Artificial Language Learning in Children

Culbertson, Jennifer; Schuler, Kathryn D.

doi:10.1146/annurev-linguistics-011718-012329

Cited by 24 publications

(21 citation statements)

References 96 publications

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“…In past work, ALL has corroborated or enhanced insights from natural language acquisition (Wonnacott, Newport, & Tanenhaus, 2008), language typology (Culbertson, Smolensky, & Legendre, 2012), and computational modeling (Schuler, Yang, & Newport, 2016), so we conclude that ALL can-and doesplay an important role in piecing together our understanding of learning biases. See Culbertson and Schuler (2019) and Morgan and Newport (1981) for further discussion of what ALL can tell us about language acquisition.…”

Section: Discussionmentioning

confidence: 99%

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

McCoy¹,

Culbertson²,

Smolensky³

et al. 2021

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

McCoy¹,

Culbertson²,

Smolensky³

et al. 2021

Preprint

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“…Artificial language learning has a long history as a tool for studying learning in both adults and children (see e.g. Culbertson & Schuler, 2019 for review), and allows us to explore how learners deviate from their data during learning. To extend this technique to study the cycle of learning and use shown in Figure 1, we can simply connect up a series of participants, using the language (re)produced by one participant as the learning input for the next participant in a chain of transmission, meaning that any errors or innovations in learning can accumulate over 'generations' of transmission.…”

Section: Non-compositionality In Englishmentioning

confidence: 99%

How language learning and language use create linguistic structure

Smith¹

2021

Preprint

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Languages persist through a cycle of learning and use: you learn a language through immersion in the language used in your linguistic community, and in using language to communicate you produce further linguistic data which others might learn from in turn. We know that languages change over historical time as a result of errors and innovations in these processes of learning and use; this paper reviews experimental and computational methods which have been developed to test the hypotheses that those same processes of learning and use are responsible for creating the fundamental structural properties shared by all human languages, including some of the design features that make language such a powerful tool for communication.

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“…Depictions of alien communication in film and literature often touch upon issues central to language evolution, and the potential challenge of speaking to aliens has often helped to frame linguistic research (Little, 2018). In empirical studies of langauge, especially those used in language evolution, many experiments use aliens explicitly to engage participants in the task of learning novel artifical languages to communicate with a naïve interlocutor (Kirby et al 2008;Culbertson and Schuler, 2019).…”

mentioning

confidence: 99%

“…Meanings range from simple and abstract (e.g., coloured shapes) to more concrete or complex (e.g., a child kicking a ball). Artificial language learning is often used to simulate the process of child language acquisition (Gómez andGerken, 2000, Culbertson andSchuler, 2019), or to compare how children learn artificial languages differently from adults (Hudson Kam andNewport, 2005, Folia et al 2010). These languages are often framed explicitly as 'alien', though the nonsense-words are constrained by the phonological rules of the participants' native language (e.g., for English, blick, napilu, but not *lbick, *ngipnu).…”

mentioning

confidence: 99%

Alien forms for alien language: investigating novel form spaces in cultural evolution

Cuskley

2019

Palgrave Commun

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A cornerstone of experimental studies in language evolution has been iterated artificial language learning: studies where participants learn of artificial 'alien' languages, and the product of their learning is then passed onto other participants successively. Results over the last decade show that some defining features of human language can arise under these experimental conditions, which use iteration to simulate processes of cultural transmission. The form modalities used in iterated learning studies have expanded considerably in recent years, but the dynamics of how learning a completely novel form system interact with processes of cultural transmission are only beginning to be explored. This paper provides a brief overview of artificial language learning studies in the context of language evolution, situating them in a framework which focuses on forms used in learning and production. This issue is further explored with an iterated experiment which focuses on the role of learning completely novel forms in isolation. This experiment uses a new set of graphical symbols called Ferros, which are produced using a virtual palette. Results show that properties of this novel form space-in particular, ease of articulation-have specific effects on sequence learning and evolution. These results have implications for how forms and modalities might constrain language systems, and demonstrate how the use of truly novel alien forms might be extended to address new questions in cultural and linguistic evolution.

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Artificial Language Learning in Children

Cited by 24 publications

References 96 publications

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

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

How language learning and language use create linguistic structure

Alien forms for alien language: investigating novel form spaces in cultural evolution

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