Abstract

We address the problem of learning grounded causal models: systems of concepts that are connected by causal relations and explicitly grounded in perception. We present a Bayesian framework for learning these models—both a causal Bayesian network structure over variables and the consequential region of each variable in perceptual space—from dynamic perceptual evidence. Using a novel experimental paradigm we show that humans are able to learn grounded causal models, and that the Bayesian model accounts well for human performance.

Citations