# On the link between conscious function and general intelligence in humans and machines arXiv: https://arxiv.org/abs/2204.05133 ยท HTML: https://mlsanity.com/paper/2204.05133/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 92 of its 248 references that are in the map. - First posted on arXiv 24 Mar 2022. ## Builds on (most cited first, 30 of 92) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017) - [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021) - [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013) - [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019) - [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020) - [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020) - [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020) - [World Models](https://mlsanity.com/paper/1803.10122/) (2018) - [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015) - [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016) - [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020) - [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019) - [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018) - [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018) - [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019) - [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015) - [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016) - [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020) - [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014) - [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019) - [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014) - [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018) ## Built on by (most cited first, 16 of 16) - [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023) - [Taking AI Welfare Seriously](https://mlsanity.com/paper/2411.00986/) (2024) - [Task-Aware Information Routing from Common Representation Space in Lifelong Learning](https://mlsanity.com/paper/2302.11346/) (2023) - [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023) - [Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning](https://mlsanity.com/paper/2310.11341/) (2023) - [LSFSL: Leveraging Shape Information in Few-shot Learning](https://mlsanity.com/paper/2304.06672/) (2023) - [Associative Transformer](https://mlsanity.com/paper/2309.12862/) (2023) - [If consciousness is dynamically relevant, artificial intelligence isn't conscious](https://mlsanity.com/paper/2304.05077/) (2023) - [A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization](https://mlsanity.com/paper/2310.15177/) (2023) - [Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning](https://mlsanity.com/paper/2405.13978/) (2024) - [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) - [MIRROR: Converging Cognitive Principles as Computational Mechanisms for AI Reasoning](https://mlsanity.com/paper/2506.00430/) (2025) - [Just aware enough: Evaluating awareness across artificial systems](https://mlsanity.com/paper/2601.14901/) (2026) - [Meta Neural Coordination](https://mlsanity.com/paper/2305.12109/) (2023) - [Self-Monitoring Benefits from Structural Integration: Lessons from Metacognition in Continuous-Time Multi-Timescale Agents](https://mlsanity.com/paper/2604.11914/) (2026) - [AwarenessBench: Assessing Cognitive Capabilities of Language Models](https://mlsanity.com/paper/2609.35409/) (2026) ## Abstract (arXiv) In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this work, we explore the validity and potential application of this seemingly intuitive link between consciousness and intelligence. We do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST). We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Having identified this trend, we use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a single unified and implementable model. Given that it is made possible by cognitive abilities underlying each of the three functional theories, artificial agents capable of mental time travel would not only possess greater general intelligence than current approaches, but also be more consistent with our current understanding of the functional role of consciousness in humans, thus making it a promising near-term goal for AI research.