possible to observe these distinct results because of the specific design of the study in which frequency structure and abstract grammaticality were independently varied. The results show neural correlates of classical artificial grammar learning and speak in favor of two distinct mechanisms of implicit learning: one responsible for abstract rules learning and another – for the learning of frequency structure of the environment.

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  1. 1

    Reber, A.S., Implicit learning of artificial grammars, J. Verbal Learn., Verbal Behav., 1967, vol. 6, no. 6, p. 855.

  2. 2

    Reber, A.S., Implicit Learning and Tacit Knowledge: An Essay on the Cognitive Unconscious, New York: Oxford Univ. Press, 1993.

  3. 3

    Ivanchei, I., Theories of implicit learning: contradictory approaches to the same phenomenon or consistent descriptions of different types of learning? Russ. J. Cognit. Sci., 2014, vol. 1, no. 4, p. 4.

  4. 4

    Reber, A.S., Implicit learning of synthetic languages: the role of instructional set, J. Exp. Psychol.: Learn., Mem., Cognit., 1976, vol. 2, no. 1, p. 88.

  5. 5

    Kinder, A., Shanks, D.R., Cock, J., and Tunney, R.J., Recollection, fluency, and the explicit/implicit distinction in artificial grammar learning, J. Exp. Psychol.: Gen., 2003, vol. 132, no. 4, p. 551.

  6. 6

    Knowlton, B.J. and Squire, L.R., The information acquired during artificial grammar learning, J. Exp. Psychol.: Learn., Mem., Cognit., 1994, vol. 20, no. 1, p. 79.

  7. 7

    Ivanchei, I.I. and Moroshkina, N.V., The effect of subjective awareness measures on performance in artificial grammar learning task, Conscious. Cognit., 2018, vol. 57, p. 116.

  8. 8

    Moroshkina, N.V., Ivanchei, I.I., Karpov, A.D., and Ovchinnikova, I.V., The verbalization effect on implicit learning, in Implicit Learning: 50 Years On, Cleeremans, A., Allakhverdov, V., and Kuvaldina, M., Eds., London: Routledge, 2019, p. 189.

  9. 9

    Moroshkina, N.V., Ivanchei, I.I., Karpov, A.D., and Ovchinnikova, I.V., Logical and intuitive modes of cognitive activity in implicit learning studies, in Sovremennye issledovaniya intellekta i tvorchestva (Current Research of Intelligence and Creativity), Zhuravlev, A.L., Ushakov, D.V., and Kholodnaya, M.A., Eds., Moscow, 2015, p. 78.

  10. 10

    Christiansen, M.H., Conway, C.M., and Onnis, L., Similar neural correlates for language and sequential learning: evidence from event-related brain potentials, Lang. Cognit. Process., 2012, vol. 27, no. 2, p. 231.

  11. 11

    Hagoort, P., Brown, C.M., and Groothusn, J., The syntactic positive shift as an ERP measure of syntactic processing, Lang. Cognit. Process., 1993, vol. 8, no. 4, p. 439.

  12. 12

    Kutas, M. and Hillyard, S.A., Event-related brain potentials to grammatical errors and semantic anomalies, Mem. Cognit., 1983, vol. 11, no. 5, p. 539.

  13. 13

    Friederici, A.D., Steinhauer, K., and Pfeifer, E., Brain signatures of artificial language processing: Evidence challenging the critical period hypothesis, Proc. Natl. Acad. Sci. U.S.A., 2002, vol. 99, p. 529.

  14. 14

    Lelekov, T., Dominey, P.F., and Garcia-Larrea, L., Dissociable ERP profiles for processing rules vs instances in a cognitive sequencing task, NeuroReport, 2000, vol. 11, no. 5, p. 1129.

  15. 15

    Silva, S., Folia, V., Hagoort, P., and Petersson, K.M., The P600 in implicit artificial grammar learning, Cognit. Sci., 2016, vol. 41, no. 1, p. 137.

  16. 16

    Perruchet, P. and Pacteau, C., Synthetic grammar learning: Implicit rule abstraction or explicit fragmentary knowledge? J. Exp. Psychol.: Gen., 1990, vol. 119, no. 3, p. 264.

  17. 17

    Johnstone, T. and Shanks, D.R., Two mechanisms in implicit artificial grammar learning? Comment on Meulemans and Van der Linden (1997), J. Exp. Psychol.: Learn., Mem., Cognit., 1999, vol. 25, no. 2, p. 524.

  18. 18

    Meulemans, T. and van der Linden, M., Associative chunk strength in artificial grammar learning, J. Exp. Psychol.: Learn., Mem., Cognit., 1997, vol. 23, no. 4, p. 1007.

  19. 19

    Opitz, B., Hofmann J. Concurrence of rule- and similarity-based mechanisms in artificial grammar learning, Cognit. Psychol., 2015, vol. 77, p. 77.

  20. 20

    Abrams, M. and Reber, A.S., Implicit learning: robustness in the face of psychiatric disorders, J. Psycholinguistic Res., 1988, vol. 17, no. 5, p. 425.

  21. 21

    Knowlton, B.J. and Squire, L.R., Artificial grammar learning depends on implicit acquisition of both abstract and exemplar-specific information, J. Exp. Psychol.: Learn., Mem., Cognit., 1996, vol. 22, no. 1, p. 169.

  22. 22

    Baldwin, K.B. and Kutas, M., An ERP analysis of implicit structured sequence learning, Psychophysiology, 1997, vol. 34, no. 1, p. 74.

  23. 23

    Carrión, R.E., and Bly, B.M., Event-related potential markers of expectation violation in an artificial grammar learning task, NeuroReport, 2007, vol. 18, no. 2, p. 191.

  24. 24

    Mangun, G.R., Hillyard, S.A., and Luck, S.J., Electrocortical substrates of visual selective attention, in Attention and Performance XIV: Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience Meyer, D. and Kornblum, S., Eds., Cambridge, MA: MIT Press, 1993, p. 219.

  25. 25

    Vogel, E.K. and Luck, S.J., The visual N1 component as an index of a discrimination process, Psychophysiology, 2000, vol. 37, no. 2, p. 190.

  26. 26

    Luck, S., Multiple mechanisms of visual-spatial attention: Recent evidence from human electrophysiology, Behav. Brain Res., 1995, vol. 71, no. 1–2, p. 113.

  27. 27

    Squires, K.C., Squires, N.K., and Hillyard, S.A., Decision-related cortical potentials during an auditory signal detection task with cued intervals, J. Exp. Psychol.: Hum. Percept. Perform., 1975, vol. 1, no. 3, p. 168.

  28. 28

    Key, A.P.F., Dove, G.O., and Maguire, M.J., Linking brainwaves to the brain: an ERP primer, Dev. Neuropsychol., 2005, vol. 27, no. 2, p. 183.

  29. 29

    Simson, R., Vaughan, H.G., Jr., and Ritter, W., The scalp topography of potentials in auditory and visual discrimination tasks, Electroencephalogr. Clin. Neurophysiol., 1977, vol. 42, no. 4, p. 528.

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Ivanchei, I.I., Absatova, K.A. & Kurgansky, A.V. ERP Correlates of the Short-term Implicit Artificial Grammar Learning. Hum Physiol 45, 604–613 (2019).

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  • implicit learning
  • artificial grammar learning
  • ERP
  • associative chunk strength