What you see is what you hear: How visual prosody affects artificial language learning in adults and children

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.

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Learning Paradigmatic Structure

Changing Minds Changing Tools ◽

10.7551/mitpress/9780262037860.003.0008 ◽

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.

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Learning biases in opaque interactions

Phonology ◽

10.1017/s0952675719000320 ◽

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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Compositional Hierarchical Structure Evolves through Cultural Transmission: An Experimental Study

Journal of Language Evolution ◽

10.1093/jole/lzz002 ◽

2019 ◽

Vol 4 (2) ◽

pp. 83-107 ◽

Cited By ~ 3

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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Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2019.00358 ◽

2019 ◽

Vol 13 ◽

Author(s):

Ethan Jost ◽

Katherine Brill-Schuetz ◽

Kara Morgan-Short ◽

Morten H. Christiansen

Keyword(s):

Language Learning ◽

Learning Task ◽

Artificial Language ◽

Artificial Language Learning ◽

Term Retention ◽

Long Term Retention

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

Quarterly Journal of Experimental Psychology ◽

10.1177/1747021818759477 ◽

2018 ◽

Vol 71 (7) ◽

pp. 1497-1500 ◽

Cited By ~ 5

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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Variability, negative evidence, and the acquisition of verb argument constructions

Journal of Child Language ◽

10.1017/s0305000910000012 ◽

2010 ◽

Vol 37 (3) ◽

pp. 607-642 ◽

Cited By ~ 49

Author(s):

AMY PERFORS ◽

JOSHUA B. TENENBAUM ◽

ELIZABETH WONNACOTT

Keyword(s):

Language Learning ◽

Time Course ◽

Broad Class ◽

Artificial Language ◽

Negative Evidence ◽

Levels Of Abstraction ◽

Approach To Learning ◽

Artificial Language Learning ◽

Learning Patterns ◽

Verb Constructions

ABSTRACTWe present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object names. Here, we demonstrate that the same model captures several phenomena in the acquisition of verb constructions. Our model, like adults in a series of artificial language learning experiments, makes inferences about the distributional statistics of verbs on several levels of abstraction simultaneously. It also produces the qualitative learning patterns displayed by children over the time course of acquisition. These results suggest that the patterns of generalization observed in both children and adults could emerge from basic assumptions about the nature of learning. They also provide an example of a broad class of computational approaches that can resolve Baker's Paradox.

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