ArticlePLoS computational biology2020
Value-complexity tradeoff explains mouse navigational learning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Understanding dual process cognition via the minimum description length principle.PLoS computational biology · 2024Article
- Human decision making balances reward maximization and policy compression.PLoS computational biology · 2024Article
- Bayesian Reinforcement Learning With Limited Cognitive Load.Open mind : discoveries in cognitive science · 2024Article
- Humans account for cognitive costs when finding shortcuts: An information-theoretic analysis of navigation.PLoS computational biology · 2023Article
- Learning, fast and slow.Current opinion in neurobiology · 2022Review
- Extrinsic rewards, intrinsic rewards, and non-optimal behavior.Journal of computational neuroscience · 2022Article
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Authors and funding
6 authors.
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Abstract
We introduce a novel methodology for describing animal behavior as a tradeoff between value and complexity, using the Morris Water Maze navigation task as a concrete example. We develop a dynamical system model of the Water Maze navigation task, solve its optimal control under varying complexity constraints, and analyze the learning process in terms of the value and complexity of swimming trajectories. The value of a trajectory is related to its energetic cost and is correlated with swimming time. Complexity is a novel learning metric which measures how unlikely is a trajectory to be generated by a naive animal. Our model is analytically tractable, provides good fit to observed behavior and reveals that the learning process is characterized by early value optimization followed by complexity reduction. Furthermore, complexity sensitively characterizes behavioral differences between mouse strains.
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