1
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Carr JW, Rastle K. Why do languages tolerate heterography? An experimental investigation into the emergence of informative orthography. Cognition 2024; 249:105809. [PMID: 38781759 DOI: 10.1016/j.cognition.2024.105809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
It is widely acknowledged that opaque orthographies place additional demands on learning, often requiring many years to fully acquire. It is less widely recognized, however, that such opacity may offer certain benefits in the context of reading. For example, heterographic homophones such as ⟨knight⟩ and ⟨night⟩ (words that sound the same but which are spelled differently) impose additional costs in learning but reduce ambiguity in reading. Here, we consider the possibility that-left to evolve freely-writing systems will sometimes choose to forego some simplicity for the sake of informativeness when there is functional pressure to do so. We investigate this hypothesis by simulating the evolution of orthography as it is transmitted from one generation to the next, both with and without a communicative pressure for ambiguity avoidance. In addition, we consider two mechanisms by which informative heterography might be selected for: differentiation, in which new spellings are created to differentiate meaning (e.g., ⟨lite⟩ vs. ⟨light⟩), and conservation, in which heterography arises as a byproduct of sound change (e.g., ⟨meat⟩ vs. ⟨meet⟩). Under pressure from learning alone, orthographic systems become transparent, but when combined with communicative pressure, they tend to favor some additional informativeness. Nevertheless, our findings also suggest that, in the long term, simpler, transparent spellings may be preferred in the absence of top-down explicit teaching.
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Affiliation(s)
- Jon W Carr
- Department of Psychology, Royal Holloway, University of London, United Kingdom.
| | - Kathleen Rastle
- Department of Psychology, Royal Holloway, University of London, United Kingdom
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2
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Jacoby N, Polak R, Grahn JA, Cameron DJ, Lee KM, Godoy R, Undurraga EA, Huanca T, Thalwitzer T, Doumbia N, Goldberg D, Margulis EH, Wong PCM, Jure L, Rocamora M, Fujii S, Savage PE, Ajimi J, Konno R, Oishi S, Jakubowski K, Holzapfel A, Mungan E, Kaya E, Rao P, Rohit MA, Alladi S, Tarr B, Anglada-Tort M, Harrison PMC, McPherson MJ, Dolan S, Durango A, McDermott JH. Commonality and variation in mental representations of music revealed by a cross-cultural comparison of rhythm priors in 15 countries. Nat Hum Behav 2024; 8:846-877. [PMID: 38438653 PMCID: PMC11132990 DOI: 10.1038/s41562-023-01800-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 12/07/2023] [Indexed: 03/06/2024]
Abstract
Music is present in every known society but varies from place to place. What, if anything, is universal to music cognition? We measured a signature of mental representations of rhythm in 39 participant groups in 15 countries, spanning urban societies and Indigenous populations. Listeners reproduced random 'seed' rhythms; their reproductions were fed back as the stimulus (as in the game of 'telephone'), such that their biases (the prior) could be estimated from the distribution of reproductions. Every tested group showed a sparse prior with peaks at integer-ratio rhythms. However, the importance of different integer ratios varied across groups, often reflecting local musical practices. Our results suggest a common feature of music cognition: discrete rhythm 'categories' at small-integer ratios. These discrete representations plausibly stabilize musical systems in the face of cultural transmission but interact with culture-specific traditions to yield the diversity that is evident when mental representations are probed across many cultures.
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Affiliation(s)
- Nori Jacoby
- Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany.
