# Estimating cognitive biases with attention-aware inverse planning

arXiv: https://arxiv.org/abs/2510.25951 · HTML: https://mlsanity.com/paper/2510.25951/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 21 of 21)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://mlsanity.com/paper/1807.01281/) (2018)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [People construct simplified mental representations to plan](https://mlsanity.com/paper/2105.06948/) (2021)
- [GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS](https://mlsanity.com/paper/2408.01584/) (2024)
- [Cognitive science as a source of forward and inverse models of human decisions for robotics and control](https://mlsanity.com/paper/2109.00127/) (2021)
- [Building reliable sim driving agents by scaling self-play](https://mlsanity.com/paper/2502.14706/) (2025)
- [MAAD: A Model and Dataset for "Attended Awareness" in Driving](https://mlsanity.com/paper/2110.08610/) (2021)
- [Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2203.04421/) (2022)
- [Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior](https://mlsanity.com/paper/2502.20349/) (2025)
- [Concept Alignment as a Prerequisite for Value Alignment](https://mlsanity.com/paper/2310.20059/) (2023)
- [Few-shot Personalized Scanpath Prediction](https://mlsanity.com/paper/2504.05499/) (2025)
- [Customized Handling of Unintended Interface Operation in Assistive Robots](https://mlsanity.com/paper/2007.02092/) (2020)
- [How attention simplifies mental representations for planning](https://mlsanity.com/paper/2506.09520/) (2025)
- [Optimal Driver Warning Generation in Dynamic Driving Environment](https://mlsanity.com/paper/2411.06306/) (2024)

## Built on by (most cited first, 2 of 2)

- [Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making](https://mlsanity.com/paper/2601.08247/) (2026)
- [Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design](https://mlsanity.com/paper/2607.28894/) (2026)

## Abstract (arXiv)

People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks such as driving to work. Here, building on recent work in computational cognitive science, we formally articulate the attention-aware inverse planning problem, in which the goal is to estimate a person's attentional biases from their actions. We demonstrate how attention-aware inverse planning systematically differs from standard inverse reinforcement learning and how cognitive biases can be inferred from behavior. Finally, we present an approach to attention-aware inverse planning that combines deep reinforcement learning with computational cognitive modeling. We use this approach to infer the attentional strategies of RL agents in real-life driving scenarios selected from the Waymo Open Dataset, demonstrating the scalability of estimating cognitive biases with attention-aware inverse planning.