# Cognitive science as a source of forward and inverse models of human decisions for robotics and control

arXiv: https://arxiv.org/abs/2109.00127 · HTML: https://mlsanity.com/paper/2109.00127/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 122 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [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)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Interactive Learning from Policy-Dependent Human Feedback](https://mlsanity.com/paper/1701.06049/) (2017)
- [Reward-rational (implicit) choice: A unifying formalism for reward learning](https://mlsanity.com/paper/2002.04833/) (2020)
- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [Environment-Independent Task Specifications via GLTL](https://mlsanity.com/paper/1704.04341/) (2017)
- [Evaluating Theory of Mind in Question Answering](https://mlsanity.com/paper/1808.09352/) (2018)
- [Deep Reinforcement Learning from Policy-Dependent Human Feedback](https://mlsanity.com/paper/1902.04257/) (2019)
- [Learning the Preferences of Ignorant, Inconsistent Agents](https://mlsanity.com/paper/1512.05832/) (2015)
- [Pragmatic-Pedagogic Value Alignment](https://mlsanity.com/paper/1707.06354/) (2017)
- [Theory of Minds: Understanding Behavior in Groups Through Inverse Planning](https://mlsanity.com/paper/1901.06085/) (2019)
- [Cognitive Model Priors for Predicting Human Decisions](https://mlsanity.com/paper/1905.09397/) (2019)
- [A mathematical theory of cooperative communication](https://mlsanity.com/paper/1910.02822/) (2019)
- [Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind](https://mlsanity.com/paper/2106.13249/) (2021)
- [Reasoning About Beliefs and Actions Under Computational Resource Constraints](https://mlsanity.com/paper/1304.2759/) (2013)
- [Resource-rational Task Decomposition to Minimize Planning Costs](https://mlsanity.com/paper/2007.13862/) (2020)
- [The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective](https://mlsanity.com/paper/1807.09000/) (2018)
- [Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making](https://mlsanity.com/paper/1611.10228/) (2016)

## Built on by (most cited first, 22 of 22)

- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)
- [Beyond Preferences in AI Alignment](https://mlsanity.com/paper/2408.16984/) (2024)
- [On Benchmarking Human-Like Intelligence in Machines](https://mlsanity.com/paper/2502.20502/) (2025)
- [Goals as Reward-Producing Programs](https://mlsanity.com/paper/2405.13242/) (2024)
- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs](https://mlsanity.com/paper/2204.01160/) (2022)
- [Concept Alignment as a Prerequisite for Value Alignment](https://mlsanity.com/paper/2310.20059/) (2023)
- [Zero-Shot Assistance in Sequential Decision Problems](https://mlsanity.com/paper/2202.07364/) (2022)
- [Bayesian Reinforcement Learning with Limited Cognitive Load](https://mlsanity.com/paper/2305.03263/) (2023)
- [PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles](https://mlsanity.com/paper/2407.16740/) (2024)
- [Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments](https://mlsanity.com/paper/2206.04546/) (2022)
- [Inverse Concave-Utility Reinforcement Learning is Inverse Game Theory](https://mlsanity.com/paper/2405.19024/) (2024)
- [Estimating cognitive biases with attention-aware inverse planning](https://mlsanity.com/paper/2510.25951/) (2025)
- [A space of goals: the cognitive geometry of informationally bounded agents](https://mlsanity.com/paper/2111.03699/) (2021)
- [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)
- [Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search](https://mlsanity.com/paper/2506.17424/) (2025)
- [The Alignment Bottleneck](https://mlsanity.com/paper/2509.15932/) (2025)
- [Goals and the Structure of Experience](https://mlsanity.com/paper/2508.15013/) (2025)
- [IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models](https://mlsanity.com/paper/2604.24002/) (2026)
- [Medical Model Synthesis Architectures: A Case Study](https://mlsanity.com/paper/2605.09716/) (2026)
- [CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition](https://mlsanity.com/paper/2609.21259/) (2026)

## Abstract (arXiv)

Those designing autonomous systems that interact with humans will invariably face questions about how humans think and make decisions. Fortunately, computational cognitive science offers insight into human decision-making using tools that will be familiar to those with backgrounds in optimization and control (e.g., probability theory, statistical machine learning, and reinforcement learning). Here, we review some of this work, focusing on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others' decision-making. We highlight relevant recent developments, including approaches that synthesize blackbox and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-theoretic terms. In doing so, we aim to provide readers with a glimpse of the range of frameworks, methodologies, and actionable insights that lie at the intersection of cognitive science and control research.