- Presidential Scholars in Society and Neuroscience, Columbia University, New York, NY, USA.
| | - Rainer Polak
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Blindern, Oslo, Norway
| | - Jessica A Grahn
- Brain and Mind Institute and Department of Psychology, University of Western Ontario, London, Ontario, Canada
| | - Daniel J Cameron
- Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Kyung Myun Lee
- School of Digital Humanities and Social Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
- Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Ricardo Godoy
- Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA
| | - Eduardo A Undurraga
- Escuela de Gobierno, Pontificia Universidad Católica de Chile, Santiago, Chile
- CIFAR Azrieli Global Scholars programme, CIFAR, Toronto, Ontario, Canada
| | - Tomás Huanca
- Centro Boliviano de Investigación y Desarrollo Socio Integral, San Borja, Bolivia
| | | | - Noumouké Doumbia
- Sciences de l'Education, Université Catholique d'Afrique de l'Ouest, Bamako, Mali
| | - Daniel Goldberg
- Department of Music, University of Connecticut, Storrs, CT, USA
| | | | - Patrick C M Wong
- Department of Linguistics & Modern Languages and Brain and Mind Institute, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Luis Jure
- School of Music, Universidad de la República, Montevideo, Uruguay
| | - Martín Rocamora
- Signal Processing Department, School of Engineering, Universidad de la República, Montevideo, Uruguay
- Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Shinya Fujii
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - Patrick E Savage
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Jun Ajimi
- Department of Traditional Japanese Music, Tokyo University of the Arts, Tokyo, Japan
| | - Rei Konno
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | - Sho Oishi
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
| | | | - Andre Holzapfel
- Division of Media Technology and Interaction Design, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Esra Mungan
- Department of Psychology, Bogazici University, Istanbul, Turkey
| | - Ece Kaya
- Max Planck Research Group 'Neural and Environmental Rhythms', Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Cognitive Science Master Program, Bogazici University, Istanbul, Turkey
| | - Preeti Rao
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Mattur A Rohit
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | | | - Bronwyn Tarr
- Department of Cognitive and Evolutionary Anthropology, University of Oxford, Oxford, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Manuel Anglada-Tort
- Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Department of Psychology, Goldsmiths, University of London, London, UK
| | - Peter M C Harrison
- Computational Auditory Perception Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
- Faculty of Music, University of Cambridge, Cambridge, UK
| | - Malinda J McPherson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sophie Dolan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Wellesley College, Wellesley, MA, USA
| | - Alex Durango
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Neurosciences Graduate Program, Stanford University, Stanford, CA, USA
| | - Josh H McDermott
- Faculty of Music, University of Cambridge, Cambridge, UK.
- Program in Speech and Hearing Biosciences and Technology, Harvard University, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Center for Brains, Minds & Machines, Massachusetts Institute of Technology, Cambridge, MA, USA.
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3
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Arnon I, Kirby S. Cultural evolution creates the statistical structure of language. Sci Rep 2024; 14:5255. [PMID: 38438558 PMCID: PMC10912608 DOI: 10.1038/s41598-024-56152-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/01/2024] [Indexed: 03/06/2024] Open
Abstract
Human language is unique in its structure: language is made up of parts that can be recombined in a productive way. The parts are not given but have to be discovered by learners exposed to unsegmented wholes. Across languages, the frequency distribution of those parts follows a power law. Both statistical properties-having parts and having them follow a particular distribution-facilitate learning, yet their origin is still poorly understood. Where do the parts come from and why do they follow a particular frequency distribution? Here, we show how these two core properties emerge from the process of cultural evolution with whole-to-part learning. We use an experimental analog of cultural transmission in which participants copy sets of non-linguistic sequences produced by a previous participant: This design allows us to ask if parts will emerge purely under pressure for the system to be learnable, even without meanings to convey. We show that parts emerge from initially unsegmented sequences, that their distribution becomes closer to a power law over generations, and, importantly, that these properties make the sets of sequences more learnable. We argue that these two core statistical properties of language emerge culturally both as a cause and effect of greater learnability.
