面对面的枢纽利用概率表达和集成的不确定性来引导认知灵活性
bioRxiv : the preprint server for biology
|September 5, 2025
概括
大脑使用不同的神经区域来整合内部的信念和外部的线索. 通过动态调整大脑连接, 实现灵活的行为.
科学领域:
- 神经科学
- 认知科学
- 计算模型
背景情况:
- 适应性行为依赖于从多个来源整合信息,通常是随机和不确定的.
- 整合内部和外部噪音信息的神经和计算机制在很大程度上是未知的.
研究的目的:
- 解释大脑系统如何整合内部维护和外部提示的随机信息来指导行为.
- 为分析信息整合引入计算神经成像框架.
主要方法:
- 收集了健康成年人的神经成像数据.
- 开发了一个计算模型来估计试验对试验的信念,并将其整合到联合概率分布中.
- 使用联合分布的来追踪信念,预测错误和更新动态的量化不确定性.
主要成果:
- 不同的大脑区域编码潜伏状态的信念 (前部中额头,中背丘脑,下叶) 和感知信念 (侧侧角部区域,内角部,前中心部).
- 整合的信息和它的汇聚在前侧侧 (中间前侧,内侧).
- 用感觉,运动,前运动和预测错误电路进行适应性重新配置的连接.
结论:
- 面对面的中心集成并解决分布式不确定性以指导灵活的行为.
- 这些发现揭示了前端对象系统如何实现多个噪音输入的认知集成.
- 不同的神经群体对内部和外部信息进行编码,然后由前端平行网络集成.
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