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Artificial grammar learning is facilitated by distributed practice: Evidence from a letter reordering task

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Abstract

Previous studies have shown that distributed practice—a training strategy that is known to facilitate memory—is likely to result in greater learning than massed practice. This effect has been demonstrated largely in explicit tasks. The purpose of this study was to test whether statistical learning of artificial grammar is affected by the lag between learning sessions overall, and by high and low complexity stimuli (as measure by chunk strength). Two groups (spaced-short and spaced-long) learned strings of letters created according to a set of rules and were required to produce new strings using given letter sets. For the spaced-short group, the two learning sessions, each including training and a test phase, took place sequentially with a 10-min break, whereas for the spaced-long group, learning sessions were distributed across two days (1-day lag). Overall results showed improved performance following spaced-long practice compared to spaced-short practice. The results also indicated that in the low chunk strength strings (indicating high complexity), both groups demonstrated similar improvement from first to second testing, while in the high chunk strength strings (indicating low complexity), improvement in letter reordering performance was significantly higher when the learning sessions were distributed across two days. This pattern of findings suggests that stimuli complexity affects the extent to which distributed practice enhance artificial grammar learning.

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Availability of data and material (data transparency)

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Learning Disabilities Studies, Haddad Center for Dyslexia and Learning Disabilities, School of Education, Bar-Ilan University, 52900, Ramat-Gan, Israel

    Rachel Schiff, Hadas Green & Shani Kahta

  2. English as a Foreign Language Unit, Bar Ilan University, 52900, Ramat-Gan, Israel

    Ayelet Sasson

Authors
  1. Rachel Schiff
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  2. Ayelet Sasson
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  4. Shani Kahta
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Correspondence to Rachel Schiff.

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The authors declare that they have no conflicts of interest.

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Informed consent was obtained from all individual participants included in the study.

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Section editor: Valerio Santangelo (University of Perugia); Handling editor: Anouschka Foltz (University of Graz); Reviewers: John Rogers (Education University of Hong Kong), Federica Bulgarelli (Duke University).

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Appendices

Appendix 1: An example of a finite state language (Knowlton and Squire 1996)

figure a

Appendix 2: Training and test strings used in the study (from Knowlton and Squire 1996)

Training strings

Test strings

1. VJTVJ

2. VJTVTVJ

3. VJTVXJJ

4. VJTXVTV

5. VTVJ

6. VTVJJ

7. VXJJJJJ

8. XVJ

9. XVJTVJ

10. XVJTVT

11. XVJTVTV

12. XVTV

13. XVTVJJJ

14. XVXJ

15. XXVJTVT

16. XXVXJ

17. XXXVJ

18. XXXVTVJ

19. XXXXVJ

20. XXXXVXJ

1. VJTVX

2. VJTXVX

3. VTV

4. VTVJJJ

5. XVT

6. VXJJ

7. XVXJJ

8. XXVTV

9. XXVX

10. XXVXJJ

11. XXXVT

12. XXXXVT

13. XVXJJJJ

14. XXXXVTV

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Schiff, R., Sasson, A., Green, H. et al. Artificial grammar learning is facilitated by distributed practice: Evidence from a letter reordering task. Cogn Process 23, 55–67 (2022). https://doi.org/10.1007/s10339-021-01048-z

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