Learnable vs. Unlearnable Harmony Patterns

2015 ◽  
Vol 46 (3) ◽  
pp. 425-451 ◽  
Author(s):  
Regine Lai
Keyword(s):  
Language Learning ◽  
Empirical Evidence ◽  
Artificial Language ◽  
Learning Paradigm ◽  
Long Distance ◽  
Artificial Language Learning ◽  
Computational Properties

The present study provides empirical evidence for Heinz’s (2010) Subregular Hypothesis, which predicts that some gaps found in the typology of phonotactic patterns are due to learnability—more specifically, that only phonotactic patterns with specific computational properties are humanly learnable. The study compares the learnability of two long-distance harmony patterns that differ typologically (attested vs. unattested) and computationally (Strictly Piecewise vs. Locally Testable) using the artificial-language-learning paradigm. The results reveal a general bias toward learning the attested, Strictly Piecewise pattern, exactly as the Subregular Hypothesis predicts.

The Complexities of Morphology

2020 ◽  
Keyword(s):  
Experimental Data ◽  
Language Learning ◽  
Empirical Evidence ◽  
Minimum Description Length ◽  
Artificial Language ◽  
Morphological Complexity ◽  
Artificial Language Learning ◽  
Crosslinguistic Variation

The volume deals with the multifaceted nature of morphological complexity understood as a composite rather than unitary phenomenon as it shows an amazing degree of crosslinguistic variation. It features an Introduction by the editors that critically discusses some of the foundational assumptions informing contemporary views on morphological complexity, eleven chapters authored by an excellent set of contributors, and a concluding chapter by Östen Dahl that reviews various approaches to morphological complexity addressed in the preceding contributions and focuses on the minimum description length approach. The central eleven chapters approach morphological complexity from different perspectives, including the language-particular, the crosslinguistic, and the acquisitional one, and offer insights into issues such as the quantification of morphological complexity, its syntagmatic vs. paradigmatic aspects, diachronic developments including the emergence and acquisition of complexity, and the relations between morphological complexity and socioecological parameters of language. The empirical evidence includes data from both better-known languages such as Russian, and lesser-known and underdescribed languages from Africa, Australia, and the Americas, as well as experimental data drawn from iterated artificial language learning.

scholarly journals Learnable vs. unlearnable harmony patterns

2012 ◽  
Vol 3 ◽  
pp. 12
Author(s):  
Regine Lai
Keyword(s):  
Language Learning ◽  
Finite State Machine ◽  
Necessary Condition ◽  
Sufficient Condition ◽  
Artificial Language ◽  
Artificial Language Learning ◽  
Regular Class ◽  
Finite State ◽  
Computational Properties ◽  
Regular Patterns

Phonological patterns have been characterized as regular, and regular patterns are those that are accepted by a finite state machine. However, being regular is only a necessary condition, but not a sufficient condition for phonology. Two subregular classes which further restrict the computational properties of phonological patterns have been identified: strictly piecewise (SP), and strictly local (SL). In this study, the learnability of a SP pattern and a pattern from the regular class (but not SP/SL) were tested by using the artificial language learning paradigm, and the results suggest that the identified computational boundaries are psychologically real.

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.

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