Symbol Emergence in Cognitive Developmental Systems: A Survey
Open accessAbstract
Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, second, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems.
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Attempts to train models on multimodal sensorimotor data have yielded some success, with agents able to associate the sensory information of an object such as a cup with the signals that represent it.
“Attempts to train models on multimodal sensorimotor data have yielded some success, with agents able to associate the sensory information of an object such as a cup with the signals that represent it [7,8,9].”
From Compression, The Fermi Paradox and Artificial Super-Intelligence (2022) · cites this paper as [7]
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Lay, F. S., Bauer, A. S., Albu‐Schäffer, A., Stulp, F., & Leidner, D. S. (2021) · Advanced Robotics · doi:10.1080/01691864.2021.2007169 · Cited by 5 · see citing works
Nagano, M., Nakamura, T., Nagai, T., Mochihashi, D., & Kobayashi, I. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.903450 · Cited by 5 · see citing works
Self-citationYou, Z., Ebara, H., Nakamura, T., Taniguchi, A., & Taniguchi, T. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644473 · Cited by 4 · see citing works
Self-citationTaniguchi, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35382-6_2 · Cited by 4 · see citing works
Self-citationHigh-level features for resource economy and fast learning in skill transfer
Ahmetoğlu, A., Uğur, E., Asada, M., & Öztop, E. (2022) · Advanced Robotics · doi:10.1080/01691864.2021.2019613 · Cited by 4 · see citing works
Zubek, J., Korbak, T., & Rączaszek‐Leonardi, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11048-y · Cited by 4 · see citing works
Computable Artificial General IntelligencePreprint
Bennett, M. T. (2022) · doi:10.36227/techrxiv.19740190 · Cited by 4 · see citing works
Learning Multi-Object Symbols for Manipulation with Attentive Deep Effect PredictorsPreprint
Ahmetoğlu, A., Öztop, E., & Uğur, E. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2208.01021 · Cited by 4 · see citing works
Compositionality and Generalization in Emergent Communication Using Metropolis-Hastings Naming Game
Hoang, N. L., Matsui, Y., Hagiwara, Y., Taniguchi, A., & Taniguchi, T. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644635 · Cited by 3 · see citing works
Self-citationTaniguchi, T. (2025) · Cognitive Neuroscience · doi:10.1080/17588928.2025.2518942 · Cited by 3 · see citing works
Self-citationHigh-level representations through unconstrained sensorimotor learning
Ozturkcu, O. B., Uğur, E., & Öztop, E. (2020) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl-epirob48136.2020.9278100 · Cited by 3 · see citing works
A Review of Robotics Research in South Africa
Boje, E., Christopher, R., Fernandes, J., Hepworth, J. H., Kuriakose, R. B., Kruger, K., Lorimer, T., Luwes, N. J., Mouton, H. D., Patel, A., et al. (2019) · R&D Journal · doi:10.17159/2309-8988/2019/v35a9 · Cited by 3 · see citing works
Self-citationPrasad, P. K., & Ertel, W. (2020) · International Conference on Robotics and Automation Engineering (ICRAE) · doi:10.1109/icrae50850.2020.9310835 · Cited by 2 · see citing works
Bootstrapping Concept Formation in Small Neural Networks
Tamošiūnaitė, M., Kulvičius, T., & Wörgötter, F. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3163022 · Cited by 2 · see citing works
Okada, M., Nagata, K., Watanabe, N., & Tada, M. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3262598 · Cited by 3 · see citing works
Kurshan, E. (2024) · International Journal of Semantic Computing · doi:10.1142/s1793351x24300073 · Cited by 3 · see citing works
Girgin, T., & Uğur, E. (2024) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2024.3520086 · Cited by 2 · see citing works
Learning, Development, and Emergence of Compositionality in Natural Language Processing
Maruyama, Y. (2021) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl49984.2021.9515636 · Cited by 2 · see citing works
Editorial: Machine Learning Methods for High-Level Cognitive Capabilities in Robotics
Taniguchi, T., Uğur, E., Ogata, T., Nagai, T., & Demiris, Y. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00083 · Cited by 2 · see citing works
Self-citationEditorial: Language and Robotics
Taniguchi, T., Horii, T., Hinaut, X., Spranger, M., Mochihashi, D., & Nagai, T. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.674832 · Cited by 2 · see citing works
Self-citationTowards Teachable Autonomous AgentsPreprint
Sigaud, O., Caselles-Dupré, H., Colas, C., Akakzia, A., Oudeyer, P., & Chétouani, M. (2021) · HAL · Cited by 2 · see citing works
Computable Artificial General IntelligencePreprint
Bennett, M. T. (2022) · doi:10.36227/techrxiv.19740190.v6 · Cited by 2 · see citing works
Supporting the quality of mathematics teaching through lead teachers
