根据多种替代方案,基底格里的决策值学习.
Thom Griffith1, Sophie-Anne Baker2, Nathan F Lepora3
1School of Engineering Mathematics and Technology, University of Bristol, Bristol, BS8 1TW, U.K.; and School of Psychology, Cardiff University, Cardiff, CF10 3AT, U.K. thom.griffith@bristol.ac.uk.
Neural computation
|May 27, 2025
概括
本研究引入了一种新的决策模型,将强化学习 (RL) 和多选项的证据积累集成在一起. 它增强了灵活性,并与基底腺中的大脑机制联系在一起.
科学领域:
- 计算神经科学是一种神经科学.
- 认知科学 认知科学
- 决策科学 决策科学 决策科学
背景情况:
- 强化学习 (RL) 和证据积累模型,如扩散决策模型 (DDM),在历史上是分开的,但越来越多的集成.
- RL-DDM将价值学习与DDM相结合,但在多选项决策和环境灵活性方面存在困难.
- 目前的模型缺乏对神经物理过程的明确映射,特别是在基底腺节的去/不去决策中.
研究的目的:
- 提出一种新的决策模型,解决现有的RL-DDM方法的局限性.
- 扩展决策建模以处理多个选择和取决于背景的环境变化.
- 将决策的计算模型与基底腺中的神经生物学机制联系起来.
主要方法:
- 结合了多选择序列概率比测试 (MSPRT) 决策模型与双路径基底腺值学习模型.
- 开发了一个学习决策值的模型,以平衡时间成本和错误成本.
- 嵌入式上下文依赖性,以灵活适应速度精度权衡 (SAT) 变化.
主要成果:
- 拟议的模型有效地扩展到具有多个选项的决策.
- 它在适应奖励环境的快速,背景调整的变化方面表现出灵活性.
- 该模型复制了基于价值的决策中观察到的量效应,并且适用于各种证据类型.
结论:
- 这种新型模型成功地整合了RL和DDM概念,用于多选项决策.
- 它提供了一种神经生理学上可信的解释,描述了基底质中决策值的学习.
- 这项工作弥合了单独的研究领域,将RL-DDM与多巴胺激应动机和风险承担模型联系起来.
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