# Building Second-Order Mental Models for Human-Robot Interaction

arXiv: https://arxiv.org/abs/1909.06508 · HTML: https://mlsanity.com/paper/1909.06508/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

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

- [On the Critical Role of Conventions in Adaptive Human-AI Collaboration](https://mlsanity.com/paper/2104.02871/) (2021)
- [Partner-Aware Algorithms in Decentralized Cooperative Bandit Teams](https://mlsanity.com/paper/2110.00751/) (2021)
- [Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave](https://mlsanity.com/paper/2409.13533/) (2024)
- [What Do You Think I Think? Accounting for Human Beliefs Using Second-Order Theory of Mind](https://mlsanity.com/paper/2605.12745/) (2026)

## Abstract (arXiv)

The mental models that humans form of other agents---encapsulating human beliefs about agent goals, intentions, capabilities, and more---create an underlying basis for interaction. These mental models have the potential to affect both the human's decision making during the interaction and the human's subjective assessment of the interaction. In this paper, we surveyed existing methods for modeling how humans view robots, then identified a potential method for improving these estimates through inferring a human's model of a robot agent directly from their actions. Then, we conducted an online study to collect data in a grid-world environment involving humans moving an avatar past a virtual agent. Through our analysis, we demonstrated that participants' action choices leaked information about their mental models of a virtual agent. We conclude by discussing the implications of these findings and the potential for such a method to improve human-robot interactions.