  It has been proposed that, when processing a stream of events, humans divide
their experiences in terms of inferred latent causes (LCs) to support
context-dependent learning. However, when shared structure is present across
contexts, it is still unclear how the "splitting" of LCs and learning of shared
structure can be simultaneously achieved. Here, we present the Latent Cause
Network (LCNet), a neural network model of LC inference. Through learning, it
naturally stores structure that is shared across tasks in the network weights.
Additionally, it represents context-specific structure using a context module,
controlled by a Bayesian nonparametric inference algorithm, which assigns a
unique context vector for each inferred LC. Across three simulations, we found
that LCNet could 1) extract shared structure across LCs in a function learning
task while avoiding catastrophic interference, 2) capture human data on
curriculum effects in schema learning, and 3) infer the underlying event
structure when processing naturalistic videos of daily events. Overall, these
results demonstrate a computationally feasible approach to reconciling shared
structure and context-specific structure in a model of LCs that is scalable
from laboratory experiment settings to naturalistic settings.
