mlsanityData to 14 Oct 2026

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

arXiv 2109.00127 ↗First posted 1 Sep 2021cs.AI cs.RO cs.SYcited by 22

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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.

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

20 of 122 references.

References · 20

  1. 2016Cooperative Inverse Reinforcement Learningcited by 300
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 298
  3. 2017Inverse Reward Designcited by 249
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  5. 2017Interactive Learning from Policy-Dependent Human Feedbackcited by 158
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8 more
  1. 2019Theory of Minds: Understanding Behavior in Groups Through Inverse Planningcited by 30
  2. 2019Cognitive Model Priors for Predicting Human Decisionscited by 25
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 6; 2025: 6; 2026: 3.

22 papers.

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  1. 2024Building Machines that Learn and Think with Peoplecited by 73
  2. 2024Large Language Models Assume People are More Rational than We Really arecited by 54
  3. 2024Beyond Preferences in AI Alignmentcited by 49
  4. 2025On Benchmarking Human-Like Intelligence in Machinescited by 25
  5. 2024Goals as Reward-Producing Programscited by 14
  6. 2022MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communicationcited by 7
  7. 2022Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurscited by 5
  8. 2023Concept Alignment as a Prerequisite for Value Alignmentcited by 5
  9. 2022Zero-Shot Assistance in Sequential Decision Problemscited by 4
  10. 2023Bayesian Reinforcement Learning with Limited Cognitive Loadcited by 4
  11. 2024PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehiclescited by 3
  12. 2022Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environmentscited by 2
10 more
  1. 2024Inverse Concave-Utility Reinforcement Learning is Inverse Game Theorycited by 2
  2. 2025Estimating cognitive biases with attention-aware inverse planningcited by 2
  3. 2021A space of goals: the cognitive geometry of informationally bounded agentscited by 1
  4. 2025Hypothesis on the Functional Advantages of the Selection-Broadcast Cycle Structure: Global Workspace Theory and Dealing with a Real-Time Worldcited by 1
  5. 2025Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Searchcited by 1
  6. 2025The Alignment Bottleneckcited by 1
  7. 2025Goals and the Structure of Experiencecited by 0
  8. 2026IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Modelscited by 0
  9. 2026Medical Model Synthesis Architectures: A Case Studycited by 0
  10. 2026CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognitioncited by 0