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.

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.

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.

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 Using EEG to decode semantics during an artificial language learning task

2021 ◽  
Author(s):  
Chris Foster ◽  
Chad C. Williams ◽  
Olave E. Krigolson ◽  
Alona Fyshe
Keyword(s):  
Language Learning ◽  
Learning Task ◽  
Artificial Language ◽  
Artificial Language Learning

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.

Different trajectories of morphological overspecification and irregularity under imperfect language learning

2020 ◽  
pp. 283-305
Author(s):  
Aleksandrs Berdicevskis ◽  
Arturs Semenuks
Keyword(s):  
Language Learning ◽  
Artificial Language ◽  
Change Over Time ◽  
Morphological Complexity ◽  
Learning Experiment ◽  
Artificial Language Learning ◽  
Transmission Chain ◽  
Over Time

This chapter investigates how morphological complexity is related to socioecological parameters. Results of an iterated artificial language learning experiment are reported, with the focus on how two facets of complexity, overspecification and irregularity, change over time. The presence of imperfect learners in a transmission chain leads to a much stronger decrease in morphological overspecification in the language. Overspecification, however, does not usually get fully eliminated, and its partial decrease often leads to increase in irregularity, thus making languages simpler in one respect, but more complex in another. Additionally, higher irregularity decreases the learnability of the language, and this effect is stronger for imperfect learners compared to normal learners. Thus, to reach a fully simplified state, languages have to pass a suboptimal state (low overspecification, high irregularity), where they often get stuck.

scholarly journals Suffixing, prefixing, and the functional order of regularities in meaningful strings

Psihologija ◽  
2013 ◽  
Vol 46 (4) ◽  
pp. 377-396 ◽  
Author(s):  
Michael Ramscar
Keyword(s):  
Language Learning ◽  
General Purpose ◽  
Artificial Language ◽  
Information Structures ◽  
Learning Mechanisms ◽  
Artificial Language Learning ◽  
The World ◽  
The Relationship ◽  
The Brain ◽  
Better Than

The world?s languages tend to exhibit a suffixing preference, adding inflections to the ends of words, rather than the beginning of them. Previous works has suggested that this apparently universal preference arises out of the constraints imposed by general purpose learning mechanisms in the brain, and specifically, the kinds of information structures that facilitate discrimination learning (St Clair, Monaghan, & Ramscar, 2009). Here I show that learning theory predicts that prefixes and suffixes will tend to promote different kinds of learning: prefixes will facilitate the learning of the probabilities that any following elements in a sequence will follow a label, whereas suffixing will promote the abstraction of common dimensions from a set of preceding elements. The results of the artificial language learning experiment support this analysis: When words are learned with consistent prefixes, participants learned the relationship between the prefixes and the noun labels, and the relationship between the noun labels and the objects associated with them, better than when words were learned with consistent suffixes. When words were learned with consistent suffixes, participants treated similarly suffixed nouns as being more similar than nouns learned with consistent prefixes. It appears that while prefixes tend to make items more predictable and to make veridical discriminations easier, suffixes tended to make items cohere more, increasing the similarities between them.

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