# Attention Schema in Neural Agents

arXiv: https://arxiv.org/abs/2305.17375 · HTML: https://mlsanity.com/paper/2305.17375/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels](https://mlsanity.com/paper/2101.02722/) (2021)
- [Opponent Modeling in Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05559/) (2016)
- [Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules](https://mlsanity.com/paper/2006.16981/) (2020)
- [Pragmatic-Pedagogic Value Alignment](https://mlsanity.com/paper/1707.06354/) (2017)
- [Joint Attention for Multi-Agent Coordination and Social Learning](https://mlsanity.com/paper/2104.07750/) (2021)
- [Neural Attention Models in Deep Learning: Survey and Taxonomy](https://mlsanity.com/paper/2112.05909/) (2021)
- [Social Neuro AI: Social Interaction as the "dark matter" of AI](https://mlsanity.com/paper/2112.15459/) (2021)

## Built on by (most cited first, 4 of 4)

- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Principles for Responsible AI Consciousness Research](https://mlsanity.com/paper/2501.07290/) (2025)
- [Testing Components of the Attention Schema Theory in Artificial Neural Networks](https://mlsanity.com/paper/2411.00983/) (2024)
- [Attention Schema-based Attention Control (ASAC): A Cognitive-Inspired Approach for Attention Management in Transformers](https://mlsanity.com/paper/2509.16058/) (2025)

## Abstract (arXiv)

Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static selection of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.