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Cited by 28Embodied and Extended CognitionLanguage and cultural evolutionSyntax, Semantics, Linguistic Variation

Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference

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Taniguchi, Tadahiro·2024·Frontiers in Robotics and AI
DOI 10.3389/frobt.2024.1353870PMC PMC11300318
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Abstract

Understanding the emergence of symbol systems, especially language, requires the construction of a computational model that reproduces both the developmental learning process in everyday life and the evolutionary dynamics of symbol emergence throughout history. This study introduces the collective predictive coding (CPC) hypothesis, which emphasizes and models the interdependence between forming internal representations through physical interactions with the environment and sharing and utilizing meanings through social semiotic interactions within a symbol emergence system. The total system dynamics is theorized from the perspective of predictive coding . The hypothesis draws inspiration from computational studies grounded in probabilistic generative models and language games, including the Metropolis–Hastings naming game. Thus, playing such games among agents in a distributed manner can be interpreted as a decentralized Bayesian inference of representations shared by a multi-agent system. Moreover, this study explores the potential link between the CPC hypothesis and the free-energy principle, positing that symbol emergence adheres to the society-wide free-energy principle. Furthermore, this paper provides a new explanation for why large language models appear to possess knowledge about the world based on experience, even though they have neither sensory organs nor bodies. This paper reviews past approaches to symbol emergence systems, offers a comprehensive survey of related prior studies, and presents a discussion on CPC-based generalizations. Future challenges and potential cross-disciplinary research avenues are highlighted.

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  • Agents in the CPC framework are passive observers that infer representations from observations they receive, leaving the feedback pathway from individual creative acts to social representation formation unaddressed.

    “Furthermore, agents in this framework are passive observers that infer representations from observations they receive , leaving the feedback pathway from individual creative acts to social representation formation unaddressed.”

    From Social Reality Construction via Active Inference: Modeling the Dialectic of Conformity and Creativity (2026) · cites this paper as Taniguchi, 2024

    Section 3.2 says agents create and share external representations: one agent utters a name, another accepts or rejects it, and acceptance updates that agent’s internal parameters. Section 4.3 likewise describes autonomous decisions to adopt or reject signs as part of social representation learning. That is contrary to describing CPC agents as passive observers with no pathway from individual acts to shared representations. (LLM, conf=0.89)

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  • The Collective Predictive Coding (CPC) hypothesis extends predictive coding and the free energy principle to the societal scale, offering a theoretical account of how shared representations such as symbol systems and social norms emerge through communicative interaction.

    “The Collective Predictive Coding (CPC) hypothesis (Taniguchi, 2024) extends predictive coding and the free energy principle to the societal scale, offering a theoretical account of how shared representations such as symbol systems and social norms emerge through communicative interaction.”

    From Social Reality Construction via Active Inference: Modeling the Dialectic of Conformity and Creativity (2026) · cites this paper as Taniguchi, 2024

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • CPC treats social representations as external shared variables that connect all agents through a joint generative model.

    “However, CPC treats social representations as external shared variables that connect all agents through a joint generative model.”

    From Social Reality Construction via Active Inference: Modeling the Dialectic of Conformity and Creativity (2026) · cites this paper as Taniguchi, 2024

  • Under CPC, social representations are formed through decentralized Bayesian inference.

    “Under CPC, social representations are formed through decentralized Bayesian inference.”

    From Social Reality Construction via Active Inference: Modeling the Dialectic of Conformity and Creativity (2026) · cites this paper as Taniguchi, 2024

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Research works printed but not linked: 111