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Affiliation(s)
- Inbal Arnon
- Psychology Department, Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Simon Kirby
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
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4
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van de Pol I, Lodder P, van Maanen L, Steinert-Threlkeld S, Szymanik J. Quantifiers satisfying semantic universals have shorter minimal description length. Cognition 2023; 232:105150. [PMID: 36563568 DOI: 10.1016/j.cognition.2022.105150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/24/2022]
Abstract
Despite wide variation among natural languages, there are linguistic properties thought to be universal to all or nearly all languages. Here, we consider universals at the semantic level, in the domain of quantifiers, which are given by the properties of monotonicity, quantity, and conservativity, and we investigate whether these universals might be explained by differences in complexity. First, we use a minimal pair methodology and compare the complexities of individual quantifiers using approximate Kolmogorov complexity. Second, we use a simple yet expressive grammar to generate a large collection of quantifiers and we investigate their complexities at an aggregate level in terms of both their minimal description lengths and their approximate Kolmogorov complexities. For minimal description length we find that quantifiers satisfying semantic universals are simpler: they have a shorter minimal description length. For approximate Kolmogorov complexity we find that monotone quantifiers have a lower Kolmogorov complexity than non-monotone quantifiers and for quantity and conservativity we find that approximate Kolmogorov complexity does not scale robustly. These results suggest that the simplicity of quantifier meanings, in terms of their minimal description length, partially explains the presence of semantic universals in the domain of quantifiers.
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Affiliation(s)
- Iris van de Pol
- Institute for Logic, Language and Computation, University of Amsterdam, the Netherlands.
| | - Paul Lodder
- Institute for Logic, Language and Computation, University of Amsterdam, the Netherlands
| | | | | | - Jakub Szymanik
- Center for Mind/Brain Sciences and Dept. of Information Engineering and Computer Science, University of Trento, Italy
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5
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Saldana C, Herce B, Bickel B. More or Less Unnatural: Semantic Similarity Shapes the Learnability and Cross-Linguistic Distribution of Unnatural Syncretism in Morphological Paradigms. Open Mind (Camb) 2022; 6:183-210. [PMID: 36439066 PMCID: PMC9692061 DOI: 10.1162/opmi_a_00062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 08/28/2022] [Indexed: 01/25/2023] Open
Abstract
Morphological systems often reuse the same forms in different functions, creating what is known as syncretism. While syncretism varies greatly, certain cross-linguistic tendencies are apparent. Patterns where all syncretic forms share a morphological feature value (e.g., first person, or plural number) are most common cross-linguistically, and this preference is mirrored in results from learning experiments. While this suggests a general bias towards natural (featurally homogeneous) over unnatural (featurally heterogeneous) patterns, little is yet known about gradients in learnability and distributions of different kinds of unnatural patterns. In this paper we assess apparent cross-linguistic asymmetries between different types of unnatural patterns in person-number verbal agreement paradigms and test their learnability in an artificial language learning experiment. We find that the cross-linguistic recurrence of unnatural patterns of syncretism in person-number paradigms is proportional to the amount of shared feature values (i.e., semantic similarity) amongst the syncretic forms. Our experimental results further suggest that the learnability of syncretic patterns also mirrors the paradigm's degree of feature-value similarity. We propose that this gradient in learnability reflects a general bias towards similarity-based structure in morphological learning, which previous literature has shown to play a crucial role in word learning as well as in category and concept learning more generally. Rather than a dichotomous natural/unnatural distinction, our results thus support a more nuanced view of (un)naturalness in morphological paradigms and suggest that a preference for similarity-based structure during language learning might shape the worldwide transmission and typological distribution of patterns of syncretism.
