scholarly journals The role of L1 and L2 frequency in cross-linguistic structural priming: An artificial language learning study

2021 ◽  
pp. 1-12
Author(s):  
Merel Muylle ◽  
Sarah Bernolet ◽  
Robert J. Hartsuiker
Keyword(s):  
Language Learning ◽  
Structural Priming ◽  
Artificial Language ◽  
Double Object ◽  
Frequency Effects ◽  
Artificial Language Learning ◽  
Prepositional Object ◽  
Biased Language ◽  
L1 And L2

Abstract We investigated L1 and L2 frequency effects in the sharing of syntax across languages (reflected in cross-linguistic structural priming) using an artificial language (AL) paradigm. Ninety-six Dutch speakers learned an AL with either a prepositional-object (PO) dative bias (PO datives appeared three times as often as double-object [DO] datives) or a DO dative bias (DOs appeared three times as often as POs). Priming was assessed from the AL to Dutch (a strongly PO-biased language). There was weak immediate priming for DOs, but not for POs in both bias conditions. This suggests that L1, but not AL, frequency influenced immediate priming. Furthermore, the DO bias group produced 10% more DOs in Dutch than the PO bias group, showing that cumulative priming was influenced by AL frequency. We discuss the different effects of L1 and AL frequency on cross-linguistic structural priming in terms of lexicalist and implicit learning accounts.

scholarly journals The role of L1 and L2 frequency in cross-linguistic structural priming: An artificial language learning study

2020 ◽  
Author(s):  
Merel Muylle ◽  
Bernolet Sarah ◽  
Robert Hartsuiker
Keyword(s):  
Language Learning ◽  
Structural Priming ◽  
Artificial Language ◽  
Double Object ◽  
Frequency Effects ◽  
Artificial Language Learning ◽  
Prepositional Object ◽  
Biased Language ◽  
L1 And L2

We investigated L1 and L2 frequency effects in the sharing of syntax across languages (reflected in cross-linguistic structural priming) using an artificial language (AL) paradigm. Ninety-six Dutch speakers learned an AL with either a prepositional-object (PO) dative bias (PO datives appeared three times as often as double-object [DO] datives) or a DO dative bias (DOs appeared three times as often as POs). Priming was assessed from the AL to Dutch (a strongly PO-biased language). There was weak immediate priming for DOs, but not for POs in both bias conditions. This suggests that L1, but not AL frequency influenced immediate priming. Furthermore, the DO bias group produced 10% more DOs in Dutch than the PO bias group, showing that cumulative priming was influenced by AL frequency. We discuss the different effects of L1 and AL frequency on cross-linguistic structural priming in terms of lexicalist and implicit learning accounts.

scholarly journals Production and Substantive Bias in Phonological Learning

2021 ◽  
Vol 9 ◽  
Author(s):  
Youngah Do ◽  
Jonathan Havenhill
Keyword(s):  
Language Learning ◽  
Language Change ◽  
Pattern Learning ◽  
Artificial Language ◽  
Phonological Learning ◽  
Artificial Language Learning ◽  
Learning Contexts ◽  
Learning Conditions ◽  
Show Evidence

The role of inductive biases has been actively examined in work on phonological learning. While previous studies systematically supported a structural bias hypothesis, i.e., patterns with simpler phonological featural descriptions are easier to learn, the results have been mixed for a substantive bias hypothesis, i.e., phonetically motivated patterns are easier to learn. This study explores an explanation for the uncertain status of substantive bias in phonological learning. Among the aspects of phonetic substance, we focus on articulatory factors. We hypothesize that practice producing phonological patterns makes salient to learners the articulatory factors underlying articulatorily (un-)grounded patterns. An artificial language learning experiment was conducted to test the learning of postnasal (de)voicing, a pattern which is primarily grounded on articulatory components. We examine the role of production in the learning of articulatorily grounded (postnasal voicing) vs. ungrounded patterns (postnasal devoicing), by comparing the outcomes of perception-only vs. perception-with-production learning contexts, both in categorical and variable pattern learning conditions. The results show evidence for a production effect, but it was restricted to certain contexts, namely those involving a higher level of uncertainty and for languages exhibiting dominant natural patterns. We discuss the implications of our findings for phonological learning and language change.

