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Experimental Brain Research

, Volume 233, Issue 10, pp 2767–2776 | Cite as

Effects of learning duration on implicit transfer

  • Kanji Tanaka
  • Katsumi Watanabe
Research Article

Abstract

Implicit learning and transfer in sequence acquisition play important roles in daily life. Several previous studies have found that even when participants are not aware that a transfer sequence has been transformed from the learning sequence, they are able to perform the transfer sequence faster and more accurately; this suggests implicit transfer of visuomotor sequences. Here, we investigated whether implicit transfer could be modulated by the number of trials completed in a learning session. Participants learned a sequence through trial and error, known as the m × n task (Hikosaka et al. in J Neurophysiol 74:1652–1661, 1995). In the learning session, participants were required to successfully perform the same sequence 4, 12, 16, or 20 times. In the transfer session, participants then learned one of two other sequences: one where the button configuration Vertically Mirrored the learning sequence, or a randomly generated sequence. Our results show that even when participants did not notice the alternation rule (i.e., vertical mirroring), their total working time was less and their total number of errors was lower in the transfer session compared with those who performed a Random sequence, irrespective of the number of trials completed in the learning session. This result suggests that implicit transfer likely occurs even over a shorter learning duration.

Keywords

Implicit learning Sequence learning Transfer 

Notes

Acknowledgments

This work was supported by a Grant-in-Aid for Young Scientists (20727086) to KT and Grant-in-Aid for Scientific Research (20320624) from the JSPS and CREST to KW.

