在一个多维推理任务中,神经对信念的表现
bioRxiv : the preprint server for biology
|January 9, 2026
概括
这项研究揭示了大脑如何使用贝叶斯推理更新关于世界的信念. 跨多个大脑区域的神经活动在学习和决策过程中灵活地表示和更新这些概率信念.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 适应性行为依赖于更新概率学信念.
- 在分布式大脑电路中进行这些计算的神经机制尚不清楚.
研究的目的:
- 调查分布式大脑电路如何实现概率学信念更新.
- 在推断过程中识别信念和信心的神经表示.
主要方法:
- 记录了子大脑六个区域的1400多个神经元的神经活动.
- 使用了涉及试错学习的多维推理任务.
- 使用贝叶斯的信念更新来建模行为.
主要成果:
- 刺激,奖励和潜伏的信念的神经表现在大脑区域广泛分布.
- 人口神经活动的变化反映了贝叶斯的信念更新,将以前的信念与新证据整合在一起.
- 识别了独立于特定信念的自信表示,具有独特的时间动态.
结论:
- 概率推理源于分布式大脑系统之间协调的动态.
- 不同的大脑区域灵活地为基于计算需求的信念更新做出贡献.
- 在学习和决策过程中,信念和信心的神经表现会动态更新.
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