# The Role of Higher-Order Cognitive Models in Active Learning

arXiv: https://arxiv.org/abs/2401.04397 · HTML: https://mlsanity.com/paper/2401.04397/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences](https://mlsanity.com/paper/2006.14091/) (2020)
- [Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1811.01458/) (2018)
- [Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.09207/) (2019)
- [How to talk so AI will learn: Instructions, descriptions, and autonomy](https://mlsanity.com/paper/2206.07870/) (2022)
- [Neural Recursive Belief States in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.02274/) (2021)
- [Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning](https://mlsanity.com/paper/1903.03877/) (2019)
- [Active Deep Q-learning with Demonstration](https://mlsanity.com/paper/1812.02632/) (2018)
- [Human Strategic Steering Improves Performance of Interactive Optimization](https://mlsanity.com/paper/2005.01291/) (2020)

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

- [Active Inference and Human--Computer Interaction](https://mlsanity.com/paper/2412.14741/) (2024)
- [A formal definition and meta-model for a machine theory of mind](https://mlsanity.com/paper/2606.03471/) (2026)

## Abstract (arXiv)

Building machines capable of efficiently collaborating with humans has been a longstanding goal in artificial intelligence. Especially in the presence of uncertainties, optimal cooperation often requires that humans and artificial agents model each other's behavior and use these models to infer underlying goals, beliefs or intentions, potentially involving multiple levels of recursion. Empirical evidence for such higher-order cognition in human behavior is also provided by previous works in cognitive science, linguistics, and robotics. We advocate for a new paradigm for active learning for human feedback that utilises humans as active data sources while accounting for their higher levels of agency. In particular, we discuss how increasing level of agency results in qualitatively different forms of rational communication between an active learning system and a teacher. Additionally, we provide a practical example of active learning using a higher-order cognitive model. This is accompanied by a computational study that underscores the unique behaviors that this model produces.