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A critical challenge for any intelligent system is to infer structure fromcontinuous data streams. Theories of event-predictive cognition suggest thatthe brain segments sensorimotor information into compact event encodings, whichare used to anticipate and interpret environmental dynamics. Here, we introducea SUrprise-GAted Recurrent neural network (SUGAR) using a novel form ofcounterfactual regularization. We test the model on a hierarchical sequenceprediction task, where sequences are generated by alternating hidden graphstructures. Our model learns to both compress the temporal dynamics of the taskinto latent event-predictive encodings and anticipate event transitions at theright moments, given noisy hidden signals about them. The addition of thecounterfactual regularization term ensures fluid transitions from one latentcode to the next, whereby the resulting latent codes exhibit compositionalproperties. The implemented mechanisms offer a host of useful applications inother domains, including hierarchical reasoning, planning, and decision making.

Latent Event-Predictive Encodings through Counterfactual Regularization