Individual Differences in Learning Abilities Impact Structure Addition: Better Learners Create More Structured Languages

Tamar Johnson, Noam Siegelman, Inbal Arnon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non-structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of change. Here, we identify and test two contrasting predictions: The first sees learning as a pre-requisite for structure addition, and predicts a positive correlation between learning accuracy and structure addition, whereas the second maintains that it is those learners who struggle with learning and reproducing their input who add structure to it. This prediction is hard to test in standard iterated learning paradigms since each learner is exposed to a different input, and since structure and accuracy are computed using the same test items. Here, we test these contrasting predictions in two experiments using a one-generation artificial language learning paradigm designed to provide independent measures of learning accuracy and structure addition. Adults (N = 48 in each study) were exposed to a semi-regular language (with probabilistic structure) and had to learn it: Learning was assessed using seen items, whereas structure addition was calculated over unseen items. In both studies, we find a strong positive correlation between individuals' ability to learn the language and their tendency to add structure to it: Better learners also produced more structured languages. These findings suggest a strong link between learning and generalization. We discuss the implications of these findings for iterated language models and theories of language change more generally.

Original languageAmerican English
Article numbere12877
JournalCognitive Science
Issue number8
StatePublished - 1 Aug 2020

Bibliographical note

Publisher Copyright:
© 2020 Cognitive Science Society (CSS)


  • Artificial language learning
  • Individual differences
  • Language evolution
  • Language learning
  • Linguistic structure
  • Statistical learning


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