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Affiliation(s)
- Carmen Saldana
- Department of Comparative Linguistics, University of Zurich, Zurich, Switzerland,Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Zurich, Switzerland,* Corresponding Author:
| | - Borja Herce
- Department of Comparative Linguistics, University of Zurich, Zurich, Switzerland,Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Zurich, Switzerland
| | - Balthasar Bickel
- Department of Comparative Linguistics, University of Zurich, Zurich, Switzerland,Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Zurich, Switzerland
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6
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Gyevnar B, Dagan G, Haley C, Guo S, Mollica F. Communicative Efficiency or Iconic Learning: Do Acquisition and Communicative Pressures Interact to Shape Colour- Naming Systems? ENTROPY (BASEL, SWITZERLAND) 2022; 24:1542. [PMID: 36359632 PMCID: PMC9689105 DOI: 10.3390/e24111542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Language evolution is driven by pressures for simplicity and informativity; however, the timescale on which these pressures operate is debated. Over several generations, learners' biases for simple and informative systems can guide language evolution. Over repeated instances of dyadic communication, the principle of least effort dictates that speakers should bias systems towards simplicity and listeners towards informativity, similarly guiding language evolution. At the same time, it has been argued that learners only provide a bias for simplicity and, thus, language users must provide a bias for informativity. To what extent do languages evolve during acquisition versus use? We address this question by formally defining and investigating the communicative efficiency of acquisition trajectories. We illustrate our approach using colour-naming systems, replicating the communicative efficiency model of Zaslavsky, Kemp, Regier & Tishby (2018, PNAS) and the acquisition model of Beekhuizen & Stevenson (2018, Cogn. Sci.). We find that to the extent that language is iconic, learning alone is sufficient to shape language evolution. Regarding colour-naming systems specifically, we find that incorporating learning biases into communicative efficiency accounts might explain how speakers and listeners trade off communicative effort.
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7
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Brochhagen T, Boleda G. When do languages use the same word for different meanings? The Goldilocks principle in colexification. Cognition 2022; 226:105179. [PMID: 35700657 DOI: 10.1016/j.cognition.2022.105179] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/13/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
Lexical ambiguity is pervasive in language, and often systematic. For instance, the Spanish word dedo can refer to a toe or a finger, that is, these two meanings colexify in Spanish; and they do so as well in over one hundred other languages. Previous work shows that related meanings are more likely to colexify. This is attributed to cognitive pressure towards simplicity in language, as it makes lexicons easier to learn and use. The present study examines the interplay between this pressure and the competing pressure for languages to support accurate information transfer. We hypothesize that colexification follows a Goldilocks principle that balances the two pressures: meanings are more likely to attach to the same word when they are related to an optimal degree-neither too much, nor too little. We find support for this principle in data from over 1200 languages and 1400 meanings. Our results thus suggest that universal forces shape the lexicons of natural languages. More broadly, they contribute to the growing body of evidence suggesting that languages evolve to strike a balance between competing functional and cognitive pressures.
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Affiliation(s)
- Thomas Brochhagen
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Roc Boronat, 138, 08018 Barcelona, Spain.
| | - Gemma Boleda
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Roc Boronat, 138, 08018 Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys, 23, 08010 Barcelona, Spain
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8
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Kirby S, Tamariz M. Cumulative cultural evolution, population structure and the origin of combinatoriality in human language. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200319. [PMID: 34894728 PMCID: PMC8666903 DOI: 10.1098/rstb.2020.0319] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2021] [Indexed: 11/28/2022] Open
Abstract
Language is the primary repository and mediator of human collective knowledge. A central question for evolutionary linguistics is the origin of the combinatorial structure of language (sometimes referred to as duality of patterning), one of language's basic design features. Emerging sign languages provide a promising arena to study the emergence of language properties. Many, but not all such sign languages exhibit combinatoriality, which generates testable hypotheses about its source. We hypothesize that combinatoriality is the inevitable result of learning biases in cultural transmission, and that population structure explains differences across languages. We construct an agent-based model with population turnover. Bayesian learning agents with a prior preference for compressible languages (modelling a pressure for language learnability) communicate in pairs under pressure to reduce ambiguity. We include two transmission conditions: agents learn the language either from the oldest agent or from an agent in the middle of their lifespan. Results suggest that (1) combinatoriality emerges during iterated cultural transmission under concurrent pressures for simplicity and expressivity and (2) population dynamics affect the rate of evolution, which is faster when agents learn from other learners than when they learn from old individuals. This may explain its absence in some emerging sign languages. We discuss the consequences of this finding for cultural evolution, highlighting the interplay of population-level, functional and cognitive factors. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'.
