Linguistic entrenchment: Prior knowledge impacts statistical learning performance

Noam Siegelman*, Louisa Bogaerts, Amit Elazar, Joanne Arciuli, Ram Frost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Statistical Learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying statistical regularities in the input. Recent findings, however, show clear differences in processing regularities across modalities and stimuli as well as low correlations between performance on visual and auditory tasks. Why does a presumably domain-general mechanism show distinct patterns of modality and stimulus specificity? Here we claim that the key to this puzzle lies in the prior knowledge brought upon by learners to the learning task. Specifically, we argue that learners’ already entrenched expectations about speech co-occurrences from their native language impacts what they learn from novel auditory verbal input. In contrast, learners are free of such entrenchment when processing sequences of visual material such as abstract shapes. We present evidence from three experiments supporting this hypothesis by showing that auditory-verbal tasks display distinct item-specific effects resulting in low correlations between test items. In contrast, non-verbal tasks – visual and auditory – show high correlations between items. Importantly, we also show that individual performance in visual and auditory SL tasks that do not implicate prior knowledge regarding co-occurrence of elements, is highly correlated. In a fourth experiment, we present further support for the entrenchment hypothesis by showing that the variance in performance between different stimuli in auditory-verbal statistical learning tasks can be traced back to their resemblance to participants’ native language. We discuss the methodological and theoretical implications of these findings, focusing on models of domain generality/specificity of SL.

Original languageAmerican English
Pages (from-to)198-213
Number of pages16
StatePublished - Aug 2018

Bibliographical note

Funding Information:
This paper was supported by the ERC Advanced grant awarded to Ram Frost (project 692502-L2STAT ), the Israel Science Foundation (Grant 217/14 awarded to Ram Frost) and by the National Institute of Child Health and Human Development (RO1 HD 067364 awarded to Ken Pugh and Ram Frost, and PO1 HD 01994 awarded to Haskins Laboratories). Louisa Bogaerts received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 743528 (IF-EF). We wish to thank Inbal Arnon for comments and helpful discussion. We also thank Arit Glicksohn and Limor Raviv for kindly sharing their data.

Publisher Copyright:
© 2018 Elsevier B.V.


  • Domain generality vs. domain specificity
  • Entrenchment
  • Prior knowledge
  • Statistical learning


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