Motseki, P. D., Jojo, Z. M. M., & Gumbo, M. T. (2025) · African Journal of Teacher Education and Development · doi:10.4102/ajoted.v4i1.98 · Cited by 1 · see citing works
Understanding Symbols: Towards a Compact Vocabulary Learned from Robot Experience for Task Planning
Beraldo, G., Rasconi, R., & Oddi, A. (2025) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2025.105242 · Cited by 1 · see citing works
Evolution of a cognitive architecture on the basis of a functional core
Serov, A. (2022) · SN Applied Sciences · doi:10.1007/s42452-022-05195-6 · Cited by 1 · see citing works
Cross-Situational Word Learning in Disentangled Latent Space
Matsui, Y., Taniguchi, A., Hagiwara, Y., & Taniguchi, T. (2023) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl55364.2023.10364474 · Cited by 1 · see citing works
Self-citationRelational Concepts in Deep Reinforcement Learning: Emergence and Representation
Schnürer, T., Probst, M., & Groß, H. (2020)
Integrative Cognitive Systems for Language Understanding and Symbol Emergence in Robotics
Taniguchi, T. (2021) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.39.405
Self-citationNoguchi, W., Iizuka, H., & Yamamoto, M. (2021) · Advanced Robotics · doi:10.1080/01691864.2021.1993334
Editorial: Constructive approach to spatial cognition in intelligent robotics
Taniguchi, A., Spranger, M., Yamakawa, H., & Inamura, T. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1077891
Computable Artificial General IntelligencePreprint
Bennett, M. T. (2022) · doi:10.36227/techrxiv.19740190.v7
An Idiomatic Framework for Cognitive Robotics
Damgaard, M. R. (2022) · doi:10.54337/aau520799670
言語とコミュニケーションの創発に関する構成論的研究の展開Preprint
Ueda, R., Taniguchi, T., Suzuki, R., Ebara, H., Nakamura, T., Iwamura, I., & Hashimoto, T. (2023) · doi:10.31234/osf.io/rz5ng
Self-citationDeep Multi-Object Symbol Learning with Self-Attention Based Predictors
Ahmetoğlu, A., Öztop, E., & Uğur, E. (2023) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu59756.2023.10223865
Unsupervised Acquisition of Phonemes, Words, and Grammar from Continuous Speech Signals
Ochiai, S., Nagano, M., & Nakamura, T. (2024) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl61372.2024.10644607
Hirose, M., Nagano, M., & Taniguchi, T. (2025) · IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) · doi:10.1109/arso64737.2025.11124961
Self-citationWorking Memory and Self-Directed Inner Speech Enhance Multitask Generalization in Active Inference
Queißer, J. F., & Tani, J. (2025) · Neural Computation · doi:10.1162/neco.a.36
Continuous Symbol Emergence via Decentralized Bayesian Inference With Gaussian Processes
You, Z., Saito, I., Nagano, M., Nakamura, T., Taniguchi, A., & Taniguchi, T. (2026) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2026.3661019
Self-citationMH-MuG: Collaborative Music Generation Game Between AI Agents Toward Emergent Musical Creativity
Sakurai, K., Uenoyama, H., Taniguchi, A., & Taniguchi, T. (2026) · IEEE Access · doi:10.1109/access.2026.3666234
Self-citationToward Spatial Cognition and Semantic Understanding of Place in Robots
Taniguchi, A. (2026) · Journal of the Robotics Society of Japan · doi:10.7210/jrsj.44.468
- Authors
- Taniguchi, T., Piater, J., Worgotter, F., Ugur, E., Hoffmann, M., Jamone, L., Nagai, T., Rosman, B., Matsuka, T., Iwahashi, N., & Oztop, E.
- Affiliations
- Ritsumeikan University (JP)
- Universität Innsbruck (AT)
- Czech Technical University in Prague (CZ)
- Queen Mary University of London (GB)
- University of Electro-Communications (JP)
- University of the Witwatersrand (ZA)
- Chiba University (JP)
- Okayama Prefectural University (JP)
- ORCID
- Venue
- IEEE Transactions on Cognitive and Developmental Systems
- Published
- 2019-12
- Volume
- 11
- Issue
- 4
- Pages
- 494-516
- ISSN
- 2379-8920 (ISSN-L)
- 2379-8939
- OpenAlex
- W2963475757
- Cited by
- 92
- References
- 171
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 13 · 2023: 11 · 2022: 27 · 2021: 15 · 2020: 7 · 2019: 7 · 2018: 1
- FWCI
- 57.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Cognitive and Developmental Systems (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- eResearch@Ozyegin (submittedVersion, other-oa)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Grants-in-Aid for Young Scientists (15H05319)
- Grant-in-Aid for Scientific Research (18H03308)
- Ministry of Education, Culture, Sports, Science and Technology (16H06569)
- European Unions H2020 Research and Innovation Programme (731761 (IMAGINE))
- Grantová Agentura České Republiky (GA17-15697Y)
- European H2020 Programme (732266 (Plan4Act))
- Topics
- Social Sciences › Cultural Studies › Language and cultural evolution
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- symbol emergence, symbol grounding problem, semiotics, signs, cognitive development, cognitive systems, symbolic AI, human communication
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