Abstract

Cross-linguistic differences in morphological complexity could have important consequences for language learning. Specifically, it is often assumed that languages with more regular, compositional, and transparent grammars are easier to learn by both children and adults. Moreover, it has been shown that such grammars are more likely to evolve in bigger communities. Together, this suggests that some languages are acquired faster than others, and that this advantage can be traced back to community size and to the degree of systematicity in the language. However, the causal relationship between systematic linguistic structure and language learnability has not been formally tested, despite its potential importance for theories on language evolution, second language learning, and the origin of linguistic diversity. In this pre-registered study, we experimentally tested the effects of community size and systematic structure on adult language learning. We compared the acquisition of different yet comparable artificial languages that were created by big or small groups in a previous communication experiment, which varied in their degree of systematic linguistic structure. We asked (a) whether more structured languages were easier to learn; and (b) whether languages created by the bigger groups were easier to learn. We found that highly systematic languages were learned faster and more accurately by adults, but that the relationship between language learnability and linguistic structure was typically non-linear: high systematicity was advantageous for learning, but learners did not benefit from partly or semi-structured languages. Community size did not affect learnability: languages that evolved in big and small groups were equally learnable, and there was no additional advantage for languages created by bigger groups beyond their degree of systematic structure. Furthermore, our results suggested that predictability is an important advantage of systematic structure: participants who learned more structured languages were better at generalizing these languages to new, unfamiliar meanings, and different participants who learned the same more structured languages were more likely to produce similar labels. That is, systematic structure may allow speakers to converge effortlessly, such that strangers can immediately understand each other.

Highlights

  • Languages differ greatly in how they map different meanings into morpho-syntactic structures (Dryer and Haspelmath, 2013; Evans and Levinson, 2009)

  • The effect of STRUCTURE SCORE on accuracy followed a U-shape: participants’ binary accuracy was poorer when trained on medium structure languages than when trained on low structured lan­ guages, but the highest when trained on high structured languages (Fig. 2a)

  • The current study tested the acquisition of different artificial lan­ guages that varied in their degree of systematic structure and in their community size origin

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Summary

Introduction

Languages differ greatly in how they map different meanings into morpho-syntactic structures (Dryer and Haspelmath, 2013; Evans and Levinson, 2009). Verbs in Georgian can take an astonishing number of different forms (estimated at around 200), and many verbs are truly irregular and follow unique rules, requiring speakers to learn the inflections of these verbs independently. Beyond such anecdotal examples, cross-linguistic studies have confirmed that languages differ in their degree of morphological complexity (Ackerman & Malouf, 2013; Bentz and Berdicevskis, 2016; Hengeveld and Leufkens, 2018; Lewis and Frank, 2016; Lupyan and Dale, 2010; McCauley & Christiansen, 2019)

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