  1. Akbari H. Yuan L. Qian R. Chuang W. H. Chang S.-F. Cui Y. (2021). Vatt: transformers for multimodal self-supervised learning from raw video, audio and text. Adv. Neural Inf. Process. Syst. 34, 24206– 24221.
  2. Althaus N. Westermann G. (2016). Labels constructively shape object categories in 10-month-old infants. J. Exp. child Psychol. 151, 5– 17. 10.1016/j.jecp.2015.11.013 26778468 · DOI
  3. Ando Y. Nakamura T. Nagai T. (2013). “ Formation of hierarchical object concept using hierarchical latent dirichlet allocation,” in IEEE/RSJ international conference on intelligent robots and systems (IROS), 2272– 2279.
  4. Araki T. Nakamura T. Nagai T. Nagasaka S. Taniguchi T. Iwahashi N. (2012). “ Online learning of concepts and words using multimodal LDA and hierarchical Pitman-Yor Language Model,” in IEEE/RSJ international conference on intelligent robots and systems (IROS) (IEEE), 1623– 1630.
  5. Barsalou L. W. (1999). Perceptual symbol system. Behav. Brain Sci. 22, 277– 660. 11301525
  6. Bengio Y. (2017). The consciousness prior. arXiv preprint arXiv:1709.08568. · arXiv
  7. Bishop C. M. (2006). Pattern recognition and machine learning. New York, NY, USA: Springer.
  8. Bleys J. (2015) “ Language strategies for the domain of colour,” in Computational models of language evolution. Berlin: Language Science Press.
  9. Bornstein M. H. Arterberry M. E. (2010). The development of object categorization in young children: hierarchical inclusiveness, age, perceptual attribute, and group versus individual analyses. Dev. Psychol. 46, 350– 365. 10.1037/a0018411 20210495 PMC2856652 · DOI
  10. Brighton H. (2002). Compositional syntax from cultural transmission. Artif. Life 8, 25– 54. 10.1162/106454602753694756 12020420 · DOI
  11. Brown T. B. Mann B. Ryder N. Subbiah M. Kaplan J. Dhariwal P. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. · arXiv
  12. Buşoniu L. Babuška R. De Schutter B. (2010). Multi-agent reinforcement learning: an overview. Innovations multi-agent Syst. applications- 1, 183– 221. 10.1007/978-3-642-14435-6_7 · DOI
  13. Cangelosi A. Greco A. Harnad S. (2000). From robotic toil to symbolic theft: grounding transfer from entry-level to higher-level categories. Connect. Sci. 12, 143– 162. 10.1080/09540090050129763 · DOI
  14. Cangelosi A. Schlesinger M. (2014). Developmental robotics: from babies to robots. The MIT Press.
  15. Cangelosi A. Schlesinger M. (2015). Developmental robotics: from babies to robots. MIT press.
  16. Chandler D. (2002). Semiotics the basics. London, UK: Routledge.
  17. Chen X. He K. (2021). “ Exploring simple siamese representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 15750– 15758.
  18. Ciria A. Schillaci G. Pezzulo G. Hafner V. V. Lara B. (2021). Predictive processing in cognitive robotics: a review. Neural Comput. 33, 1402– 1432. 10.1162/neco_a_01383 34496394 · DOI · Paper page
  19. Csibra G. Gergely G. (2009). Natural pedagogy. Trends Cognitive Sci. 13, 148– 153. 10.1016/j.tics.2009.01.005 19285912 · DOI · Paper page
  20. Doya K. Ishii S. Pouget A. Rao R. P. N. (2007). Bayesian brain: probabilistic approaches to neural coding (The MIT Press).
  21. Foerster J. Assael I. A. De Freitas N. Whiteson S. (2016a). Learning to communicate with deep multi-agent reinforcement learning. Adv. Neural Inf. Process. Syst. 29.
  22. Foerster J. N. Assael Y. M. de Freitas N. Whiteson S. (2016b). “ Learning to communicate with deep multi-agent reinforcement learning,” in Advances in neural information processing systems, 2145– 2153.
