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

Rachel Schiff, Ayelet Sasson, Hadas Green, Shani Kahta

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)55-67
Number of pages13
JournalCognitive Processing
Issue number1
StatePublished - Feb 2022

Bibliographical note

Publisher Copyright:
© 2021, Marta Olivetti Belardinelli and Springer-Verlag GmbH Germany, part of Springer Nature.


  • Artificial grammar learning
  • Chunks
  • Distributed practice
  • Letter reordering
  • Massed practice
  • Statistical learning


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