Intuitive Physics: Current Research and Controversies

Kubricht et al., 2017

Source: Kubricht et al., 2017


  • Early research in intuitve physics indicated that humans exhibit common misconceptions and biases when predicting the physical world
  • However, more recent work has indicated that some biases can be explained by the application of normative physical principles to noisy perceptual inputs
  • How these physical principles are learned, represented, and applied remains unclear
  • Links: [ website ] [ pdf ]


  • Early work found that human predictions often disagree with (ground-truth) Newtonian physics
    • Participants asked to draw how situation would unfold based on static diagram of physical scenario
  • These misconceptions can be reduced by changing the experimental paradigm
    • Adults are bad at drawing trajectory, but can predict landing position
    • Suggests that humans have strong intuitive “physics engine”, but only applied in certain conditions
    • Cortical activation associated with explicit physical knowledge does not enitrely overlap with areas for tacit physical inference


  • Recent research on intuitive physics uses the noisy Newton framework, where inference is achieved by passing noisy information through a physics engine

    • Object dynamics knowledge is “written in”
    • The belief that humans contrust mental models for physical situations underlies these models
  • Shift metric to correlation of predictions between humans and models versus absolute performance levels

  • Account for human predictions through noisy perceptual inputs, since physical princeiples are approximated but not biased

  • Additional hybrid approach combining knowledge-based physics model with learned network for predicting physical attributes from visual inputs

  • Formal definition of problem

  • Model details/experimental setup


  • Probabilistic simulation model predictions correlate well with human performance across experimental conditions for stacked blocks and liquids moving past obstacles
    • Introducing randomness into dynamics improves fit to human predictions of an occluded object bouncing in a box
  • Support for core knowledge thesis: core physical principles guide the construction of tacit theories of motion
    • Initial knowledge about the physical world is specific to learned domains


  • Intuitive physics research has benefited from advances in:
    • Stimulus displays - from static diagrams to dynamic animations
    • Computational theory - from heuristic accounts to probabilistic simulation framework
    • Choice of physical situations - from focus on rigid objects to non-rigid fluids
  • While human predictions are consistent with probabilistic inference, such models require numerous simulations based on hard-coded physical principles
Elias Z. Wang
Elias Z. Wang
AI Researcher | PhD Candidate