  23. Friston K. (2013). Life as we know it. J. R. Soc. Interface 10, 20130475. 10.1098/rsif.2013.0475 23825119 PMC3730701 · DOI · Paper page
  24. Friston K. (2019). A free energy principle for a particular physics. arXiv preprint arXiv:1906.10184. 35588546 · arXiv
  25. Friston K. Moran R. J. Nagai Y. Taniguchi T. Gomi H. Tenenbaum J. (2021). World model learning and inference. Neural Netw. 144, 573– 590. 10.1016/j.neunet.2021.09.011 34634605 · DOI
  26. Garg S. Sünderhauf N. Dayoub F. Morrison D. Cosgun A. Carneiro G. (2020). Semantics for robotic mapping, perception and interaction: a survey. Found. Trends Robotics 8, 1– 224. 10.1561/2300000059 · DOI
  27. Gliozzi V. Mayor J. Hu J.-F. Plunkett K. (2009). Labels as features (not names) for infant categorization: a neurocomputational approach. Cognitive Sci. 33, 709– 738. 10.1111/j.1551-6709.2009.01026.x 21585482 · DOI
  28. Goldwater S. Griffiths T. L. Johnson M. (2009). A Bayesian framework for word segmentation: exploring the effects of context. Cognition 112, 21– 54. 10.1016/j.cognition.2009.03.008 19409539 · DOI
  29. Griffiths T. L. Kalish M. L. (2007). Language evolution by iterated learning with bayesian agents. Cognitive Sci. 31, 441– 480. 10.1080/15326900701326576 21635304 · DOI
  30. Ha D. Schmidhuber J. (2018). World models. arXiv preprint arXiv:1803.10122. · arXiv
  31. Hagiwara Y. Kobayashi H. Taniguchi A. Taniguchi T. (2019). Symbol emergence as an interpersonal multimodal categorization. Front. Robotics AI 6, 134. 10.3389/frobt.2019.00134 PMC7805687 33501149 · DOI · Paper page
  32. Hastings W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97– 109. 10.2307/2334940 · DOI
  33. Havrylov S. Titov I. (2017). “ Emergence of language with multi-agent games: learning to communicate with sequences of symbols,”. Advances in neural information processing systems. Editors Guyon I. Luxburg U. V. Bengio S. Wallach H. Fergus R. Vishwanathan S. (Curran Associates, Inc.), 30.
  34. Hohwy J. (2013). The predictive mind. Oxford University Press.
  35. Inukai J. Taniguchi T. Taniguchi A. Hagiwara Y. (2023). Recursive metropolis-hastings naming game: symbol emergence in a multi-agent system based on probabilistic generative models. Front. Artif. Intell. 6, 1229127. 10.3389/frai.2023.1229127 37920571 PMC10619661 · DOI · Paper page
  36. Iqbal S. Sha F. (2019). “ Actor-attention-critic for multi-agent reinforcement learning,” in International conference on machine learning, 2961– 2970.
  37. Jiang J. Lu Z. (2018). Learning attentional communication for multi-agent cooperation. Adv. Neural Inf. Process. Syst. 31.
  38. Junge C. van Rooijen R. Raijmakers M. (2018). Distributional information shapes infants’ categorization of objects. Infancy 23, 917– 926. 10.1111/infa.12258 · DOI
  39. Kalantari S. Nazemi E. Masoumi B. (2020). Emergence phenomena in self-organizing systems: a systematic literature review of concepts, researches, and future prospects. J. Organ. Comput. Electron. Commer. 30, 224– 265. 10.1080/10919392.2020.1748977 · DOI
  40. Kastel N. Hesp C. Ridderinkhof K. R. Friston K. J. (2022). Small steps for mankind: modeling the emergence of cumulative culture from joint active inference communication. Front. Neurorobot. 16, 944986. 10.3389/fnbot.2022.944986 36699948 PMC9868743 · DOI
  41. Kaufmann R. Gupta P. Taylor J. (2021). An active inference model of collective intelligence. Entropy 23, 830. 10.3390/e23070830 34210008 PMC8306784 · DOI · Paper page
  42. Kawakita G. Zeleznikow-Johnston A. Tsuchiya N. Oizumi M. (2023). Comparing color similarity structures between humans and LLMs via unsupervised alignment PMC11237038 38987348
