Humans have schematic knowledge of how certain types of events unfold (e.g. coffeeshop visits) that can readily be generalized to new instances of those events. Schematic knowledge allows humans to perform role-filler binding, the task of associating schematic roles (e.g. "barista") with specific fillers (e.g. "Bob"). Here we examined whether and how recurrent neural networks learn to do this. We procedurally generated stories from an underlying generative graph, and trained networks on role-filler binding question-answering tasks. We tested whether networks can learn to maintain filler information on their own, and whether they can generalize to fillers that they have not seen before. We studied networks by analyzing their behavior and decoding their memory states. We found that a network's success in learning role-filler binding depends on both the breadth of roles introduced during training, and the network's memory architecture. In our decoding analyses, we observed a close relationship between the information we could decode from various parts of network architecture, and the information the network could recall.

We develop a probabilistic model of human memory performance in free recall experiments. In these experiments, a subject first studies a list of words and then tries to recall them. To model these data, we draw on both previous psychological research and statistical topic models of text documents. We assume that memories are formed by assimilating the semantic meaning of studied words (represented as a distribution over topics) into a slowly changing latent context (represented in the same space). During recall, this context is reinstated and used as a cue for retrieving studied words. By conceptualizing memory retrieval as a dynamic latent variable model, we are able to use Bayesian inference to represent uncertainty and reason about the cognitive processes underlying memory. We present a particle filter algorithm for performing approximate posterior inference, and evaluate our model on the prediction of recalled words in experimental data. By specifying the model hierarchically, we are also able to capture inter-subject variability.