Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations
James Webb1,2, Paul Steffan1, Benjamin Y Hayden3
1Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America.
Plos Computational Biology
|April 30, 2025
Summary
Mice foraging behavior adapts to changing environments. They use a hierarchical Bayesian strategy to handle uncertainty, optimizing decisions in volatile conditions by tracking both local and global environmental statistics.
Area of Science:
- Behavioral Ecology
- Computational Neuroscience
- Decision Science
Background:
- Foraging theory, including the marginal value theorem (MVT), models optimal patch-leaving strategies in predictable environments.
- Natural environments present uncertainty due to variable parameters and unpredicted statistical changes, creating meta-uncertainty.
- Understanding animal strategies for foraging under meta-uncertainty and their neural basis is largely unknown.
Purpose of the Study:
- To investigate patch-leaving decisions in mice under conditions of meta-uncertainty.
- To develop a novel behavioral task and computational framework for studying foraging under changing environmental statistics.
- To elucidate the cognitive and neural mechanisms underlying adaptive foraging in volatile environments.
Main Methods:
- Developed a novel behavioral task for head-fixed and freely moving mice involving stochastic variation of between-patch travel time and within-patch reward depletion rate.
- Applied a computational framework to model foraging behavior under varying levels of first-order (local variability) and second-order (global statistics) uncertainty.
- Analyzed patch residence times and compared them to predictions from the marginal value theorem and heuristic strategies.
Main Results:
- Mice behavior aligned with the MVT in low-uncertainty conditions, outperforming simple heuristic strategies.
- In highly variable environments, mouse behavior was best explained by a hierarchical Bayesian model incorporating both local variability and dynamic global statistics.
- This suggests mice employ sophisticated Bayesian inference to manage meta-uncertainty during foraging.
Conclusions:
- Mice utilize a hierarchical Bayesian strategy to forage efficiently in volatile environments with meta-uncertainty.
- This adaptive strategy allows animals to distinguish between environmental fluctuations and genuine changes in statistical properties.
- The findings provide a foundation for exploring the neural basis of decision-making under naturalistic uncertainty.
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