  A critical challenge for any intelligent system is to infer structure from
continuous data streams. Theories of event-predictive cognition suggest that
the brain segments sensorimotor information into compact event encodings, which
are used to anticipate and interpret environmental dynamics. Here, we introduce
a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of
counterfactual regularization. We test the model on a hierarchical sequence
prediction task, where sequences are generated by alternating hidden graph
structures. Our model learns to both compress the temporal dynamics of the task
into latent event-predictive encodings and anticipate event transitions at the
right moments, given noisy hidden signals about them. The addition of the
counterfactual regularization term ensures fluid transitions from one latent
code to the next, whereby the resulting latent codes exhibit compositional
properties. The implemented mechanisms offer a host of useful applications in
other domains, including hierarchical reasoning, planning, and decision making.
