Humans predict liquid dynamics using probabilistic simulation

79 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Flexible Neural Representation for Physics Prediction (2018, background): “Recent research in computational cognitive science has posited that humans run physics simulations in their mind [5, 3, 20, 48, 21].”
A Compositional Object-Based Approach to Learning Physical Dynamics (2016, methodology): “Top-down and bottom-up approaches A recent set of top-down approaches investigate probabilistic game physics engines as computational models for physical simulation in humans (Bates et al., 2015; Battaglia et al., 2013; Hamrick et al., 2011; Ullman et al., 2014).”
Learning Physical Intuition of Block Towers by Example (2016, background): “A number of works in cognitive science have explored intuitive physics, for example, in the context of liquid dynamics (Bates et al., 2015), ballistic motion (Smith et al., 2013) and gears and pulleys (Hegarty, 2004).”
Mind Games: Game Engines as an Architecture for Intuitive Physics. (2017, background): “Such a ﬂuid-simulation may also explain adult proﬁciency with predicting certain ﬂuid dynamic tasks [49].”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “This line of research aligns with the recent findings of intuitive physics in psychology [70, 165, 181, 182, 183, 184], and enabled a few interesting new directions and applications in computer vision, including reasoning about liquid transfer [283, 284], container and containment relationships [285”