  43. Kilinc O. Montana G. (2018). Multi-agent deep reinforcement learning with extremely noisy observations. Adv. Neural Inf. Process. Syst. Deep Reinf. Learn. Workshop.
  44. Kim D. Moon S. Hostallero D. Kang W. J. Lee T. Son K. (2019). “ Learning to schedule communication in multi-agent reinforcement learning,” in International conference on representation learning.
  45. Kim W. Park J. Sung Y. (2021). “ Communication in multi-agent reinforcement learning: intention sharing,” in International conference on learning representations.
  46. Kirby S. (2001). Spontaneous evolution of linguistic structure-an iterated learning model of the emergence of regularity and irregularity. IEEE Trans. Evol. Comput. 5, 102– 110. 10.1109/4235.918430 · DOI
  47. Kirby S. (2002). Learning, bottlenecks and the evolution of recursive syntax, 173, 204. 10.1017/cbo9780511486524.006 · DOI
  48. Kirby S. Cornish H. Smith K. (2008). Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human language. Proc. Natl. Acad. Sci. 105, 10681– 10686. 10.1073/pnas.0707835105 18667697 PMC2504810 · DOI · Paper page
  49. Kirby S. Hurford J. R. (2002). The emergence of linguistic structure: an overview of the iterated learning model. Simulating Evol. Lang., 121– 147. 10.1007/978-1-4471-0663-0_6 · DOI
  50. Kirby S. Tamariz M. Cornish H. Smith K. (2015). Compression and communication in the cultural evolution of linguistic structure. Cognition 141, 87– 102. 10.1016/j.cognition.2015.03.016 25966840 · DOI · Paper page
  51. Kostavelis I. Gasteratos A. (2015). Semantic mapping for mobile robotics tasks: a survey. Robotics Aut. Syst. 66, 86– 103. 10.1016/j.robot.2014.12.006 · DOI
  52. Lake B. M. Ullman T. D. Tenenbaum J. B. Gershman S. J. (2017). Building machines that learn and think like people. Behav. Brain Sci. 40, e253. 10.1017/s0140525x16001837 27881212 · DOI · Paper page
  53. Lazaridou A. Baroni M. (2020). Emergent multi-agent communication in the deep learning era. arXiv preprint arXiv:2006.02419. · arXiv
  54. Lazaridou A. Peysakhovich A. Baroni M. (2017a). “ Multi-agent cooperation and the emergence of (natural) language,” in The international conference on learning representations.
  55. Lazaridou A. Peysakhovich A. Baroni M. (2017b). “ Multi-agent cooperation and the emergence of (natural) language,” in International conference on learning representations.
  56. Levchuk G. Pattipati K. Serfaty D. Fouse A. McCormack R. (2019). “ Active inference in multiagent systems: context-Driven collaboration and decentralized Purpose-Driven team adaptation,” in Artificial intelligence for the internet of everything. Editors Lawless W. Mittu R. Sofge D. Moskowitz I. S. Russell S. (Academic Press), 67– 85.
  57. Levine S. (2018). Reinforcement learning and control as probabilistic inference: Tutorial and review. Report.
  58. Lillicrap T. P. Hunt J. J. Pritzel A. Heess N. Erez T. Tassa Y. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971. · arXiv
  59. Lowe R. Wu Y. I. Tamar A. Harb J. Pieter Abbeel O. Mordatch I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Adv. Neural Inf. Process. Syst. 30.
  60. Mahowald K. Ivanova A. A. Blank I. A. Kanwisher N. Tenenbaum J. B. Fedorenko E. (2023). Dissociating language and thought in large language models: a cognitive perspective. arXiv preprint arXiv:2301.06627. PMC11416727 38508911 · arXiv
  61. Matuszek C. (2018). “ Grounded language learning: where robotics and nlp meet,” in Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18 (International Joint Conferences on Artificial Intelligence Organization), 5687– 5691. 10.24963/ijcai.2018/810 · DOI
