Implicit Learning of the Time Interval Sequence

The article considers the studies performed in the «Sequence Learning» paradigm. A special case of this experimental approach is the method of temporal sequences memorization. The elements of such sequences are time intervals instead of stimulus or their spatial localization. The item of the conducted and described study was implicit learning of the time interval sequence. The goal of the experiment was to check the possibility of unconscious acquisition of the temporal sequences, not related to the sequences of another type of organization. To process the obtained results, mixed linear models were used. It was found that the learning of time interval sequences can occur regardless of the presence of regularity in the reaction order (motor sequence) and without rules in stimuli organization (structural sequence) or in the order of their localization (spatial sequence).

1. Agafonov A.Y., Burmistrov S.N., Kozlov D.D., Kryukova A.P. Implicitnoye vyuchivaniye kombinirovannyh posledovatel’nostey [Implicit Learning of Combined Sequences]. Integratciya obrazovaniya [Intagration of education]. 2018. Vol. 22. № 2. Pp. 340—353. DOI: 10.15507/1991-9468.091.022.201802.340-353 (In Russ.).
2. Agafonov A.Y., Deeva T.M., Shilov Y.E. Implicitnoye usvoyeniye kategorial’nyih posledovatel’nostey [Implicit acquisition of categorical sequences]. Kognitivniye issledovaniya na sovremennom etape: materialy Vserossiyskoy konferentcii s megdunarodnym uchastiyem po kognitivnoy nauke 19—22 noyabrya 2018. — Electronniye tekstoviye danniye. Arkhangel’sk: SAFU [Cognitive research at the present stage: materials of the All-Russian conference with international participation in cognitive science November 19—22, 2018]. Electronic text data. Arkhangelsk: NAFU. 2018. Pp. 9—11. URL: https://narfu.ru/upload/medialibrary/ec1/Sbornik- KISE_2018-_isbn_-.pdf#page=10 (Accessed 04.03.2019). (In Russ.).
3. Kryukova A.P. Agafonov A.Y., Burmistrov S.N., Kozlov D.D., Shilov Y.E. Effect perenosa implicitnogo znaniya na sensomotornuyu deyatelnoct’ [Effect of transfer of implicit knowledge of artificial grammar under sensorimotor activity]. Eksperimental’naya psikhologiya [Experimental psychology], 2018. Vol. 11. No. 3. Pp. 63—77. doi:10.17759/exppsy.2018110305. (In Russ.).
4. Moroshkina N.V. Vliyaniye konflikta implicitnich i explicitnich znanij subyecta na resul’tati naucheniya v zadache klassificatsii [Influence of the conflict of implicit and explicit knowledge of a subject on the results of learning process in classification task]. Eksperimental’naya psikhologiya [Experimental psychology]. 2013. Vol. 6. No. 3. Pp. 62—73. (In Russ.).
5. Moroshkina N.V., Ivanchei I.I. Implicitnoe naucheniye: issledovaniye sootnosheniya osoznavayemich i neosoznavayemich processov v kognitivnoi psychologii [Implicit learning: a study of the relation of perceived and unconscious processes in cognitive psychology]. Metodologiya i istoriya psychologii [Metodology and history of psychology]. 2012. Vol. 6. No. 4. Pp. 109—131. (In Russ.).
6. Chetverikov A. Lineiniye modeli so smeshannimi effectami v kognitivnich issledovaniyach [Linear Mixed Effects Regression in Cognitive Studies]. Rossiyskiy jurnal kognitivnoi nauki [The Russian Journal of Cognitive Science]. 2015. Vol. 2. No 1. Pp. 41 — 51. (In Russ.).
7. Burmistrov S.N., Kryukova A.P., Agafonova S.V. Explicitniye i implicitniye processyi: effectyi interferentcii pri reshenii zadach raznogo tipa [Explicit and implicit processes: effects of an interference at problem solving of different types]. Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk. Sotsial’nyye, gumanitarnyye, mediko-biologicheskiye nauki [Izvestiya of the Samara Science Centre of the Russian Academy of Sciences. Social, humanitarian, medicobiological sciences]. 2017. Vol. 19. № 2. Pp. 33— 37. (In Russ.).
