Forgetful inference in a sophisticated world model

Humans and other animals are able to discover underlying statistical structure in their environments and exploit it to achieve efficient and effective performance. However, such structure is often difficult to learn and use because it is obscure, involving long- range temporal dependencies. Here, we analysed behavioural data from an extended experiment with rats, showing that the subjects learned the underlying statistical structure, albeit suffering at times from immediate inferential imperfections as to their current state within it. We accounted for their behaviour using a Hidden Markov Model, in which recent observations are integrated with the recollections of an imperfect memory. We found that over the course of training, subjects came to track their progress through the task more accurately, a change that our model largely attributed to decreased forgetting. This 9learning to remember9 decreased reliance on recent observations, which may be misleading, in favour of a longer-term memory.