  62. Mochihashi D. Yamada T. Ueda N. (2009). “ Bayesian unsupervised word segmentation with nested Pitman-Yor language modeling,” in Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP (ACL-IJCNLP), 100– 108.
  63. Moulin-Frier C. Diard J. Schwartz J.-L. Bessière P. (2015). Cosmo (“communicating about objects using sensory–motor operations”): a bayesian modeling framework for studying speech communication and the emergence of phonological systems. J. Phonetics 53, 5– 41. 10.1016/j.wocn.2015.06.001 · DOI
  64. Nakamura T. Ando Y. Nagai T. Kaneko M. (2015). “ Concept formation by robots using an infinite mixture of models,” in IEEE/RSJ international conference on intelligent robots and systems (IROS).
  65. Nakamura T. Nagai T. Funakoshi K. Nagasaka S. Taniguchi T. Iwahashi N. (2014). “ Mutual learning of an object concept and language model based on MLDA and NPYLM,” in IEEE/RSJ international conference on intelligent robots and systems, 600– 607.
  66. Nakamura T. Nagai T. Iwahashi N. (2009). “ Grounding of word meanings in multimodal concepts using LDA,” in IEEE/RSJ international conference on intelligent robots and systems, 3943– 3948.
  67. Nakamura T. Nagai T. Iwahashi N. (2011a). “ Bag of multimodal lda models for concept formation,” in 2011 IEEE international conference on robotics and automation, 6233– 6238.
  68. Nakamura T. Nagai T. Iwahashi N. (2011b). “ Multimodal categorization by hierarchical Dirichlet process,” in IEEE/RSJ international conference on intelligent robots and systems, 1520– 1525.
  69. Nakamura T. Nagai T. Iwahashi N. (2012). “ Bag of multimodal hierarchical dirichlet processes: model of complex conceptual structure for intelligent robots,” in 2012 IEEE/RSJ international conference on intelligent robots and systems (IEEE), 3818– 3823.
  70. Nakamura T. Nagai T. Taniguchi T. (2018). Serket: an architecture for connecting stochastic models to realize a large-scale cognitive model. Front. Neurorobotics 12, 25. 10.3389/fnbot.2018.00025 PMC6028621 29997493 · DOI
  71. Nakamura T. Taniguchi A. Taniguchi T. (2023b). Control as probabilistic inference as an emergent communication mechanism in multi-agent reinforcement learning. arXiv preprint arXiv:2307.05004. · arXiv
  72. Newell A. (1980). Physical symbol systems. Cognitive Sci. 4, 135– 183. 10.1016/s0364-0213(80)80015-2 · DOI · Paper page
  73. Okada M. Taniguchi T. (2020). “ Variational inference mpc for bayesian model-based reinforcement learning,” in Conference on robot learning (PMLR), 258– 272.
  74. Okumura R. Taniguchi T. Hagiwara Y. Taniguchi A. (2023). Metropolis-hastings algorithm in joint-attention naming game: experimental semiotics study. Front. Artif. Intell. 6, 1235231. 10.3389/frai.2023.1235231 38116389 PMC10728479 · DOI
  75. Oudeyer P.-Y. (2005). The self-organization of speech sounds. J. Theor. Biol. 233, 435– 449. 10.1016/j.jtbi.2004.10.025 15652151 · DOI
  76. Pandey G. Dukkipati A. (2017). “ Variational methods for conditional multimodal deep learning,” in 2017 international joint conference on neural networks (IJCNN) (IEEE), 308– 315.
  77. Parr T. Pezzulo G. Friston K. J. (2022). “ Active inference: the free energy principle,” in Mind, brain, and behavior. English Edition) (The MIT Press).
  78. Quinn P. C. Eimas P. D. Tarr M. J. (2001). Perceptual categorization of cat and dog silhouettes by 3-to 4-month-old infants. J. Exp. child Psychol. 79, 78– 94. 10.1006/jecp.2000.2609 11292312 · DOI
  79. Rendall D. Owren M. J. Ryan M. J. (2009). What do animal signals mean? Anim. Behav. 78, 233– 240. 10.1016/j.anbehav.2009.06.007 · DOI
