Reconciling Shared versus Context-Specific Information in a Neural Network Model of Latent Causes
This work addresses a fundamental challenge in cognitive modeling for researchers studying context-dependent learning, though it appears incremental as it builds on existing latent cause theories.
The authors tackled the problem of how neural networks can simultaneously learn shared structure across contexts and context-specific information without catastrophic interference, and demonstrated that their Latent Cause Network (LCNet) model successfully achieved this in simulations including function learning, schema learning, and naturalistic video processing.
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.