1,416 research outputs found
Chestnut-crowned babbler calls are composed of meaningless shared building blocks
A core component of human language is its combinatorial sound system: meaningful signals are built from different combinations of meaningless sounds. Investigating whether non-human communication systems are also combinatorial is hampered by difficulties in identifying the extent to which vocalizations are constructed from shared, meaningless building blocks. Here we present a novel approach to circumvent this difficulty and show that a pair of functionally distinct chestnut-crowned babbler (Pomatostomus ruficeps) vocalizations can be decomposed into perceptibly distinct, meaningless entities that are shared across the two calls. Specifically, by focusing on the acoustic distinctiveness of sound elements using a habituation-discrimination paradigm on wild-caught babblers under standardized aviary conditions, we show that two multi-element calls are composed of perceptibly distinct sounds that are reused in different arrangements across the two calls. Furthermore, and critically, we show that none of the five constituent elements elicits functionally relevant responses in receivers, indicating that the constituent sounds do not carry the meaning of the call; so are contextually meaningless. Our work, which allows combinatorial systems in animals to be more easily identified, suggests that animals can produce functionally distinct calls that are built in a way superficially reminiscent of the way that humans produce morphemes and words. The results reported lend credence to the recent idea that language’s combinatorial system may have been preceded by a superficial stage where signalers neither needed to be cognitively aware of the combinatorial strategy in place, nor of its building blocks
COSMO (“Communicating about Objects using Sensory–Motor Operations”): A Bayesian modeling framework for studying speech communication and the emergence of phonological systems
International audienceWhile the origin of language remains a somewhat mysterious process, understanding how human language takes specific forms appears to be accessible by the experimental method. Languages, despite their wide variety, display obvious regularities. In this paper, we attempt to derive some properties of phonological systems (the sound systems for human languages) from speech communication principles. We introduce a model of the cognitive architecture of a communicating agent, called COSMO (for “Communicating about Objects using Sensory–Motor Operations') that allows a probabilistic expression of the main theoretical trends found in the speech production and perception literature. This enables a computational comparison of these theoretical trends, which helps us to identify the conditions that favor the emergence of linguistic codes. We present realistic simulations of phonological system emergence showing that COSMO is able to predict the main regularities in vowel, stop consonant and syllable systems in human languages
Computer Simulation of Musical Evolution: A Lesson from Whales
Simulating musical creativity using computers needs more than the ability to devise elegant computational implementations of sophisticated algorithms. It requires, firstly, an understanding of what phenomena might be regarded as music; and, secondly, an understanding of the nature of such phenomena — including their evolutionary history, their recursive-hierarchic structure, and the mechanisms by which they are transmitted within cultural groups. To understand these issues it is fruitful to compare human music, and indeed human language, with analogous phenomena in other areas of the animal kingdom. Whale song, specifically that of the humpback (Megaptera novaeangeliae), possesses many structural and functional similarities to human music (as do certain types of birdsong). Using a memetic perspective, this paper compares the “musilanguage” of humpbacks with the music of humans, and aims to identify a number of shared characteristics. A consequence of nature and nurture, these commonalities appear to arise partly from certain constraints of perception and cognition (and thus they determine an aspect of the environment within which the “musemes” (musical memes) constituting whale vocalizations and human music is replicated), and partly from the social-emotive-embodied and sexual-selective nature of musemic transmission. The paper argues that Universal-Darwinian forces give rise to uniformities of structure in phenomena we might regard as “music”, irrespective of the animal group — certain primates, cetaceans or birds - within which it occurs. It considers the extent to which whale song might be regarded as creative, by invoking certain criteria used to assess this attribute in human music. On the basis of these various comparisons, the paper concludes by attempting to draw conclusions applicable to those engaged in designing evolutionary music simulation/generation algorithms
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges
Computational models of emergent communication in agent populations are
currently gaining interest in the machine learning community due to recent
advances in Multi-Agent Reinforcement Learning (MARL). Current contributions
are however still relatively disconnected from the earlier theoretical and
computational literature aiming at understanding how language might have
emerged from a prelinguistic substance. The goal of this paper is to position
recent MARL contributions within the historical context of language evolution
research, as well as to extract from this theoretical and computational
background a few challenges for future research
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Emergent Typological Effects of Agent-Based Learning Models in Maximum Entropy Grammar
This dissertation shows how a theory of grammatical representations and a theory of learning can be combined to generate gradient typological predictions in phonology, predicting not only which patterns are expected to exist, but also their relative frequencies: patterns which are learned more easily are predicted to be more typologically frequent than those which are more difficult.
In Chapter 1 I motivate and describe the specific implementation of this methodology in this dissertation. Maximum Entropy grammar (Goldwater & Johnson 2003) is combined with two agent-based learning models, the iterated and the interactive learning model, each of which mimics a type of learning dynamic observed in natural language acquisition.
In Chapter 2 I illustrate how this system works using a simplified, abstract example typology, and show how the models generate a bias away from patterns which rely on cumulative constraint interaction ( gang effects ), and a bias away from variable patterns. Both of these biases match observed trends in natural language typology and psycholinguistic experiments.
Chapter 3 further explores the models\u27 bias away from cumulative constraint interaction using an empirical test case: the typology of possible patterns of contrast between two fricatives. This typology yields five possible patterns, the rarest of which is the result of a gang effect. The results of simulations performed with both models produce a bias against the gang effect pattern.
Chapter 4 further explores the models\u27 bias away from variation using evidence from artificial grammar learning experiments, in which human participants show a bias away from variable patterns (e.g. Smith & Wonnacott 2010). This test case was chosen additionally to disambiguate between variable behavior within a lexical item (variation), and variable behavior across lexical items (exceptionality). The results of simulations performed with both learning models are consistent with the observed bias away from variable patterns in humans.
The results of the iterated and interactive learning models presented in this dissertation provide support for the use of this methodology in investigating the typological predictions of linguistic theories of grammar and learning, as well as in addressing broader questions regarding the source of gradient typological trends, and whether certain properties of natural language must be innately specified, or might emerge through other means
A report on the workshop on complexity in linguistics: Developmental and evolutionary perspectives
published_or_final_versio
Compression and communication in the cultural evolution of linguistic structure
Language exhibits striking systematic structure. Words are composed of combinations of reusable sounds, and those words in turn are combined to form complex sentences. These properties make language unique among natural communication systems and enable our species to convey an open-ended set of messages. We provide a cultural evolutionary account of the origins of this structure. We show, using simulations of rational learners and laboratory experiments, that structure arises from a trade-off between pressures for compressibility (imposed during learning) and expressivity (imposed during communication). We further demonstrate that the relative strength of these two pressures can be varied in different social contexts, leading to novel predictions about the emergence of structured behaviour in the wild
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