# Sequential Explanations with Mental Model-Based Policies

arXiv: https://arxiv.org/abs/2007.09028 · HTML: https://mlsanity.com/paper/2007.09028/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making](https://mlsanity.com/paper/2205.05126/) (2022)
- [Explainable Autonomous Robots: A Survey and Perspective](https://mlsanity.com/paper/2105.02658/) (2021)
- [Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations](https://mlsanity.com/paper/2305.03117/) (2023)
- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [Mediators: Conversational Agents Explaining NLP Model Behavior](https://mlsanity.com/paper/2206.06029/) (2022)
- [Situated Natural Language Explanations](https://mlsanity.com/paper/2308.14115/) (2023)
- [Optimising Human-AI Collaboration by Learning Convincing Explanations](https://mlsanity.com/paper/2311.07426/) (2023)
- [Scenarios and Approaches for Situated Natural Language Explanations](https://mlsanity.com/paper/2406.05035/) (2024)

## Abstract (arXiv)

The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this information. We apply a reinforcement learning framework which emulates this format by providing explanations based on the explainee's current mental model. We conduct novel online human experiments where explanations generated by various explanation methods are selected and presented to participants, using policies which observe participants' mental models, in order to optimize an interpretability proxy. Our results suggest that mental model-based policies (anchored in our proposed state representation) may increase interpretability over multiple sequential explanations, when compared to a random selection baseline. This work provides insight into how to select explanations which increase relevant information for users, and into conducting human-grounded experimentation to understand interpretability.