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Affiliation(s)
- Simon Kirby
- Centre for Language Evolution, University of Edinburgh, Edinburgh, UK
| | - Monica Tamariz
- Department of Psychology, Heriot-Watt University, Edinburgh, UK
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9
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The forms and meanings of grammatical markers support efficient communication. Proc Natl Acad Sci U S A 2021; 118:2025993118. [PMID: 34873051 DOI: 10.1073/pnas.2025993118] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 11/18/2022] Open
Abstract
Functionalist accounts of language suggest that forms are paired with meanings in ways that support efficient communication. Previous work on grammatical marking suggests that word forms have lengths that enable efficient production, and work on the semantic typology of the lexicon suggests that word meanings represent efficient partitions of semantic space. Here we establish a theoretical link between these two lines of work and present an information-theoretic analysis that captures how communicative pressures influence both form and meaning. We apply our approach to the grammatical features of number, tense, and evidentiality and show that the approach explains both which systems of feature values are attested across languages and the relative lengths of the forms for those feature values. Our approach shows that general information-theoretic principles can capture variation in both form and meaning across languages.
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10
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Karjus A, Blythe RA, Kirby S, Wang T, Smith K. Conceptual Similarity and Communicative Need Shape Colexification: An Experimental Study. Cogn Sci 2021; 45:e13035. [PMID: 34491584 PMCID: PMC9285023 DOI: 10.1111/cogs.13035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/18/2021] [Accepted: 07/19/2021] [Indexed: 11/28/2022]
Abstract
Colexification refers to the phenomenon of multiple meanings sharing one word in a language. Cross‐linguistic lexification patterns have been shown to be largely predictable, as similar concepts are often colexified. We test a recent claim that, beyond this general tendency, communicative needs play an important role in shaping colexification patterns. We approach this question by means of a series of human experiments, using an artificial language communication game paradigm. Our results across four experiments match the previous cross‐linguistic findings: all other things being equal, speakers do prefer to colexify similar concepts. However, we also find evidence supporting the communicative need hypothesis: when faced with a frequent need to distinguish similar pairs of meanings, speakadjust their colexification preferences to maintain communicative efficiency and avoid colexifying those similar meanings which need to be distinguished in communication. This research provides further evidence to support the argument that languages are shaped by the needs and preferences of their speakers.
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Affiliation(s)
- Andres Karjus
- ERA Chair for Cultural Data Analytics, Tallinn University.,School of Humanities, Tallinn University.,Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh
| | - Richard A Blythe
- Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh.,School of Physics and Astronomy, University of Edinburgh
| | - Simon Kirby
- Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh
| | | | - Kenny Smith
- Centre for Language Evolution, School of Philosophy, Psychology and Language Sciences, University of Edinburgh
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11
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Chaabouni R, Kharitonov E, Dupoux E, Baroni M. Communicating artificial neural networks develop efficient color-naming systems. Proc Natl Acad Sci U S A 2021; 118:e2016569118. [PMID: 33723064 PMCID: PMC8000426 DOI: 10.1073/pnas.2016569118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Words categorize the semantic fields they refer to in ways that maximize communication accuracy while minimizing complexity. Focusing on the well-studied color domain, we show that artificial neural networks trained with deep-learning techniques to play a discrimination game develop communication systems whose distribution on the accuracy/complexity plane closely matches that of human languages. The observed variation among emergent color-naming systems is explained by different degrees of discriminative need, of the sort that might also characterize different human communities. Like human languages, emergent systems show a preference for relatively low-complexity solutions, even at the cost of imperfect communication. We demonstrate next that the nature of the emergent systems crucially depends on communication being discrete (as is human word usage). When continuous message passing is allowed, emergent systems become more complex and eventually less efficient. Our study suggests that efficient semantic categorization is a general property of discrete communication systems, not limited to human language. It suggests moreover that it is exactly the discrete nature of such systems that, acting as a bottleneck, pushes them toward low complexity and optimal efficiency.
