scholarly journals Amount of Learning and Signal Stability Modulate Emergence of Combinatorial Structure and Iconicity in Novel Signaling Systems

2020 ◽  
Author(s):  
Vera Kempe ◽  
Nikolay Panayotov ◽  
Nicolas Gauvrit ◽  
Sheila J Cunningham ◽  
Monica Tamariz
Keyword(s):  
Language Learning ◽  
Temporal Stability ◽  
Visual Signals ◽  
Combinatorial Structure ◽  
Signal Space ◽  
Signaling Systems ◽  
Signal Stability ◽  
Compositional Structure ◽  
Signal Characteristics ◽  
Iterated Learning

Iterated language learning experiments that explore emergence of linguistic structure in the laboratory vary considerably in methodological implementation, limiting generalizability of findings. Most studies also restrict themselves to exploring the emergence of combinatorial and compositional structure in isolation. Here, we use a novel signal space comprising binary auditory and visual sequences and manipulate amount of learning and temporal stability of these signals. Participants had to learn signals for meanings differing in size, shape and brightness; their productions in the test phase were transmitted to the next participant. Across transmission chains of 10 generations each, Experiment 1 varied how much learning of auditory signals took place, and Experiment 2 varied temporal stability of visual signals. We found that combinatorial structure emerged most reliably with greater amount of learning and when signals were temporally stable. Iconicity emerged with reduced amount of learning, as opportunity for rote-memorization appeared to hamper exploration of the iconic affordances of the signal space. However, emergence of combinatoriality and iconicity in these entirely unfamiliar signaling systems was too fragile to allow for compositional signal-meaning mappings to emerge, so learnability did not improve over the course of transmission. These findings underscore the importance of systematically manipulating training conditions and signal characteristics in iterated learning language learning experiments and suggest that combinatorial structure and iconicity may be a prerequisite for emergence of compositional structure in novel signaling systems.

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 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 The emergence of duality of patterning through iterated learning: Precursors to phonology in a visual lexicon

2012 ◽  
Vol 4 (4) ◽  
pp. 381-418 ◽  
Author(s):  
Alex Del Giudice
Keyword(s):  
Design Feature ◽  
Combinatorial Structure ◽  
Human Language ◽  
Lexical Structure ◽  
Small Stock ◽  
Iterated Learning ◽  
Human Participants ◽  
Duality Of Patterning ◽  
Future Work ◽  
Infinite Set

AbstractDuality of Patterning, one of Hockett's (1960) proposed design features unique to human language, refers in part to the arrangements of a relatively small stock of distinguishable meaningless sounds which are combined to create a potentially infinite set of morphemes. Literature regarding the emergence of this design feature is less abundant than that exploring other levels of structure as focus is more often given to the emergence of syntax. In an effort to explore where combinatorial structure of meaningless elements arises the results of two pilot experiments are presented within which we observe human participants modifying a small lexicon of visual symbols through a process of iterated learning. As this lexicon evolves there is evidence that it becomes simpler and more learnable, more easily transmitted. I argue that these features are a consequence of spontaneous emergence of combinatorial, sub-lexical structure in the lexicon, that the pattern of emergence is more complex than the most widely espoused explanation suggests, and I propose ways in which future work can build on what we learn from these pilot experiments to confirm this hypothesis.

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 Amount of Learning and Signal Stability Modulate Emergence of Structure and Iconicity in Novel Signaling Systems

2021 ◽  
Vol 45 (11) ◽  
Author(s):  
Vera Kempe ◽  
Nicolas Gauvrit ◽  
Nikolay Panayotov ◽  
Sheila Cunningham ◽  
Monica Tamariz
Keyword(s):  
Signaling Systems ◽  
Signal Stability

LANGUAGE DYNAMICS IN STRUCTURED FORM AND MEANING SPACES

2012 ◽  
Vol 15 (03n04) ◽  
pp. 1150021 ◽  
Author(s):  
BART DE BOER ◽  
TESSA VERHOEF
Keyword(s):  
Complex Form ◽  
Combinatorial Structure ◽  
Human Language ◽  
Functional Constraints ◽  
Cognitive Constraints ◽  
Learning Experiment ◽  
Fundamental Properties ◽  
Previous Generation ◽  
Relative Contribution ◽  
Iterated Learning

This paper reviews how the structure of form and meaning spaces influences the nature and the dynamics of the form-meaning mappings in language. In general, in a structured form or meaning space, not all forms and meanings are equivalent: some forms and some meanings are more easily confused with each other than with other forms or meanings. We first give a formalization of this idea, and explore how it influences robust form-meaning mappings. It is shown that some fundamental properties of human language, such as discreteness and combinatorial structure as well as universals of sound systems of human languages follow from optimal communication in structured form and meaning spaces. We also argue that some properties of human language follow less from these fundamental issues, and more from cognitive constraints. We then show that it is possible to experimentally investigate the relative contribution of functional constraints and of cognitive constraints. We illustrate this with an example of one of our own experiments, in which experimental participants have to learn a set of complex form-meaning mappings that have been produced by a previous generation of participants. Theoretically predicted properties appear in the sets of signals that emerge in this iterated learning experiment.

scholarly journals The origins of duality of patterning in artificial whistled languages

2012 ◽  
Vol 4 (4) ◽  
pp. 357-380 ◽  
Author(s):  
Tessa Verhoef
Keyword(s):  
Cultural Transmission ◽  
Cognitive Biases ◽  
Combinatorial Structure ◽  
Basic Design ◽  
Human Language ◽  
Design Features ◽  
Finite Set ◽  
Iterated Learning ◽  
Duality Of Patterning

AbstractIn human speech, a finite set of basic sounds is combined into a (potentially) unlimited set of well-formed morphemes. Hockett (1960) placed this phenomenon under the term ‘duality of patterning’ and included it as one of the basic design features of human language. Of the thirteen basic design features Hockett proposed, duality of patterning is the least studied and it is still unclear how it evolved in language. Recent work shedding light on this is summarized in this paper and experimental data is presented. This data shows that combinatorial structure can emerge in an artificial whistled language through cultural transmission as an adaptation to human cognitive biases and learning. In this work the method of experimental iterated learning (Kirby et al. 2008) is used, in which a participant is trained on the reproductions of the utterances the previous participant learned. Participants learn and recall a system of sounds that are produced with a slide whistle. Transmission from participant to participant causes the whistle systems to change and become more learnable and more structured. These findings follow from qualitative observations, quantitative measures and a follow-up experiment that tests how well participants can learn the emerged whistled languages by generalizing from a few examples.

scholarly journals Adults are more efficient in creating and transmitting novel signalling systems than children

2018 ◽  
Author(s):  
Vera Kempe ◽  
Nicolas Gauvrit ◽  
Alison Gibson ◽  
Margaret Jamieson
Keyword(s):  
Language Learning ◽  
Communication Systems ◽  
Language Evolution ◽  
Native Language ◽  
Referential Communication ◽  
Iterated Learning ◽  
Cognitive Maturity ◽  
Signalling Systems ◽  
Communicative Effectiveness

Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5-7-year old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4-5-year old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems is unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution.

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