Phonetically natural rules benefit from a learning bias: a re-examination of vowel harmony and disharmony

Phonology ◽  
2020 ◽  
Vol 37 (1) ◽  
pp. 65-90 ◽  
Author(s):  
Alexander Martin ◽  
Sharon Peperkamp
Keyword(s):  
Language Learning ◽  
Speech Production ◽  
Memory Consolidation ◽  
Rule Learning ◽  
Vowel Harmony ◽  
Artificial Language ◽  
Learning Biases ◽  
Artificial Language Learning ◽  
Better Than

Substance-based phonological theories predict that a preference for phonetically natural rules (those which reflect constraints on speech production and perception) is encoded in synchronic grammars, and translates into learning biases. Some previous work has shown evidence for such biases, but methodological concerns with these studies mean that the question warrants further investigation. We revisit this issue by focusing on the learning of palatal vowel harmony (phonetically natural) compared to disharmony (phonetically unnatural). In addition, we investigate the role of memory consolidation during sleep on rule learning. We use an artificial language learning paradigm with two test phases separated by twelve hours. We observe a robust effect of phonetic naturalness: vowel harmony is learned better than vowel disharmony. For both rules, performance remains stable after twelve hours, regardless of the presence or absence of sleep.

What can artificial languages reveal about morphosyntactic processing in bilinguals?

2019 ◽  
Vol 23 (1) ◽  
pp. 81-86 ◽  
Author(s):  
Sarah Grey
Keyword(s):  
Language Learning ◽  
Learning Experience ◽  
Artificial Language ◽  
Future Directions ◽  
Second Languages ◽  
Third Language ◽  
Artificial Languages ◽  
Artificial Language Learning ◽  
Linguistic Transfer

AbstractThis article reviews work that has employed artificial languages to investigate the learning and processing of additional language grammar in bilinguals, with a focus on morphosyntactic processing in sentence contexts. The article first discusses research that has utilized artificial languages to elucidate two central issues in research on bilingual third language learning and processing: the role of prior language-learning experience and cross-linguistic transfer from the native and second languages to the third. Then, research that has compared bilingual third language to monolingual second language grammar processing is discussed, with specific consideration of hypothesized bilingual advantages at language learning. Finally, future directions in artificial language learning research on bilingual morphosyntactic processing are considered.

scholarly journals Disentangling effects of L1 and the artificial language during artificial language learning

2020 ◽  
Author(s):  
Kevin Tang ◽  
Dinah Baer-Henney
Keyword(s):  
Second Language Acquisition ◽  
Language Learning ◽  
Lexical Processing ◽  
Native Speakers ◽  
Target Language ◽  
Artificial Language ◽  
Learning Mechanisms ◽  
Artificial Language Learning ◽  
The Impact

Artificial language learning research (ALL) has become a popular tool in investigations of language learning. Learning behaviour can be characterised with limited time and effort and bring insights into real language learning. Mechanisms are uncovered and tested, for instance, for universality with learner groups with different L1s. Designing cross-linguistic ALL studies comes along with certain problems. The role of the (native and artificial) lexicons involved in the study is underestimated.Building on insights from second language acquisition and psycholinguistic research in lexical processing, this study fills an important knowledge gap in ALL research by demonstrating the need to model the influence of both the L1 and the target artificial language on language learning. Specifically, native speakers of German (n = 232) and Mandarin (n = 219) were taught a phonological pattern using a set of non-words. In addition to replicating the impact of a consonant identity effect previously found in English (Linzen and Gallagher, 2017) for two very distinct languages, we were able to capture their influences in the learning of the artificial language by training an analogical and a discriminative learning model over the lexicons of the L1 and the artificial target language used in training. Nonwords are more likely to be accepted as grammatical if they are more similar to the trained artificial lexicon and more different from the L1. In addition, nonwords are less likely to be accepted as grammatical if the decision takes longer and if the nonword is judged at a later time. Our findings show that we are at risk of underestimating the role of the lexicons when examining language learning with ALL. Some recommendations for conducting cross-linguistic ALL experiments are made.

scholarly journals The role of case marking and word order in cross-linguistic structural priming in late L2 acquisition

2020 ◽  
Author(s):  
Merel Muylle ◽  
Bernolet Sarah ◽  
Robert Hartsuiker
Keyword(s):  
Word Order ◽  
Language Variation ◽  
Structural Priming ◽  
Artificial Language ◽  
L2 Acquisition ◽  
Learning Paradigm ◽  
Natural Languages ◽  
Case Marking

