Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.
Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations.
复制标题
不确定性下的觅食遵循边际值定理和环境表示的贝叶斯更新。
DOI：
10.1101/2024.03.30.587253
复制
发表时间：
2024
期刊：
影响因子：
--
通讯作者：
McGinley,Matthew
中科院分区：
文献类型：
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作者：
Webb,James;Steffan,Paul;Hayden,BenjaminY;Lee,Daeyeol;Kemere,Caleb;McGinley,Matthew
觅食理论是在许多情况下理解动物行为的一种非常成功的方法。在基于斑块的觅食环境中，边际价值定理(MVT)表明，当边际收益率下降到环境的平均值时，最优策略是离开斑块。然而，MVT只在采集者已知其统计数据的确定性环境中有效；自然主义环境很少满足这些严格的要求。因此，必须对自然环境中觅食者使用的策略进行实证研究。我们开发了一种新的行为任务和相应的计算框架，用于研究头部固定和自由移动的小鼠的斑块离开决定。我们改变了补丁之间的旅行时间，以及补丁内的奖励耗尽率，既有确定性的，也有随机的。我们发现，小鼠采用补丁停留时间的方式与MVT一致，不能用简单的行为学动机启发式策略来解释。最重要的是，MVT的一种改进形式最好地解释了行为，其中环境表征基于由贝叶斯估计器和动态先验捕获的奖励时间的局部变化来更新。因此，我们表明，小鼠可以战略性地同时关注、学习和开发多个时间尺度上的任务结构，从而有效地在动荡的环境中觅食。这些结果为将系统神经科学工具包应用于自由活动和头部固定的小鼠以了解在不确定条件下觅食的神经基础提供了基础。
Foraging theory has been a remarkably successful approach to understanding the behavior of animals in many contexts. In patch-based foraging contexts, the marginal value theorem (MVT) shows that the optimal strategy is to leave a patch when the marginal rate of return declines to the average for the environment. However, the MVT is only valid in deterministic environments whose statistics are known to the forager; naturalistic environments seldom meet these strict requirements. As a result, the strategies used by foragers in naturalistic environments must be empirically investigated. We developed a novel behavioral task and a corresponding computational framework for studying patch-leaving decisions in head-fixed and freely moving mice. We varied between-patch travel time, as well as within-patch reward depletion rate, both deterministically and stochastically. We found that mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. Critically, behavior was best accounted for by a modified form of the MVT wherein environment representations were updated based on local variations in reward timing, captured by a Bayesian estimator and dynamic prior. Thus, we show that mice can strategically attend to, learn from, and exploit task structure on multiple timescales simultaneously, thereby efficiently foraging in volatile environments. The results provide a foundation for applying the systems neuroscience toolkit in freely moving and head-fixed mice to understand the neural basis of foraging under uncertainty.