Experimental evidence for the influence of structure and meaning on linear order in the noun phrase
Recent work has used artificial language experiments to argue that hierarchical representations drive learners’ expectations about word order in complex noun phrases like these two green cars (Culbertson & Adger 2014; Martin, Ratitamkul, et al. 2019). When trained on a novel language in which in...
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doaj-2ef3a0e3b3464fb79e87ae96b31b24462021-09-02T16:33:10ZengOpen Library of HumanitiesGlossa2397-18352020-09-015110.5334/gjgl.1085546Experimental evidence for the influence of structure and meaning on linear order in the noun phraseAlexander Martin0Annie Holtz1Klaus Abels2David Adger3Jennifer Culbertson4Centre for Language Evolution, University of Edinburgh, EdinburghCentre for Language Evolution, University of Edinburgh, EdinburghDivision of Psychology and Language Sciences, University College London, LondonSchool of Language, Linguistics and Film, Queen Mary University of London, LondonCentre for Language Evolution, University of Edinburgh, EdinburghRecent work has used artificial language experiments to argue that hierarchical representations drive learners’ expectations about word order in complex noun phrases like these two green cars (Culbertson & Adger 2014; Martin, Ratitamkul, et al. 2019). When trained on a novel language in which individual modifiers come after the Noun, English speakers overwhelmingly assume that multiple nominal modifiers should be ordered such that Adjectives come closest to the Noun, then Numerals, then Demonstratives (i.e., N-Adj-Num-Dem or some subset thereof). This order transparently reflects a constituent structure in which Adjectives combine with Nouns to the exclusion of Numerals and Demonstratives, and Numerals combine with Noun+Adjective units to the exclusion of Demonstratives. This structure has also been claimed to derive frequency asymmetries in complex noun phrase order across languages (e.g., Cinque 2005). However, we show that features of the methodology used in these experiments potentially encourage participants to use a particular metalinguistic strategy that could yield this outcome without implicating constituency structure. Here, we use a more naturalistic artificial language learning task to investigate whether the preference for hierarchy-respecting orders is still found when participants do not use this strategy. We find that the preference still holds, and, moreover, as Culbertson & Adger (2014) speculate, that its strength reflects structural distance between modifiers. It is strongest when ordering Adjectives relative to Demonstratives, and weaker when ordering Numerals relative to Adjectives or Demonstratives relative to Numerals. Our results provide the strongest evidence yet for the psychological influence of hierarchical structure on word order preferences during learning.https://www.glossa-journal.org/articles/1085learning biassyntaxtypologyartificial language learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alexander Martin Annie Holtz Klaus Abels David Adger Jennifer Culbertson |
spellingShingle |
Alexander Martin Annie Holtz Klaus Abels David Adger Jennifer Culbertson Experimental evidence for the influence of structure and meaning on linear order in the noun phrase Glossa learning bias syntax typology artificial language learning |
author_facet |
Alexander Martin Annie Holtz Klaus Abels David Adger Jennifer Culbertson |
author_sort |
Alexander Martin |
title |
Experimental evidence for the influence of structure and meaning on linear order in the noun phrase |
title_short |
Experimental evidence for the influence of structure and meaning on linear order in the noun phrase |
title_full |
Experimental evidence for the influence of structure and meaning on linear order in the noun phrase |
title_fullStr |
Experimental evidence for the influence of structure and meaning on linear order in the noun phrase |
title_full_unstemmed |
Experimental evidence for the influence of structure and meaning on linear order in the noun phrase |
title_sort |
experimental evidence for the influence of structure and meaning on linear order in the noun phrase |
publisher |
Open Library of Humanities |
series |
Glossa |
issn |
2397-1835 |
publishDate |
2020-09-01 |
description |
Recent work has used artificial language experiments to argue that hierarchical representations drive learners’ expectations about word order in complex noun phrases like these two green cars (Culbertson & Adger 2014; Martin, Ratitamkul, et al. 2019). When trained on a novel language in which individual modifiers come after the Noun, English speakers overwhelmingly assume that multiple nominal modifiers should be ordered such that Adjectives come closest to the Noun, then Numerals, then Demonstratives (i.e., N-Adj-Num-Dem or some subset thereof). This order transparently reflects a constituent structure in which Adjectives combine with Nouns to the exclusion of Numerals and Demonstratives, and Numerals combine with Noun+Adjective units to the exclusion of Demonstratives. This structure has also been claimed to derive frequency asymmetries in complex noun phrase order across languages (e.g., Cinque 2005). However, we show that features of the methodology used in these experiments potentially encourage participants to use a particular metalinguistic strategy that could yield this outcome without implicating constituency structure. Here, we use a more naturalistic artificial language learning task to investigate whether the preference for hierarchy-respecting orders is still found when participants do not use this strategy. We find that the preference still holds, and, moreover, as Culbertson & Adger (2014) speculate, that its strength reflects structural distance between modifiers. It is strongest when ordering Adjectives relative to Demonstratives, and weaker when ordering Numerals relative to Adjectives or Demonstratives relative to Numerals. Our results provide the strongest evidence yet for the psychological influence of hierarchical structure on word order preferences during learning. |
topic |
learning bias syntax typology artificial language learning |
url |
https://www.glossa-journal.org/articles/1085 |
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