8. Bates D., Maechler M., Bolker B., Walker S. Fitting Linear Mixed-Effects Models Using lme4// Journal of Statistical Software. 2015. Vol. 67(1). Pp. 1—48. doi:10.18637 / jss.v067.i01
9. Cleeremans A., Destrebecqz A., Boyer M. Implicit learning: news from the front // Trends in Cognitive Sciences. 1998. Vol. 2. № 10. Pp. 406—416. DOI: https://doi.org/10.1016/S1364-6613(98)01232-7
10. Cohen. A., Ivry R.I., Keele S.W. Attention and structure in sequence learning // Journal of Experimental psychology: Learning, Memory, and Cognition. 1990. Vol. 16. No. 1. Pp. 17—30. DOI: https://doi. org/10.1037/0278-7393.16.1.17
11. Deroost N., Soetens E. Perceptual or motor learning in SRT tasks with complex sequence structures // Psychological research. 2006. Vol. 70. No. 2. Pp. 88—102. DOI: 10.1007/s00426-004-0196-3
12. Destrebecqz A., Cleeremans A. Temporal effects in sequence learning // Advances in Consciousness Research. 2003. Vol. 48. Pp. 181—214.
13. Gheysen F., Gevers W., Schutter E.D., Wealvelde H.V., Fias W. Disentangling perceptual from motor implicit sequence learning with a serial color-matching task // Experimental Brain Research. 2009. Vol. 197. Pp. 163—174. DOI 10.1007/s00221-009-1902-6
14. Heuer H., Schmidtke V., Kleinsorge T. Implicit learning of sequences of tasks // Journal of Experimental Psychology: Learning, Memory, and Cognition. 2001. Vol. 27 (4). Pp. 967—983. DOI: 10.1037//0278- 7393.27.4.967
15. Huang H.X., Zhang J.X., Liu D.Z., Li Y.L., Wang P. Implicit Sequence Learning of Background and Goal Information Under Double Dimensions // Procedia-Social and Behavioral Sciences. 2014. Vol. 116. Pp. 2989—2993. DOI: 10.1016/j.sbspro.2014.01.694
16. Kuznetsova A., Brockhoff P., Christensen R. lmerTest Package: Tests in Linear Mixed Effects Models // Journal of Statistical Software. 2017. Vol. 82(13). Pp. 1—26. doi: 10.18637/jss.v082.i13
17. Mayr U. Spatial Attention and Implicit Sequence Learning: Evidence for Independent Learning of Spatial and Nonspatial Sequences // Journal of Experimental Psychology: Learning, Memory, and Cognition. 1996. Vol. 22, No. 2. Pp. 350—364. DOI: https://doi.org/10.1037/0278-7393.22.2.350.
18. Nissen M.J., Bullemer P. Attentional requirements of learning: Evidence from performance measure // Cognitive psychology. 1987. Vol. 19. No. 1. Pp. 1—32. DOI: 10.1016/0010-0285(87)90002-8
19. Olson I.R., Chun M.M. Temporal Contextual Cuing of Visual Attention // Journal of Experimental Psychology: Learning, Memory, and Cognition. 2001. Vol. 27. No. 5. Pp. 1299—1313. DOI: 10.1037//0278- 7393.27.5.1299
20. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org
21. Remillard G. Pure perceptual-based sequence learning // Journal of Experimental Psychology: Learning, Memory, and Cognition. 2003. Vol 29. No. 4. Pp. 581—597. DOI: https://doi.org/10.1037/0278-7393.29.4.581
22. RStudio Team (2016). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http:// www.rstudio.com.
23. Seger C. A. Implicit learning // Psychological Bulletin. 1994. Vol. 115. № 2. Pp. 163—196. DOI: https:// doi.org/10.1037/0033-2909.115.2.163
24. Shin J., Ivry R. Concurrent learning of temporal and spatial sequences // Journal of Experimental Psychology: Learning, Memory, and Cognition. 2003. Vol. 28. No 3. Pp. 445—457. DOI: 10.1037//0278- 7393.28.3.445
25. Stadler M.A. On learning complex procedural knowledge // Journal of Experimental Psychology: Learning, Memory, and Cognition. 1989. Vol. 15. No. 6. Pp. 1061—1069. DOI: https://doi.org/10.1037/0278- 7393.15.6.1061
26. Willingham D.B., Nissen M.J., Bullemer P. On the development of procedural knowledge // Journal of experimental psychology: Learning, Memory, and Cognition. 1989. Vol. 15. No. 6. Pp. 1047—1060. DOI: https://doi.org/10.1037/0278-7393.15.6.1047