# Deep Learning and the Global Workspace Theory

arXiv: https://arxiv.org/abs/2012.10390 · HTML: https://mlsanity.com/paper/2012.10390/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 30 of 36)

- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Predictive Coding: a Theoretical and Experimental Review](https://mlsanity.com/paper/2107.12979/) (2021)
- [Verbalizable Representations Form a Global Workspace in Language Models](https://mlsanity.com/paper/2607.15495/) (2026)
- [A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine](https://mlsanity.com/paper/2107.13704/) (2021)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [On the link between conscious function and general intelligence in humans and machines](https://mlsanity.com/paper/2204.05133/) (2022)
- [Semi-supervised Multimodal Representation Learning through a Global Workspace](https://mlsanity.com/paper/2306.15711/) (2023)
- [Is artificial consciousness achievable? Lessons from the human brain](https://mlsanity.com/paper/2405.04540/) (2024)
- [GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory](https://mlsanity.com/paper/2406.12375/) (2024)
- [A Case for AI Consciousness: Language Agents and Global Workspace Theory](https://mlsanity.com/paper/2410.11407/) (2024)
- [RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks](https://mlsanity.com/paper/2106.08928/) (2021)
- [Strong hallucinations from negation and how to fix them](https://mlsanity.com/paper/2402.10543/) (2024)
- [Associative Transformer](https://mlsanity.com/paper/2309.12862/) (2023)
- [AI Consciousness is Inevitable: A Theoretical Computer Science Perspective](https://mlsanity.com/paper/2403.17101/) (2024)
- [Survey of Consciousness Theory from Computational Perspective](https://mlsanity.com/paper/2309.10063/) (2023)
- [Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace](https://mlsanity.com/paper/2403.04588/) (2024)
- [Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning](https://mlsanity.com/paper/2502.21142/) (2025)
- [Continual learning for rotating machinery fault diagnosis with cross-domain environmental and operational variations](https://mlsanity.com/paper/2504.10151/) (2025)
- [GAttANet: Global attention agreement for convolutional neural networks](https://mlsanity.com/paper/2104.05575/) (2021)
- [Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning](https://mlsanity.com/paper/2405.13978/) (2024)
- [MAIN-VLA: Modeling Abstraction of Intention and eNvironment for Vision-Language-Action Models](https://mlsanity.com/paper/2602.02212/) (2026)
- [HiCI: Hierarchical Construction-Integration for Long-Context Attention](https://mlsanity.com/paper/2603.20843/) (2026)
- [CTM-AI: A Blueprint for General AI Inspired by a Model of Consciousness](https://mlsanity.com/paper/2605.04097/) (2026)
- [Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture](https://mlsanity.com/paper/2303.13651/) (2023)
- [Learning to Chain Operations by Routing Information Through a Global Workspace](https://mlsanity.com/paper/2503.01906/) (2025)
- [Hypothesis on the Functional Advantages of the Selection-Broadcast Cycle Structure: Global Workspace Theory and Dealing with a Real-Time World](https://mlsanity.com/paper/2505.13969/) (2025)
- [DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction](https://mlsanity.com/paper/2305.04891/) (2023)
- [Meta Neural Coordination](https://mlsanity.com/paper/2305.12109/) (2023)
- [Contraction Properties of the Global Workspace Primitive](https://mlsanity.com/paper/2310.01571/) (2023)
- [Attention Schema-based Attention Control (ASAC): A Cognitive-Inspired Approach for Attention Management in Transformers](https://mlsanity.com/paper/2509.16058/) (2025)

## Abstract (arXiv)

Recent advances in deep learning have allowed Artificial Intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic or cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive architectures. The Global Workspace theory refers to a large-scale system integrating and distributing information among networks of specialized modules to create higher-level forms of cognition and awareness. We argue that the time is ripe to consider explicit implementations of this theory using deep learning techniques. We propose a roadmap based on unsupervised neural translation between multiple latent spaces (neural networks trained for distinct tasks, on distinct sensory inputs and/or modalities) to create a unique, amodal global latent workspace (GLW). Potential functional advantages of GLW are reviewed, along with neuroscientific implications.