Cognitive modeling of free association
Free association among words is a fundamental and ubiquitous memory task, yet there have been few attempts to apply established cognitive process models of memory search to free association data. We address this by using a simplified variant of a popular recurrent neural network model of recall, the Context Maintenance and Retrieval (CMR) model, which we fit on a large free association dataset. We find that this network, with response biases and asymmetric cue-context and context-cue weight matrices, outperforms previous models without these components (which emerge as special cases of our model), on a variety of metrics including prediction of association asymmetries. We also find that continued free association, where the participant provides multiple responses to a single cue, is best described with a combination of (a) a partially decaying context layer, where representations of the cue and earlier responses are largely maintained over time and (b) a weak but persistent and non-decaying effect of the cue. This network also accounts for ‘response chaining’ effects in continued free association, whereby earlier responses seem to prime later responses. Finally, we show that training our CMR variant on free association data generates improved predictions for list-based recall, demonstrating the value of free association for the study of many different types of memory phenomena. Overall, our analysis provides new explanations for empirical findings on free association, predicts free association with increased accuracy, and integrates theories of free association with established cognitive process models of memory.