References

  1. Abrahamse EL, Jiménez L, Verwey WB, Clegg BA (2010) Representing serial action and perception. Psychon Bull Rev 17:603–623CrossRefPubMedGoogle Scholar
  2. Abrahamse EL, Ruitenberg MF, De Kleine E, Verwey WB (2013) Control of automated behavior: insights from the discrete sequence production task. Front Hum Neurosci 7:1–16CrossRefGoogle Scholar
  3. Ashby FG, Alfonso-Reese L, Turken A, Waldron E (1998) A neuropsychological theory of multiple-systems in category learning. Psychol Rev 105:442–481CrossRefPubMedGoogle Scholar
  4. Bapi RS, Doya K, Harner AM (2000) Evidence for effector independent and dependent representations and their differential time course of acquisition during motor sequence learning. Exp Brain Res 132:149–162CrossRefPubMedGoogle Scholar
  5. Bapi RS, Miyapuram KP, Graydon FX, Doya K (2006) fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences. Neuroimage 32:714–727CrossRefPubMedGoogle Scholar
  6. Bo J, Seidler RD (2009) Visuospatial working memory capacity predicts the organization of acquired explicit motor sequences. J Neurophysiol 101:3116–3125PubMedCentralCrossRefPubMedGoogle Scholar
  7. Dienes Z, Longuet-Higgins HC (2004) Can musical transformations be implicitly learned? Cogn Sci 28:531–558CrossRefGoogle Scholar
  8. Dienes Z, Kuhn G, Guo XY, Jones C (2012) Communicating structure, affect and movement: commentary on Bharucha, Curtis and Paroo. In: Rebuschat P, Rohrmeier M, Cross I, Hawkins J (eds) Language and music as cognitive systems. Oxford University Press, Oxford, pp 156–168Google Scholar
  9. Hikosaka O, Rand MK, Miyachi S, Miyashita K (1995) Learning of sequential movements in the monkey: process of learning and retention of memory. J Neurophysiol 74:1652–1661PubMedGoogle Scholar
  10. Hikosaka O, Sakai K, Miyauchi S, Takino R, Sasaki Y, Putz B (1996) Activation of human presupplementary motor area in learning of sequential procedures: a functional MRI study. J Neurophysiol 76:617–621PubMedGoogle Scholar
  11. Hikosaka O, Nakahara H, Rand MK, Sakai K, Lu X, Nakamura K, Miyachi S et al (1999) Parallel neural networks for learning sequential procedures. Trends Neurosci 22:464–471CrossRefPubMedGoogle Scholar
  12. Hikosaka O, Nakamura K, Sakai K, Nakahara H (2002) Central mechanisms of motor skill learning. Curr Opin Neurobiol 12:217–222CrossRefPubMedGoogle Scholar
  13. Kuhn G, Dienes Z (2005) Implicit learning of non-local musical rules: implicitly learning more than chunks. J Exp Psychol Learn 31:1417–1432CrossRefGoogle Scholar
  14. Li F, Jiang S, Guo X, Yang Z, Dienes Z (2013) The nature of the memory buffer in implicit learning: learning Chinese tonal symmetries. Conscious Cognit 22:920–930CrossRefGoogle Scholar
  15. Nissen MJ, Bullemer P (1987) Attentional requirements of learning: evidence from performance measures. Cogn Psychol 19:1–32CrossRefGoogle Scholar
  16. Perruchet P, Pacton S (2006) Implicit learning and statistical learning: one phenomenon, two approaches. Trends Cogn Sci 10:233–238CrossRefPubMedGoogle Scholar
  17. Pothos EM (2007) Theories of artificial grammar learning. Psychol Bull 133:227–244CrossRefPubMedGoogle Scholar
  18. Rand MK, Hikosaka O, Miyachi S, Lu X, Nakamura K, Kitaguchi K, Shimo Y (2000) Characteristics of sequential movements during early learning period in monkeys. Exp Brain Res 131:293–304CrossRefPubMedGoogle Scholar
  19. Reber AS (1967) Implicit learning of artificial grammars. JVLVB 6:317–327CrossRefGoogle Scholar
  20. Sakai K, Hikosaka O, Miyachi S, Takino R, Sasaki Y, Putz B (1998) Transition of brain activation from frontal to parietal areas in visuomotor sequence learning. J Neurosci 18:1827–1840PubMedGoogle Scholar
  21. Sakai K, Kitaguchi K, Hikosaka O (2003) Chunking during human visuomotor learning. Exp Brain Res 152:229–242CrossRefPubMedGoogle Scholar
  22. Sakai K, Hikosaka O, Nakamura K (2004) Emergence of rhythm during motor learning. Trends Cogn Sci 8:547–553CrossRefPubMedGoogle Scholar
  23. Tanaka K, Watanabe K (2013) Effects of learning with explicit elaboration on implicit transfer of visuomotor sequence learning. Exp Brain Res 228:411–425CrossRefPubMedGoogle Scholar
  24. Tanaka K, Watanabe K (2014a) Implicit transfer of reversed temporal structure in visuomotor sequence learning. Acta Psychol 153:1–12CrossRefGoogle Scholar
  25. Tanaka K, Watanabe K (2014b) Implicit transfer of reversed temporal structure in visuomotor sequence learning. Cogn Sci 38:565–579CrossRefPubMedGoogle Scholar
  26. Verwey WB (1999) Evidence for a multistage model of practice in a sequential movement task. J Exp Psychol Hum 25:1693–1708CrossRefGoogle Scholar
  27. Verwey WB (2003) Processing modes and parallel processors in producing familiar keying sequences. Psychol Res 67:106–122PubMedGoogle Scholar
  28. Verwey WB (2010) Diminished motor skill development in elderly: indications for limited motor chunk use. Acta Psychol 134:206–214CrossRefGoogle Scholar
  29. Verwey WB, Abrahamse EL (2012) Distinct modes of executing movement sequences: reacting, associating, and chunking. Acta Psychol 140:274–282CrossRefGoogle Scholar
  30. Verwey WB, Abrahamse EL, De Kleine E (2010) Cognitive processing in new and practiced discrete keying sequences. Front Cognit 1:1–13Google Scholar
  31. Watanabe K, Ikeda H, Hikosaka O (2006) Effects of explicit knowledge of workspace rotation in visuomotor sequence learning. Exp Brain Res 174:673–678CrossRefPubMedGoogle Scholar
  32. Watanabe K, Ikeda H, Miyao M (2010) Learning efficacy of explicit visuomotor sequences in children with attention-deficit/hyperactivity disorder and Asperger syndrome. Exp Brain Res 203:233–239PubMedCentralCrossRefPubMedGoogle Scholar
  33. Ziori E, Dienes Z (2006) Subjective measures of unconscious knowledge of concepts. Mind Soc 5:105–122CrossRefGoogle Scholar
  34. Ziori E, Dienes Z (2008) How does prior knowledge affect implicit and explicit concept learning? Q J Exp Psychol 61:601–624CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Research Center for Advanced Science and TechnologyThe University of TokyoTokyoJapan
  2. 2.Faculty of Science and EngineeringWaseda UniversityTokyoJapan

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