scholarly journals Coarticulation and learnability of transparent vowels in vowel harmony

2021 ◽  
Vol 6 (1) ◽  
pp. 92
Author(s):  
Sara Finley
Keyword(s):  
Language Learning ◽  
Natural Condition ◽  
Vowel Harmony ◽  
Artificial Language ◽  
Front Vowel ◽  
Psychological Reality ◽  
Artificial Language Learning ◽  
Back Vowel ◽  
Training Control

The representations of transparent vowels in vowel harmony have been of interest to phonologists because of the challenges they pose for constraints on locality and complexity. One proposal is that transparent vowels in back vowel harmony may be intermediate between front and back. The present study uses two artificial language learning experiments to explore the psychological reality of acoustic differences in transparent vowels in back vs. front vowel contexts. Participants were exposed to a back/round vowel harmony language with a neutral vowel that was spliced so that the F2 was lower in back vowel contexts and higher in front vowel contexts (the Natural condition) or the reverse (the Unnatural condition). While only participants in the Natural condition of Experiment 1 were able to learn the behavior of the transparent vowel relative to a No-Training control, there was no difference between the Natural and Unnatural conditions. In Experiment 2, only participants in the Natural condition learned the vowel harmony pattern, though there were no significant differences between the two conditions. No condition successfully learned the behavior of the transparent vowel in Experiment 2. These results suggest that the effects of small differences in the F2 value of transparent back vowels on learnability are minimal.

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.

scholarly journals Naturalness bias in palatalization: An experimental study

Nordlyd ◽  
2016 ◽  
Vol 42 (1) ◽  
pp. 1
Author(s):  
Joanna Zaleska ◽  
Matías Guzmán Naranjo
Keyword(s):  
Experimental Study ◽  
Language Learning ◽  
Detailed Analysis ◽  
Recent Work ◽  
Learning Difficulty ◽  
Artificial Language ◽  
Front Vowel ◽  
Learning Experiment ◽  
Artificial Language Learning

In the present study, we report on an artificial language learning experiment aiming to test the idea that it is easier to learn palatalization before a front vowel than it is to learn depalatalization in the same context. The motivation for the study comes from recent work by Czaplicki (2013), who provides a detailed analysis of palatalization-related effects in Polish, showing that they have no phonological basis. The conclusion he reaches is that ‘phonological naturalness does not play a role in linguistic computation’ Czaplicki (2013:32). It is nevertheless the case that palatalization is cross-linguistically much more common than depalatalization. If naturalness plays no role in computation, the typological asymmetry must arise from elsewhere, for example, from biases in learning difficulty. Our results provide provisory evidence that there is a small but statistically significant advantage for learning palatalization over depalatalization for adult Hungarian speakers.

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.

scholarly journals Compositional Hierarchical Structure Evolves through Cultural Transmission: An Experimental Study

2019 ◽  
Vol 4 (2) ◽  
pp. 83-107 ◽  
Author(s):  
Carmen Saldana ◽  
Simon Kirby ◽  
Robert Truswell ◽  
Kenny Smith
Keyword(s):  
Language Learning ◽  
Language Learners ◽  
Hierarchical Structure ◽  
Cultural Transmission ◽  
Artificial Language ◽  
Compositional Structure ◽  
Evolutionary Linguistics ◽  
Artificial Language Learning ◽  
Learning Techniques ◽  
Iterated Learning

AbstractCompositional hierarchical structure is a prerequisite for productive languages; it allows language learners to express and understand an infinity of meanings from finite sources (i.e., a lexicon and a grammar). Understanding how such structure evolved is central to evolutionary linguistics. Previous work combining artificial language learning and iterated learning techniques has shown how basic compositional structure can evolve from the trade-off between learnability and expressivity pressures at play in language transmission. In the present study we show, across two experiments, how the same mechanisms involved in the evolution of basic compositionality can also lead to the evolution of compositional hierarchical structure. We thus provide experimental evidence showing that cultural transmission allows advantages of compositional hierarchical structure in language learning and use to permeate language as a system of behaviour.

scholarly journals The importance of correctly characterising the English spelling system when devising and evaluating methods of reading instruction: Comment on Taylor, Davis, and Rastle (2017)

2018 ◽  
Vol 71 (7) ◽  
pp. 1497-1500 ◽  
Author(s):  
Jeffrey S Bowers ◽  
Peter N Bowers
Keyword(s):  
Language Learning ◽  
Reading Instruction ◽  
Test Performance ◽  
Artificial Language ◽  
Main Function ◽  
Artificial Language Learning ◽  
Mistaken Assumption ◽  
Basic And Applied Research ◽  
Based Instruction ◽  
Evaluating Methods

Taylor, Davis, and Rastle employed an artificial language learning paradigm to compare phonics and meaning-based approaches to reading instruction. Adults were taught consonant, vowel, and consonant (CVC) words composed of novel letters when the mappings between letters and sounds were completely systematic and the mappings between letters and meaning were completely arbitrary. At test, performance on naming tasks was better following training that emphasised the phonological rather than the semantic mappings, whereas performance on semantic tasks was similar in the two conditions. The authors concluded that these findings support phonics for early reading instruction in English. However, in our view, these conclusions are not justified given that the artificial language mischaracterised both the phonological and semantic mappings in English. Furthermore, the way participants studied the arbitrary letter-meaning correspondences bears little relation to meaning-based strategies used in schools. To compare phonics with meaning-based instruction it must be determined whether phonics is better than alternative forms of instruction that fully exploit the regularities within the semantic route. This is rarely assessed because of a widespread and mistaken assumption that underpins so much basic and applied research, namely, that the main function of spellings is to represent sounds.

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