  80. Roy D. K. Pentland A. P. (2002). Learning words from sights and sounds: a computational model. Cognitive Sci. 26, 113– 146. 10.1207/s15516709cog2601_4 · DOI · Paper page
  81. Scott-Phillips T. C. Kirby S. (2010). Language evolution in the laboratory. Trends Cognitive Sci. 14, 411– 417. 10.1016/j.tics.2010.06.006 20675183 · DOI
  82. Shannon C. E. (1948). A mathematical theory of communication. Bell Syst. Tech. J. 27, 379– 423. 10.1002/j.1538-7305.1948.tb01338.x · DOI · Paper page
  83. Spranger M. (2011). The evolution of grounded spatial language. Brussels: Vrije Universiteit Brussel. Ph.D. thesis.
  84. Spranger M. (2015). “ The evolution of grounded spatial language,” in Computational models of language evolution (Berlin: Language Science Press).
  85. Spranger M. Loetzsch M. Steels L. (2012). “ A perceptual system for language game experiments,” in Language grounding in robots. Editors Steels L. Hild M. (Springer), 89– 110.
  86. Steels L. (1995). A self-organizing spatial vocabulary. Artif. Life 2, 319– 332. 10.1162/artl.1995.2.319 8925502 · DOI
  87. Steels L. (2015). The Talking Heads experiment: origins of words and meanings, 1. Berlin, DE: Language Science Press. 10.26530/oapen_559870 · DOI
  88. Steels L. Belpaeme T. (2005). Coordinating perceptually grounded categories through language: a case study for colour. Behav. Brain Sci. 28, 469– 489. 10.1017/S0140525X05000087 16209771 · DOI · Paper page
  89. Steels L. Kaplan F. (2000). AIBO’s first words: the social learning of language and meaning. Evol. Commun. 4, 3– 32. 10.1075/eoc.4.1.03ste · DOI
  90. Steels L. Loetzsch M. (2008). “ Perspective alignment in spatial language,” in Spatial Language and dialogue. Editors Coventry K. R. Tenbrink T. Bateman J. A. (Oxford University Press).
  91. Sukhbaatar S. Fergus R. (2016). Learning multiagent communication with backpropagation. Adv. Neural Inf. Process. Syst. 29.
  92. Suzuki M. Nakayama K. Matsuo Y. (2016). “ Joint multimodal learning with deep generative models,” in International conference on learning representations.
  93. Taniguchi A. Hagiwara Y. Taniguchi T. Inamura T. (2017a). “ Online spatial concept and lexical acquisition with simultaneous localization and mapping,” in IEEE/RSJ international conference on intelligent robots and systems, 811– 818.
  94. Taniguchi A. Hagiwara Y. Taniguchi T. Inamura T. (2017b). “ Online spatial concept and lexical acquisition with simultaneous localization and mapping,” in IEEE/RSJ international conference on intelligent robots and systems, 811– 818.
  95. Taniguchi A. Hagiwara Y. Taniguchi T. Inamura T. (2020a). Improved and scalable online learning of spatial concepts and language models with mapping. Aut. Robots 44, 927– 946. 10.1007/s10514-020-09905-0 · DOI
  96. Taniguchi A. Taniguchi T. Inamura T. (2018a). Unsupervised spatial lexical acquisition by updating a language model with place clues. Robotics Aut. Syst. 99, 166– 180. 10.1016/j.robot.2017.10.013 · DOI
  97. Taniguchi T. (2024). Dynamics of language and cognition based on collective predictive coding: towards a new generation of symbol emergence in robotics. Cognitive Stud. Bull. Jpn. Cognitive Sci. Soc. 31, 186– 204. 10.11225/cs.2023.064 · DOI
  98. Taniguchi T. Murata S. Suzuki M. Ognibene D. Lanillos P. Ugur E. (2023a). World models and predictive coding for cognitive and developmental robotics: frontiers and challenges. Adv. Robot. 37, 780– 806. 10.1080/01691864.2023.2225232 · DOI
  99. Taniguchi T. Nagai T. Nakamura T. Iwahashi N. Ogata T. Asoh H. (2016a). Symbol emergence in robotics: a survey. Adv. Robot. 30, 706– 728. 10.1080/01691864.2016.1164622 · DOI · Paper page
