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Animals continuously infer latent properties of the world from noisy and changing observations. Complex approaches to this challenge such as Bayesian inference are accurate but cognitively demanding, requiring extensive working memory and adaptive learning. Simple strategies such as always using a prior bias or following the last observation are easy to implement but may be less accurate. What is the appropriate balance between complexity and accuracy? We construct a hierarchy of strategiesdoi:10.1101/563346 fatcat:bwgfnxsyk5d5rjupyskx2nsgnq