# Probing Physics Knowledge Using Tools from Developmental Psychology

arXiv: https://arxiv.org/abs/1804.01128 · HTML: https://mlsanity.com/paper/1804.01128/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [Physion: Evaluating Physical Prediction from Vision in Humans and Machines](https://mlsanity.com/paper/2106.08261/) (2021)
- [AGENT: A Benchmark for Core Psychological Reasoning](https://mlsanity.com/paper/2102.12321/) (2021)
- [A Survey on Machine Learning Approaches for Modelling Intuitive Physics](https://mlsanity.com/paper/2202.06481/) (2022)
- [WorldBench: Benchmarking Physical Understanding of World Models by Isolating Physics Concepts](https://mlsanity.com/paper/2601.21282/) (2026)
- [PhysInOne: Visual Physics Learning and Reasoning in One Suite](https://mlsanity.com/paper/2604.09415/) (2026)
- [A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories](https://mlsanity.com/paper/2111.08826/) (2021)
- [Neural Reasoning About Agents' Goals, Preferences, and Actions](https://mlsanity.com/paper/2312.07122/) (2023)
- [Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning](https://mlsanity.com/paper/2204.01437/) (2022)
- [AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition](https://mlsanity.com/paper/2110.05836/) (2021)
- [X-VoE: Measuring eXplanatory Violation of Expectation in Physical Events](https://mlsanity.com/paper/2308.10441/) (2023)
- [The Acquisition of Physical Knowledge in Generative Neural Networks](https://mlsanity.com/paper/2310.19943/) (2023)
- [Stochastic Gradient Descent Captures How Children Learn About Physics](https://mlsanity.com/paper/2209.12344/) (2022)

## Abstract (arXiv)

In order to build agents with a rich understanding of their environment, one key objective is to endow them with a grasp of intuitive physics; an ability to reason about three-dimensional objects, their dynamic interactions, and responses to forces. While some work on this problem has taken the approach of building in components such as ready-made physics engines, other research aims to extract general physical concepts directly from sensory data. In the latter case, one challenge that arises is evaluating the learning system. Research on intuitive physics knowledge in children has long employed a violation of expectations (VOE) method to assess children's mastery of specific physical concepts. We take the novel step of applying this method to artificial learning systems. In addition to introducing the VOE technique, we describe a set of probe datasets inspired by classic test stimuli from developmental psychology. We test a baseline deep learning system on this battery, as well as on a physics learning dataset ("IntPhys") recently posed by another research group. Our results show how the VOE technique may provide a useful tool for tracking physics knowledge in future research.