scholarly journals Compositional hierarchical structure evolves through cultural transmission: an experimental study

2018 ◽  
Author(s):  
Carmen Saldana ◽  
Simon Kirby ◽  
Rob 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

Compositional 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 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 Learning biases in person-number linearization

2020 ◽  
Author(s):  
Mora Maldonado ◽  
Carmen Saldana ◽  
Jennifer Culbertson
Keyword(s):  
Language Learning ◽  
Hierarchical Structure ◽  
Linear Order ◽  
Artificial Language ◽  
Learning Biases ◽  
Artificial Language Learning ◽  
Learning Techniques ◽  
English Speaking

The idea that universal representations of hierarchical structure constrain patterns of linear order is a central to many linguistic theories. In this paper we use Artificial Language Learning techniques to experimentally probe this claim. Specifically, we investigate how a hypothesized hierarchy of φ-features impacts the linearization of person and number affixes by (English-speaking) learners in the lab.

scholarly journals Animacy Distinctions Arise from Iterated Learning

2018 ◽  
Vol 4 (1) ◽  
pp. 552-565 ◽  
Author(s):  
Virve-Anneli Vihman ◽  
Diane Nelson ◽  
Simon Kirby
Keyword(s):  
Language Learning ◽  
Learning Task ◽  
Entropy Measure ◽  
Artificial Language ◽  
Morphological Complexity ◽  
Artificial Language Learning ◽  
Noun Classes ◽  
The World ◽  
Iterated Learning ◽  
A Chain

Abstract Linguistic animacy reflects a particular construal of biological distinctions encountered in the world, passed through cultural and cognitive filters. This study explores the process by which our construal of animacy becomes encoded in the grammars of human languages. We ran an iterated learning experiment investigating the effect of animacy on language transmission. Participants engaged in a simple artificial language learning task in which they were asked to learn which affix was assigned to each noun in the language. Though initially random, the language each participant produced at test became the language that the subsequent participant in a chain was trained on. Results of the experiment were analysed in terms of learnability, measured through the accuracy of responses, and structure, using an entropy measure. We found that the learnability of languages increased over generations, as expected, but entropy did not decrease. Languages did not become formally simpler over time. Instead, structure emerged through a reorganisation of noun classes around animacy-based categories. The use of semantic animacy distinctions allowed languages to retain morphological complexity while becoming more learnable. Our study shows that grammatical reflexes of animacy distinctions can arise out of learning alone, and that structuring grammar based on animacy can make languages more learnable.

COMPLEX SYSTEMS IN LANGUAGE EVOLUTION: THE CULTURAL EMERGENCE OF COMPOSITIONAL STRUCTURE

2003 ◽  
Vol 06 (04) ◽  
pp. 537-558 ◽  
Author(s):  
KENNY SMITH ◽  
HENRY BRIGHTON ◽  
SIMON KIRBY
Keyword(s):  
Language Learners ◽  
Cultural Evolution ◽  
Complex Adaptive Systems ◽  
Cultural Transmission ◽  
Adaptive Systems ◽  
Language Evolution ◽  
Biological Evolution ◽  
Compositional Structure ◽  
Complex Adaptive ◽  
Iterated Learning

Language arises from the interaction of three complex adaptive systems — biological evolution, learning, and culture. We focus here on cultural evolution, and present an Iterated Learning Model of the emergence of compositionality, a fundamental structural property of language. Our main result is to show that the poverty of the stimulus available to language learners leads to a pressure for linguistic structure. When there is a bottleneck on cultural transmission, only a language which is generalizable from sparse input data is stable. Language itself evolves on a cultural time-scale, and compositionality is language's adaptation to stimulus poverty.

scholarly journals Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar

2021 ◽  
Author(s):  
R. Thomas McCoy ◽  
Jennifer Culbertson ◽  
Paul Smolensky ◽  
Géraldine Legendre
Keyword(s):  
Finite Number ◽  
Language Learning ◽  
Language Learners ◽  
Building Blocks ◽  
Artificial Language ◽  
Generative Capacity ◽  
Learning Biases ◽  
Artificial Language Learning ◽  
Training Sequences ◽  
Center Embedding

Human language is often assumed to make "infinite use of finite means" - that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

Domain generalisation in artificial language learning

Phonology ◽  
2014 ◽  
Vol 31 (3) ◽  
pp. 399-433 ◽  
Author(s):  
Scott Myers ◽  
Jaye Padgett
Keyword(s):  
Language Learning ◽  
Language Learners ◽  
Artificial Language ◽  
Final Position ◽  
Artificial Language Learning ◽  
Distributional Patterns

Many languages have restrictions on word-final segments, such as a requirement that any word-final obstruent be voiceless. There is a phonetic basis for such restrictions at the ends of utterances, but not the ends of words. Historical linguists have long noted this mismatch, and have attributed it to an analogical generalisation of such restrictions from utterance-final to word-final position. To test whether language learners actually generalise in this way, two artificial language learning experiments were conducted. Participants heard nonsense utterances in which there was a restriction on utterance-final obstruents, but in which no information was available about word-final utterance-medial obstruents. They were then tested on utterances that included obstruents in both positions. They learned the pattern and generalised it to word-final utterance-medial position, confirming that learners are biased toward word-based distributional patterns.

scholarly journals Is regularisation uniform across linguistic levels? Comparing learning and production of unconditioned probabilistic variation in morphology and word order.

