# Learning Theory of Mind via Dynamic Traits Attribution

arXiv: https://arxiv.org/abs/2204.09047 · HTML: https://mlsanity.com/paper/2204.09047/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Using Fast Weights to Attend to the Recent Past](https://mlsanity.com/paper/1610.06258/) (2016)
- [Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration](https://mlsanity.com/paper/2010.09890/) (2020)
- [Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1901.09207/) (2019)
- [AGENT: A Benchmark for Core Psychological Reasoning](https://mlsanity.com/paper/2102.12321/) (2021)
- [Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others](https://mlsanity.com/paper/2102.11938/) (2021)
- [Neural Recursive Belief States in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.02274/) (2021)
- [Deep Interpretable Models of Theory of Mind](https://mlsanity.com/paper/2104.02938/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Review on Machine Theory of Mind](https://mlsanity.com/paper/2303.11594/) (2023)
- [Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions](https://mlsanity.com/paper/2407.06762/) (2024)
- [Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy](https://mlsanity.com/paper/2210.09435/) (2022)
- [Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mind](https://mlsanity.com/paper/2309.17275/) (2023)
- [Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling](https://mlsanity.com/paper/2605.12411/) (2026)

## Abstract (arXiv)

Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the mental states of others from observed contextual action trajectories, enabling future prediction of goals, intention, actions and successor representations. The underlying mechanism for such a prediction remains unclear, however. Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories. This trait vector then multiplicatively modulates the prediction mechanism via a `fast weights' scheme in the prediction neural network, which reads the current context and predicts the behaviour. We empirically show that the fast weights provide a good inductive bias to model the character traits of agents and hence improves mindreading ability. On the indirect assessment of false-belief understanding, the new ToM model enables more efficient helping behaviours.