# Who is Mistaken?

arXiv: https://arxiv.org/abs/1612.01175 · HTML: https://mlsanity.com/paper/1612.01175/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2016.

## Built on by (most cited first, 9 of 9)

- [Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models](https://mlsanity.com/paper/2310.19619/) (2023)
- [Learning Triadic Belief Dynamics in Nonverbal Communication from Videos](https://mlsanity.com/paper/2104.02841/) (2021)
- [A Review on Machine Theory of Mind](https://mlsanity.com/paper/2303.11594/) (2023)
- [Through the Theory of Mind's Eye: Reading Minds with Multimodal Video Large Language Models](https://mlsanity.com/paper/2406.13763/) (2024)
- [BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind](https://mlsanity.com/paper/2402.07402/) (2024)
- [From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models](https://mlsanity.com/paper/2506.14224/) (2025)
- [ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions](https://mlsanity.com/paper/2509.05066/) (2025)
- [Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models](https://mlsanity.com/paper/2103.14712/) (2021)
- [MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation](https://mlsanity.com/paper/2306.15253/) (2023)

## Abstract (arXiv)

Recognizing when people have false beliefs is crucial for understanding their actions. We introduce the novel problem of identifying when people in abstract scenes have incorrect beliefs. We present a dataset of scenes, each visually depicting an 8-frame story in which a character has a mistaken belief. We then create a representation of characters' beliefs for two tasks in human action understanding: predicting who is mistaken, and when they are mistaken. Experiments suggest that our method for identifying mistaken characters performs better on these tasks than simple baselines. Diagnostics on our model suggest it learns important cues for recognizing mistaken beliefs, such as gaze. We believe models of people's beliefs will have many applications in action understanding, robotics, and healthcare.