A learning bias for word order harmony: Evidence from speakers of non-harmonic languages

Jennifer Culbertson, Julie Franck, Guillaume Braquet, Magda Barrera Navarro, Inbal Arnon

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Word order harmony describes the tendency, found across the world's languages, to consistently order syntactic heads relative to dependents. It is one of the most well-known and well-studied typological universals. Almost since it was first noted by Greenberg (1963), there has been disagreement about what role, if any, the cognitive system plays in driving harmony. Recently, a series of studies using artificial language learning experiments reported that harmonic noun phrase word orders were preferred over non-harmonic orders by English-speaking adults and children (Culbertson et al., 2012; Culbertson & Newport, 2015, 2017). However, this evidence is potentially confounded by the fact that English is itself a harmonic language (Goldberg, 2013). Here we sought to extend the results from these studies by exploring whether learners who have substantial experience with a non-harmonic language still showed a bias for harmonic patterns during learning. We found that monolingual French- and Hebrew-speaking children, whose language has a non-harmonic noun phrase order (N Adj, Num N) nevertheless preferred harmonic patterns when learning an artificial language. We also found evidence for a harmony bias across several populations of adult learners, although this interacted in complex ways with their L2 experience. Our results suggest that transfer from the L1 cannot explain the preference for harmony found in previous studies. Moreover, they provide the strongest evidence yet that a cognitive bias for harmony is a plausible candidate for shaping linguistic typology.
Original languageEnglish
Early online date13 Jul 2020
Publication statusPublished - 1 Nov 2020

Keywords / Materials (for Non-textual outputs)

  • word order
  • harmony
  • syntax
  • learning biases
  • artificial language learning
  • second language learning


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