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Learning to use past evidence in a sophisticated world model

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Dayan,  P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Ahilan, S., Solomon, R., Breton, A.-Y., Conover, K., Niyogi, R., Shizgal, P., et al. (2019). Learning to use past evidence in a sophisticated world model. PLoS Computational Biology, 15(6), 1-20. doi:10.1371/journal.pcbi.1007093.


Cite as: http://hdl.handle.net/21.11116/0000-0003-DDC3-8
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 evidence from the past. 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 improved integration of past evidence. This learning reflected the structure of the task, decreasing reliance on recent observations, which were potentially misleading.