Effects of retrospective inference on structure learning: a simulation study

The ability to flexibly learn the structure of one’s surroundings (structure learning) is crucial for adaptive behaviour. Use of an inaccurate model of the environment can lead to incorrect inferences, and thus maladaptive actions. Despite this, relatively little is understood about how structure learning occurs in human cognition, with only a handful of experiments investigating the phenomenon. To address this, we built on existing approaches to create an online clustering algorithm, in which we included a working memory component, allowing belief update about past stimuli (retrospective inference) in contrast with the more widespread fully online approach. We used this model to simulate behaviour on a novel structure learning task, where optimal performance required estimating the number and properties of discrete clusters of continuous stimuli. In this work we show how our algorithm outperforms a parametric one (i.e. with fixed number of clusters). We further demonstrate how retrospective inference benefits structure learning, with performance increasing with working memory capacity. We finally discuss trial-by trial measures that can be derived from our model, which provide testable predictions for future empirical studies.