Several studies found cross-linguistic structural priming with various language combinations. Here, we investigated the role of two important domains of language variation: case marking and word order. We varied these features in an artificial language (AL) learning paradigm, using three different AL versions in a between-subjects design. Priming was assessed between Dutch (no case marking, SVO word order) and a) a baseline version with SVO word order, b) a case marking version, and c) a version with SOV word order. Similar within- language and cross-linguistic priming was found in all versions for transitive sentences, indicating that cross-linguistic structural priming was not hindered. In contrast, for ditransitive sentences we found similar within-language priming for all versions, but no cross-linguistic priming. The finding that cross-linguistic priming is possible between languages that vary in morphological marking or word order, is compatible with studies showing cross-linguistic priming between natural languages that differ on these dimensions.

scholarly journals Children’s sensitivity to phonological and semantic cues during noun class learning: evidence for a phonological bias

2018 ◽  
Author(s):  
Jennifer Culbertson ◽  
Hanna Jarvinen ◽  
Frances Haggarty ◽  
Kenny Smith
Keyword(s):  
Language Acquisition ◽  
Language Learning ◽  
Classification Systems ◽  
Developmental Changes ◽  
Artificial Language ◽  
Phonological Information ◽  
Semantic Cues ◽  
Artificial Language Learning ◽  
Cue Use ◽  
Noun Class

Previous research on the acquisition of noun classification systems (e.g., grammatical gender) has found that child learners rely disproportionately on phonological cues to determine the class of a new noun, even when competing semantic cues are more reliable in their language. Culbertson, Gagliardi, and Smith (2017) argue that this likely results from the early availability of phonological information during acquisition; learners base their initial representations on formal features of nouns, only later integrating semantic cues from noun meanings . Here, we use artificial language learning experiments to show that early availability drives cue use in children (67 year-olds). However, we also find evidence of developmental changes in sensitivity to semantics; when both cues types are simultaneously available, children are more likely to rely on phonology than adults. Our results suggest that early availability and a bias favoring phonological cues both contribute to children’s over-reliance on phonology in natural language acquisition.

Learning Paradigmatic Structure

2018 ◽  
pp. 179-200
Author(s):  
Vsevolod Kapatsinski
Keyword(s):  
Language Learning ◽  
Grammatical Gender ◽  
Second Order ◽  
Production Plan ◽  
Artificial Language ◽  
Artificial Language Learning ◽  
Noun Classes ◽  
Paradigmatic Structure ◽  
New Evidence

This chapter reviews research on the acquisition of paradigmatic structure (including research on canonical antonyms, morphological paradigms, associative inference, grammatical gender and noun classes). It discusses the second-order schema hypothesis, which views paradigmatic structure as mappings between constructions. New evidence from miniature artificial language learning of morphology is reported, which suggests that paradigmatic mappings involve paradigmatic associations between corresponding structures as well as an operation, copying an activated representation into the production plan. Producing a novel form of a known word is argued to involve selecting a prosodic template and filling it out with segmental material using form-meaning connections, syntagmatic and paradigmatic form-form connections and copying, which is itself an outcome cued by both semantics and phonology.

Learning biases in opaque interactions

Phonology ◽  
2019 ◽  
Vol 36 (4) ◽  
pp. 627-653
Author(s):  
Brandon Prickett
Keyword(s):  
Neural Network ◽  
Language Learning ◽  
Learning Process ◽  
Computational Modelling ◽  
Artificial Language ◽  
Phonological Acquisition ◽  
Learning Biases ◽  
Learning Experiment ◽  
Artificial Language Learning ◽  
Harmonic Serialism

This study uses an artificial language learning experiment and computational modelling to test Kiparsky's claims about Maximal Utilisation and Transparency biases in phonological acquisition. A Maximal Utilisation bias would prefer phonological patterns in which all rules are maximally utilised, and a Transparency bias would prefer patterns that are not opaque. Results from the experiment suggest that these biases affect the learnability of specific parts of a language, with Maximal Utilisation affecting the acquisition of individual rules, and Transparency affecting the acquisition of rule orderings. Two models were used to simulate the experiment: an expectation-driven Harmonic Serialism learner and a sequence-to-sequence neural network. The results from these simulations show that both models’ learning is affected by these biases, suggesting that the biases emerge from the learning process rather than any explicit structure built into the model.

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