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Affiliation(s)
- Rahma Chaabouni
- Facebook AI Research, 75002 Paris, France;
- Cognitive Machine Learning, ENS - EHESS - PSL Research University - CNRS - INRIA, 75012 Paris, France
| | | | - Emmanuel Dupoux
- Facebook AI Research, 75002 Paris, France
- Cognitive Machine Learning, ENS - EHESS - PSL Research University - CNRS - INRIA, 75012 Paris, France
| | - Marco Baroni
- Facebook AI Research, 75002 Paris, France
- Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain
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12
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Ota M, San José A, Smith K. The emergence of word-internal repetition through iterated learning: Explaining the mismatch between learning biases and language design. Cognition 2021; 210:104585. [PMID: 33465675 DOI: 10.1016/j.cognition.2021.104585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 12/11/2020] [Accepted: 12/16/2020] [Indexed: 10/22/2022]
Abstract
The idea that natural language is shaped by biases in learning plays a key role in our understanding of how human language is structured, but its corollary that there should be a correspondence between typological generalisations and ease of acquisition is not always supported. For example, natural languages tend to avoid close repetitions of consonants within a word, but developmental evidence suggests that, if anything, words containing sound repetitions are more, not less, likely to be acquired than those without. In this study, we use word-internal repetition as a test case to provide a cultural evolutionary explanation of when and how learning biases impact on language design. Two artificial language experiments showed that adult speakers possess a bias for both consonant and vowel repetitions when learning novel words, but the effects of this bias were observable in language transmission only when there was a relatively high learning pressure on the lexicon. Based on these results, we argue that whether the design of a language reflects biases in learning depends on the relative strength of pressures from learnability and communication efficiency exerted on the linguistic system during cultural transmission.
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Affiliation(s)
| | - Aitor San José
- Max Planck Institute for Psycholinguistics, The Netherlands; International Max Planck Research School for Language Sciences, The Netherlands
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13
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Nagy DG, Török B, Orbán G. Optimal forgetting: Semantic compression of episodic memories. PLoS Comput Biol 2020; 16:e1008367. [PMID: 33057380 PMCID: PMC7591090 DOI: 10.1371/journal.pcbi.1008367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 10/27/2020] [Accepted: 09/03/2020] [Indexed: 11/26/2022] Open
Abstract
It has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in qualitatively different distortions to those found in experiments with humans. We argue that the form of distortions is characteristic of relying on a generative model adapted to the environment for compression. We show that this semantic compression framework can provide a unifying explanation of a wide variety of memory phenomena. We harness recent advances in learning deep generative models, that yield powerful tools to approximate generative models of complex data. We use three datasets, chess games, natural text, and hand-drawn sketches, to demonstrate the effects of semantic compression on memory performance. Our model accounts for memory distortions related to domain expertise, gist-based distortions, contextual effects, and delayed recall.
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Affiliation(s)
- David G. Nagy
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary
- Institute of Physics, Eötvös Loránd University, Budapest, Hungary
| | - Balázs Török
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary
- Department of Cognitive Science, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gergő Orbán
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary
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14
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A reinforcement-learning approach to efficient communication. PLoS One 2020; 15:e0234894. [PMID: 32667959 PMCID: PMC7363069 DOI: 10.1371/journal.pone.0234894] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 06/04/2020] [Indexed: 11/19/2022] Open
Abstract
We present a multi-agent computational approach to partitioning semantic spaces using reinforcement-learning (RL). Two agents communicate using a finite linguistic vocabulary in order to convey a concept. This is tested in the color domain, and a natural reinforcement learning mechanism is shown to converge to a scheme that achieves a near-optimal trade-off of simplicity versus communication efficiency. Results are presented both on the communication efficiency as well as on analyses of the resulting partitions of the color space. The effect of varying environmental factors such as noise is also studied. These results suggest that RL offers a powerful and flexible computational framework that can contribute to the development of communication schemes for color names that are near-optimal in an information-theoretic sense and may shape color-naming systems across languages. Our approach is not specific to color and can be used to explore cross-language variation in other semantic domains.
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