# Symbol Emergence in Cognitive Developmental Systems: a Survey

arXiv: https://arxiv.org/abs/1801.08829 · HTML: https://mlsanity.com/paper/1801.08829/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2018.

## Built on by (most cited first, 30 of 39)

- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [Symbolic Behaviour in Artificial Intelligence](https://mlsanity.com/paper/2102.03406/) (2021)
- [Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models](https://mlsanity.com/paper/2205.12392/) (2022)
- [Generative Emergent Communication: Large Language Model is a Collective World Model](https://mlsanity.com/paper/2501.00226/) (2024)
- [Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference](https://mlsanity.com/paper/2109.07194/) (2021)
- [Symbol Emergence and The Solutions to Any Task](https://mlsanity.com/paper/2109.01281/) (2021)
- [Philosophical Specification of Empathetic Ethical Artificial Intelligence](https://mlsanity.com/paper/2107.10715/) (2021)
- [Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models](https://mlsanity.com/paper/2305.19761/) (2023)
- [Compression, The Fermi Paradox and Artificial Super-Intelligence](https://mlsanity.com/paper/2110.01835/) (2021)
- [Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding](https://mlsanity.com/paper/2603.18472/) (2026)
- [Computable Artificial General Intelligence](https://mlsanity.com/paper/2205.10513/) (2022)
- [Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis](https://mlsanity.com/paper/2309.07675/) (2023)
- [Hippocampal formation-inspired probabilistic generative model](https://mlsanity.com/paper/2103.07356/) (2021)
- [Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study](https://mlsanity.com/paper/2305.19936/) (2023)
- [Constructive Approach to Bidirectional Influence between Qualia Structure and Language Emergence](https://mlsanity.com/paper/2409.09413/) (2024)
- [A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots](https://mlsanity.com/paper/2103.08183/) (2021)
- [The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence](https://mlsanity.com/paper/2110.01831/) (2021)
- [Discovering Predictive Relational Object Symbols with Symbolic Attentive Layers](https://mlsanity.com/paper/2309.00889/) (2023)
- [Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI](https://mlsanity.com/paper/2104.11573/) (2021)
- [High-level Features for Resource Economy and Fast Learning in Skill Transfer](https://mlsanity.com/paper/2106.10354/) (2021)
- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)
- [Models of symbol emergence in communication: a conceptual review and a guide for avoiding local minima](https://mlsanity.com/paper/2303.04544/) (2023)
- [Emergence of Symbols in Neural Networks for Semantic Understanding and Communication](https://mlsanity.com/paper/2304.06377/) (2023)
- [Learning Type-Generalized Actions for Symbolic Planning](https://mlsanity.com/paper/2308.04867/) (2023)
- [Multi-Object Graph Affordance Network: Goal-Oriented Planning through Learned Compound Object Affordances](https://mlsanity.com/paper/2309.10426/) (2023)
- [System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems](https://mlsanity.com/paper/2503.06138/) (2025)
- [Bootstrapping Concept Formation in Small Neural Networks](https://mlsanity.com/paper/2110.13665/) (2021)
- [Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping](https://mlsanity.com/paper/2203.10820/) (2022)
- [Bridging Neural and Symbolic Representations with Transitional Dictionary Learning](https://mlsanity.com/paper/2308.02000/) (2023)

## Abstract (arXiv)

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 it 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, secondly, 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.