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在默认模式网络中的神经可变性在贝叶斯推理过程中以越来越高的信念精度进行压缩.
Alexander Skowron1,2,3, Julian Q Kosciessa1,2,4, Robert C Lorenz5
1Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Lentzeallee 94, 14195 Berlin, Germany, and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, 10-12 Russell Square, London WC1B 5Eh, United Kingdom.
Cerebral cortex (New York, N.Y. : 1991)
|September 19, 2025
概括
神经变异性随着学习提高信念精度而压缩. 更准确的学习者在默认模式网络区域中显示出更大的压缩,反映出对环境状态的精细理解.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 决策 决策 决策 决策
背景情况:
- 智能代理需要从观测中学习潜在的环境状态,以进行最佳决策.
- 贝叶斯框架表明,随着时间的推移,证据整合通过减少不确定性来完善状态信念.
- 在学习过程中增加信念精度的基础的神经机制仍然不清楚.
研究的目的:
- 为了研究如何在学习过程中增加信念精度反映在神经活动中.
- 测试时间神经变化在学习过程中随着不确定性减少而变化.
- 探索神经可变性,学习准确性和先前信念之间的关系.
主要方法:
- 功能性磁共振成像 (fMRI) 用于测量47名健康成年人的血液氧气水平依赖 (BOLD) 信号变化 (SDBOLD).
- 分析SDBOLD在一个学习任务的连续试验中,涉及与决策相关的证据.
- 参与者行为的计算建模,以评估先前的信念和减少不确定性.
主要成果:
- BOLD信号变异性 (SDBOLD) 压缩,增加了与决策相关证据的接触.
- 在更准确的参与者中观察到更大的SDBOLD压缩,特别是在默认模式的网络区域.
- 计算模型表明,准确的学习者有更平坦的先前信念,使得更大的不确定性减少反映在SDBOLD变化中.
结论:
- 神经可变性压缩,因为在有效学习过程中,信念精度增加.
- 这种压缩,特别是在默认模式的网络区域,可能反映了大脑对潜在状态信念的改进.
- 研究结果提出了一种神经机制,通过灵活的不确定性降低来学习环境的概率性.
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