食动物使用动态贝叶斯更新来建模环境表示中的元不确定性.
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
概括
小鼠的食行为适应不断变化的环境. 他们使用一个层次化的贝叶斯策略来处理不确定性,通过跟踪本地和全球环境统计数据,在波动条件下优化决策.
科学领域:
- 行为生态学 行为生态学
- 计算神经科学是一种神经科学.
- 决策科学 决策科学 决策科学
背景情况:
- 食理论,包括边际值定理 (MVT),在可预测的环境中模拟最佳的补丁离开策略.
- 由于可变参数和不可预测的统计变化,自然环境存在不确定性,从而产生元不确定性.
- 了解动物在元不确定性下寻找食物的策略及其神经基础在很大程度上是未知的.
研究的目的:
- 在元不确定性条件下研究小鼠的贴片离开决策.
- 开发一种新的行为任务和计算框架,用于在不断变化的环境统计数据下研究食.
- 阐明在不稳定环境中适应性食的基础上的认知和神经机制.
主要方法:
- 为固定头部和自由移动的小鼠开发了一种新的行为任务,涉及补丁之间的旅行时间和补丁内奖励耗尽率的随机变化.
- 应用了计算框架来模拟在不同级别的第一阶段 (局部可变性) 和第二阶段 (全球统计) 不确定性下的食行为.
- 分析了补丁存在时间,并将其与边际值定理和启发式策略的预测进行了比较.
主要成果:
- 在低不确定性条件下,小鼠的行为与MVT保持一致,超过了简单的启发式策略.
- 在高度可变的环境中,老鼠的行为最好通过一个分层贝叶斯模型来解释,该模型包含了本地变化和动态全球统计数据.
- 这表明小鼠使用复杂的贝叶斯推理来管理在寻找食物期间的元不确定性.
结论:
- 小鼠利用一个层次化的贝叶斯策略,在具有元不确定性的挥发性环境中有效地寻找食物.
- 这种适应性策略使动物能够区分环境波动和统计属性的真正变化.
- 这些发现为探索自然主义不确定性下决策的神经基础提供了基础.
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