奖励预测错误的不对称编码在人类insula和dorsomedial前额叶皮层中
Colin W Hoy1,2, David R Quiroga-Martinez3,4, Eduardo Sandoval3
1Department of Neurology, University of California, San Francisco, San Francisco, CA, USA. colin.hoy@ucsf.edu.
Nature communications
|December 21, 2023
概括
这项研究揭示了大脑如何通过在insula (INS) 和dorsomedial prefrontal cortex (dMPFC) 中的不对称编码来处理奖励预测错误 (RPEs). 独特的神经群体编码RPE突出和价值,告知强化学习 (RL) 和认知控制.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 认知神经科学 认知神经科学
背景情况:
- 奖励预测错误 (RPEs) 对于强化学习 (RL) 和认知控制至关重要.
- 背中介前额叶皮层 (dMPFC) 和胰岛 (INS) 参与处理RPE,但它们的特定编码机制仍在争论中.
- 最近的计算模型表明神经元可以对积极和消极的RPE表现出不对称的反应.
研究的目的:
- 调查非对称编码策略是否解释了人类INS和dMPFC中RPE表示的多样性.
- 检查不同神经群体在编码RPE突出和价值中的作用.
- 为了确定INS和dMPFC在RPE处理中的方向影响.
主要方法:
- 人类参与者的高频活动 (HFA) 的内记录.
- 对dMPFC和INS中签名和未签名的RPE的神经反应的分析.
- 定向连接性估计,以推断大脑区域之间的信息流.
主要成果:
- dMPFC和INS都显示出对积极的RPE的区域偏差,反映了行为适应.
- 在这些区域内的空间分离的神经群体对未标记的RPE突出性和价值特异性RPEs做出了反应.
- INS在向dMPFC传输积极和未签名的RPE信息方面发挥了主导作用.
结论:
- 跨不同神经群体的不对称编码是人类大脑中RPE处理的基本原则.
- 这些发现提供了关于dMPFC和INS在强化学习和认知控制中的功能性作用的见解.
- 这项研究支持一种模型,其中混合的神经回路灵活地表示RPE.
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