  100. Taniguchi T. Nagai T. Nakamura T. Iwahashi N. Ogata T. Asoh H. (2016b). Symbol emergence in robotics: a survey. Adv. Robot. 30, 706– 728. 10.1080/01691864.2016.1164622 · DOI · Paper page
  101. Taniguchi T. Nagai T. Nakamura T. Iwahashi N. Ogata T. Asoh H. (2016c). Symbol emergence in robotics: a survey. Adv. Robot. 30, 706– 728. 10.1080/01691864.2016.1164622 · DOI · Paper page
  102. Taniguchi T. Nakamura T. Suzuki M. Kuniyasu R. Hayashi K. Taniguchi A. (2020b). Neuro-serket: development of integrative cognitive system through the composition of deep probabilistic generative models. New Gener. Comput. 38, 23– 48. 10.1007/s00354-019-00084-w · DOI
  103. Taniguchi T. Nakashima R. Liu H. Nagasaka S. (2016d). Double articulation analyzer with deep sparse autoencoder for unsupervised word discovery from speech signals. Adv. Robot. 30, 770– 783. 10.1080/01691864.2016.1159981 · DOI
  104. Taniguchi T. Nakashima R. Nagasaka S. (2015). Nonparametic bayesian double articulation analyzer for direct language acquisition from continuous speech signals. CoRR.
  105. Taniguchi T. Yoshida Y. Matsui Y. Hoang N. L. Taniguchi A. Hagiwara Y. (2023b). Emergent communication through metropolis-hastings naming game with deep generative models. Adv. Robot. 37, 1266– 1282. 10.1080/01691864.2023.2260856 PMC10864618 38356917 · DOI · Paper page
  106. Ueda R. Taniguchi T. (2024). “ Lewis’s signaling game as beta-VAE for natural word lengths and segments,” in International conference on learning representations.
  107. Vaswani A. Uszkoreit J. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst. (NIPS).
  108. Vogt P. (2002). The physical symbol grounding problem. Cognitive Syst. Res. 3, 429– 457. 10.1016/S1389-0417(02)00051-7 · DOI
  109. Vogt P. (2005). The emergence of compositional structures in perceptually grounded language games. Artif. Intell. 167, 206– 242. 10.1016/j.artint.2005.04.010 · DOI
  110. Von Uexküll J. (1992). A stroll through the worlds of animals and men: a picture book of invisible worlds. Semiotica 89, 319– 391. 10.1515/semi.1992.89.4.319 · DOI
  111. Wu M. Goodman N. (2018). “ Multimodal generative models for scalable weakly-supervised learning,” in Advances in neural information processing systems, 5575– 5585.

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Authors
Taniguchi, T.
Affiliations
Ritsumeikan University (JP)
Corresponding author
Taniguchi, Tadahiro
ORCID
Taniguchi, Tadahiro
Venue
Frontiers in Robotics and AI
Publisher
Frontiers Media SA
Published
2024-07-23
Volume
11
Pages
1353870-1353870
ISSN
2296-9144 (ISSN-L)
DOI
10.3389/frobt.2024.1353870
PMC
PMC11300318
OpenAlex
W4400929897
Cited by
28
References
13
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2026: 13 · 2025: 8 · 2024: 4 · 2023: 3
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6.03
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en
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  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Funders
Japan Society for the Promotion of Science (21H04904 23H04835)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Social Sciences › Cultural Studies › Language and cultural evolution
  • Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
Keywords
symbol emergence, language emergence, predictive coding, free energy principle, naming game, Metropolis-Hastings algorithm, multi-agent systems, internal representations, large language models, generative models, language evolution

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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