  Humans readily generalize, applying prior knowledge to novel situations and
stimuli. Advances in machine learning and artificial intelligence have begun to
approximate and even surpass human performance, but machine systems reliably
struggle to generalize information to untrained situations. We describe a
neural network model that is trained to play one video game (Breakout) and
demonstrates one-shot generalization to a new game (Pong). The model
generalizes by learning representations that are functionally and formally
symbolic from training data, without feedback, and without requiring that
structured representations be specified a priori. The model uses unsupervised
comparison to discover which characteristics of the input are invariant, and to
learn relational predicates; it then applies these predicates to arguments in a
symbolic fashion, using oscillatory regularities in network firing to
dynamically bind predicates to arguments. We argue that models of human
cognition must account for far-reaching and flexible generalization, and that
in order to do so, models must be able to discover symbolic representations
from unstructured data, a process we call predicate learning. Only then can
models begin to adequately explain where human-like representations come from,
why human cognition is the way it is, and why it continues to differ from
machine intelligence in crucial ways.
