7 research outputs found
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding
Humans demonstrate remarkable abilities to predict physical events in complex
scenes. Two classes of models for physical scene understanding have recently
been proposed: "Intuitive Physics Engines", or IPEs, which posit that people
make predictions by running approximate probabilistic simulations in causal
mental models similar in nature to video-game physics engines, and memory-based
models, which make judgments based on analogies to stored experiences of
previously encountered scenes and physical outcomes. Versions of the latter
have recently been instantiated in convolutional neural network (CNN)
architectures. Here we report four experiments that, to our knowledge, are the
first rigorous comparisons of simulation-based and CNN-based models, where both
approaches are concretely instantiated in algorithms that can run on raw image
inputs and produce as outputs physical judgments such as whether a stack of
blocks will fall. Both approaches can achieve super-human accuracy levels and
can quantitatively predict human judgments to a similar degree, but only the
simulation-based models generalize to novel situations in ways that people do,
and are qualitatively consistent with systematic perceptual illusions and
judgment asymmetries that people show.Comment: Accepted to CogSci 2016 as an oral presentatio