Toward a More Neurally Plausible Neural Network Model of Latent Cause Inference

Humans spontaneously perceive a continuous stream of experience as discrete events. It has been hypothesized that this ability is supported by latent cause inference (LCI). We implemented this hypothesis using latent cause network (LCNet), a neural network model of LCI. LCNet interacts with a Bayesian LCI mechanism that activates a unique context vector for each inferred latent cause (LC). LCNet can also recall episodic memories of previously inferred LCs to avoid performing LCI all the time. These mechanisms make LCNet more neurally plausible and efficient than existing models. Across three simulations, we found that LCNet could 1) extract shared structure across LCs while avoiding catastrophic interference, 2) capture human data on curriculum effects on schema learning, and 3) infer the underlying event structure when processing naturalistic videos of daily activities. Our work provides a neurally plausible computational model that can operate in both laboratory experiment settings and naturalistic settings, opening up the possibility of providing a unified model of event cognition.