Forgetful inference in a sophisticated world model

Abstract

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 learning to remember decreased reliance on recent observations, which may be misleading, in favour of a longer-term memory.\n\nAuthor summaryHumans and other animals possess the remarkable ability to find and exploit patterns and structures in their experience of a complex and varied world. However, such structures are often temporally extended and latent or hidden, being only partially correlated with immediate observations of the world. This makes it essential to integrate current and historical information, and creates a challenging statistical and computational problem.\n\nHere, we examine the behaviour of rats facing a version of this challenge posed by a brain-stimulation reward task. We find that subjects learned the general structure of the task, but struggled when immediate observations were misleading. We captured this behaviour with a model in which subjects integrated evidence from their observations together with a memory whose imperfections accounted for their errors.\n\nThe subjects performance improved markedly over successive sessions, allowing them to overcome misleading observations. According to the model, this arose from a process of learning to remember in which subjects became better at employing more reliable past observations to determine the hidden state of the world.