2018 ◽  
Author(s):  
Carmen Saldana ◽  
Kenny Smith ◽  
Simon Kirby ◽  
Jennifer Culbertson
Keyword(s):  
Language Learning ◽  
Language Learners ◽  
Morphological Variation ◽  
Word Order ◽  
Artificial Language ◽  
Linguistic Context ◽  
Syntactic Variation ◽  
Artificial Language Learning ◽  
The Social ◽  
Natural Language Learning

Languages exhibit variation at all linguistic levels, from phonology, to the lexicon, to syntax. Importantly, that variation tends to be (at least partially) conditioned on some aspect of the social or linguistic context. When variation is unconditioned, language learners regularise it—removing some or all variants, or conditioning variant use on context. Previous studies using artificial language learning experiments have documented regularising behaviour in learning of lexical, morphological, and syntactic variation. These studies implicitly assume that regularisation reflects uniform mechanisms and processes across linguistic levels. However, studies on natural language learning and pidginisation suggest that morphological and syntactic variation may be treated differently. In particular, there is evidence that morphological variation may be more susceptible to regularisation (Good 2015;Siegel 2006; Slobin 1986). Here we provide the first systematic comparison of the strength of regularisation across these two linguistic levels. In line with previous studies, we find that the presence of a favoured variant can induce different degrees of regularisation. However, when input languages are carefully matched—with comparable initial variability, and no variant-specific biases—regularisation can be comparable across morphology and word order. This is the case regard-less of whether the task is explicitly communicative. Overall, our findings suggest an overarching regularising mechanism at work, with apparent differences among levels likely due to differences in inherent complexity or variant-specific biases. Differences between production and encoding in our tasks further suggests this overarching mechanism is driven by production

scholarly journals ULearnEnglish: An Open Ubiquitous System for Assisting in Learning English Vocabulary

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1692
Author(s):  
Letícia Garcia da Silva ◽  
Eduardo Gonçalves de Azevedo Neto ◽  
Rosemary Francisco ◽  
Jorge Luis Victória Barbosa ◽  
Luis Augusto Silva ◽  
...  
Keyword(s):  
Language Learning ◽  
Technology Acceptance ◽  
Language Learners ◽  
Incidental Learning ◽  
Ease Of Use ◽  
L2 Acquisition ◽  
Communication Problems ◽  
Learning Techniques ◽  
Ubiquitous System ◽  
Acceptance Model

Language learners often face communication problems when they need to express themselves and do not have the ability to do so. On the other hand, continuous advances in technology are creating new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate our proposal, 15 learners used the developed system, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the application of incidental learning techniques in combination with the learner context. ULearnEnglish achieved an acceptance rate of 78.66% for the perception of utility, 96% for the perception of ease of use, 86.5% for user context assessment, and 88% for ubiquity. Among its main contributions, this study demonstrates a possible tool for ubiquitous use in the future in language learning; additionally, further studies can use the available resources to develop the system.

scholarly journals In search of resilient and fragile properties of language

2014 ◽  
Vol 41 (S1) ◽  
pp. 64-77 ◽  
Author(s):  
SUSAN GOLDIN-MEADOW
Keyword(s):  
Young Children ◽  
Language Learning ◽  
Language Learners ◽  
Great Promise ◽  
Artificial Language ◽  
Fundamental Properties ◽  
Linguistic Input ◽  
Language Input

ABSTRACTYoung children are skilled language learners. They apply their skills to the language input they receive from their parents and, in this way, derive patterns that are statistically related to their input. But being an excellent statistical learner does not explain why children who are not exposed to usable linguistic input nevertheless communicate using systems containing the fundamental properties of language. Nor does it explain why learners sometimes alter the linguistic input to which they are exposed (input from either a natural or an artificial language). These observations suggest that children are prepared to learn language. Our task now, as it was in 1974, is to figure out what they are prepared with – to identify properties of language that are relatively easy to learn, the resilient properties, as well as properties of language that are more difficult to learn, the fragile properties. The new tools and paradigms for describing and explaining language learning that have been introduced into the field since 1974 offer great promise for accomplishing this task.

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