The Impact of Information Structure on the Emergence of Differential Object Marking: An Experimental Study

Shira Tal*, Kenny Smith, Jennifer Culbertson, Eitan Grossman, Inbal Arnon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Many languages exhibit differential object marking (DOM), where only certain types of grammatical objects are marked with morphological cases. Traditionally, it has been claimed that DOM arises as a way to prevent ambiguity by marking objects that might otherwise be mistaken for subjects (e.g., animate objects). While some recent experimental work supports this account, research on language typology suggests at least one alternative hypothesis. In particular, DOM may instead arise as a way of marking objects that are atypical from the point of view of information structure. According to this account, rather than being marked to avoid ambiguity, objects are marked when they are given (already familiar in the discourse) rather than new. Here, we experimentally investigate this hypothesis using two artificial language learning experiments. We find that information structure impacts participants’ object marking, but in an indirect way: atypical information structure leads to a change in word order, which then triggers increased object marking. Interestingly, this staged process of change is compatible with documented cases of DOM emergence. We argue that this process is driven by two cognitive tendencies. First, a tendency to place discourse given information before new information, and second, a tendency to mark noncanonical word order. Taken together, our findings provide corroborating evidence for the role of information structure in the emergence of DOM systems.

Original languageAmerican English
Article numbere13119
JournalCognitive Science
Issue number3
StatePublished - Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).


  • Artificial language learning
  • Differential object marking
  • Information structure
  • Language universals
  • Learning biases


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