Modeling human intuitions about liquid flow with particle-based simulation

Abstract

Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids—splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring—despite tremendous variability in their material and dynamical properties. Here we propose and test a computational model of how people perceive and predict these liquid dynamics, based on coarse approximate simulations of fluids as collections of interacting particles. Our model is analogous to a “game engine in the head”, drawing on techniques for interactive simulations (as in video games) that optimize for efficiency and natural appearance rather than physical accuracy. In two behavioral experiments, we found that the model accurately captured people’s predictions about how liquids flow among complex solid obstacles, and was significantly better than several alternatives based on simple heuristics and deep neural networks. Our model was also able to explain how people’s predictions varied as a function of the liquids’ properties (e.g., viscosity and stickiness). Together, the model and empirical results extend the recent proposal that human physical scene understanding for the dynamics of rigid, solid objects can be supported by approximate probabilistic simulation, to the more complex and unexplored domain of fluid dynamics.

Identifying the signature of prospective motor control in children with autism

“…We propose that the later temporal setting of key landmarks in ASD grasp-to-pass movements may reflect reduced motor anticipation under conditions of uncertainty. A similar explanation may hold for grasp-to-pour movements, which have been shown to involve demanding probabilistic inferences about fluid dynamics over short time scales 29 . This account makes the prediction, testable in future studies, that ASD and TD movement profiles become more distinct under conditions of increased volatility 30 .…”

Identifying the signature of prospective motor control in children with autism

“…We propose that the later temporal setting of key landmarks in ASD grasp-to-pass movements may re ect reduced motor anticipation under conditions of uncertainty. A similar explanation may hold for grasp-to-pour movements, which have been shown to involve demanding probabilistic inferences about uid dynamics over short time scales 28 . This account makes the prediction, testable in future studies, that ASD and TD movement pro les become more distinct under conditions of increased volatility 29 .…”

Mental geometry of 3D size and shape perception

“…In the dynamic case, this could be related to the possibility that articulated objects are more common in the world then are objects that increase or decrease in length over a short period. The same appeal to natural statistics could be invoked to explain the difficulty in perceiving expansion or contraction in depth of solid objects (Johansson, 1964;Jansson & Johansson, 1973;Jain & Zaidi, 2011), while other deformations are easy to discern, even for rotating and flowing shapes (Cohen, Jain & Zaidi, 2010;Fantoni, Caudek & Domini, 2014;Bates et al, 2019). However, the slant illusion is just as compelling in the static case, so biases in perceiving depth versus extent from retinal images may also be in play (Jain & Zaidi, 2013;Kim & Burge 2018).…”