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混合策略是逆向学习期间动物行为的基础

Nhat Minh Le^1,2, Murat Yildirim^2,3, Yizhi Wang²

¹Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

PLoS computational biology

|September 14, 2023

概括

在反向学习任务中,小鼠表现出杂的,低于最佳的选择,即使是专家. 这种行为源于高失误率和不同学习策略的混合,而不是统一的执行.

科学领域:

神经科学是一个神经科学.
计算生物学计算生物学
动物行为动物行为

背景情况:

反向学习任务通常假定分析的统一行为.
然而,由于任务动态的变化,会话中可能会发生显著的行为变化.

研究的目的:

调查在决定性反转学习任务期间小鼠中低于最佳和杂的选择过渡的来源.
模拟和理解小鼠使用的行为策略的混合.

主要方法:

在决定性反转学习任务中观察小鼠行为.
对失效率和选择过渡的分析.
区块隐藏马尔科夫模型 (区块HMM) 的应用来量化行为模式的混合.
无模型和基于推理的学习算法的比较.

主要成果:

小鼠表现出噪音和低于最佳的选择过渡,包括高失效率.
大多数小鼠混合了不同的行为模式与不同的过渡动态.
区块HMM成功分离了这些混合物.
行为模式与无模型和基于推理的学习算法联系在一起.

结论:

由于高失误率和探索性 (无模型) 和确定性 (基于推理) 策略的混合,老鼠在反向学习中表现出内在的噪音.
这种计算方法在反向学习任务中提供了对动物行为的更细致的理解.
发现了区块对区块的转换背后的隐藏状态.

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