System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems
Not on the map. Why?
Abstract (arXiv)
This paper introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition. Expanding upon System 1 (fast, intuitive thinking) and System 2 (slow, deliberative thinking), we incorporate System 0, which represents pre-cognitive embodied processes, and System 3, which encompasses collective intelligence and symbol emergence. We contextualize this model within Bergson's philosophy by adopting multi-scale time theory to unify the diverse temporal dynamics of cognition. System 0 emphasizes morphological computation and passive dynamics, illustrating how physical embodiment enables adaptive behavior without explicit neural processing. Systems 1 and 2 are explained from a constructive perspective, incorporating neurodynamical and AI viewpoints. In System 3, we introduce collective predictive coding to explain how societal-level adaptation and symbol emergence operate over extended timescales. This comprehensive framework ranges from rapid embodied reactions to slow-evolving collective intelligence, offering a unified perspective on cognition across multiple timescales, levels of abstraction, and forms of human intelligence. The System 0/1/2/3 model provides a novel theoretical foundation for understanding the interplay between adaptive and cognitive processes, thereby opening new avenues for research in cognitive science, AI, robotics, and collective intelligence.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 54 of its 179 references that are in the map. First posted on arXiv 8 Mar 2025.
Builds on
54 of 179 references.
References · 54
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
- 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,777
38 more
- 2016Matching Networks for One Shot Learningcited by 2,246
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
- 2018Learning Latent Dynamics for Planning from Pixelscited by 1,221
- 2020Mastering Atari with Discrete World Modelscited by 990
- 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
- 2018Recurrent World Models Facilitate Policy Evolutioncited by 761
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
- 2024Robotic Control via Embodied Chain-of-Thought Reasoningcited by 340
- 20243D-VLA: A 3D Vision-Language-Action Generative World Modelcited by 320
- 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 297
- 2024Scaling Large Language Model-based Multi-Agent Collaborationcited by 235
- 2023Large Language Model Guided Tree-of-Thoughtcited by 218
- 2016Enhanced LSTM for Natural Language Inferencecited by 199
- 2024More Agents Is All You Needcited by 174
- 2020Emergent Multi-Agent Communication in the Deep Learning Eracited by 168
- 2024Knowledge Fusion of Large Language Modelscited by 110
- 2017The Consciousness Priorcited by 105
- 2025Multiagent Finetuning: Self Improvement with Diverse Reasoning Chainscited by 90
- 2024CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Drivingcited by 74
- 2024MALT: Improving Reasoning with Multi-Agent LLM Trainingcited by 71
- 2020Thinking Fast and Slow in AIcited by 67
- 2024Real-World Robot Applications of Foundation Models: A Reviewcited by 60
- 2024Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimizationcited by 56
- 2024Symbolic Music Generation with Non-Differentiable Rule Guided Diffusioncited by 52
- 2018Symbol Emergence in Cognitive Developmental Systems: a Surveycited by 39
- 2021Fast and Slow Learning of Recurrent Independent Mechanismscited by 36
- 2023World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challengescited by 35
- 2015Symbol Emergence in Robotics: A Surveycited by 31
- 2024ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Modelscited by 26
- 2022Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Modelscited by 16
- 2024Generative Emergent Communication: Large Language Model is a Collective World Modelcited by 15
- 2019Symbol Emergence as an Interpersonal Multimodal Categorizationcited by 14
- 2024Emergent Language: A Survey and Taxonomycited by 12
- 2021Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inferencecited by 10
- 2014Exploiting short-term memory in soft body dynamics as a computational resourcecited by 8
- 2023Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Modelscited by 8
- 2024Learning and communication pressures in neural networks: Lessons from emergent communicationcited by 8
50 most cited of 54 All 54 (JSON)
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.