在学习过程中,上下文依赖的偏见决策的几何结构
bioRxiv : the preprint server for biology
|February 6, 2026
概括
子通过推断隐藏的信息,快速调整决策以适应不断变化的奖励环境. 前额叶皮层中的神经表征以几何方式转移,使灵活的,取决于背景的选择和快速学习成为可能.
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:
- 适应性行为依赖于推断环境背景和调整决策.
- 了解奖励环境是如何学习,表示和更新的,对于决策研究至关重要.
研究的目的:
- 研究奖励背景推断和决策适应的基础神经机制.
- 检查侧面前额叶皮质 (LPFC) 如何表示和更新不断变化的奖励突发事件.
主要方法:
- 在的LPFC中记录了大批神经元的神经活动,这些神经元是在方向区分任务中记录的.
- 任务涉及到奖励意外情况的不可预测的交替,需要上下文推断.
- 使用表示几何学分析了神经群体的活动,并与反复的神经网络模型进行了比较.
主要成果:
- 子展示了对上下文交换机的一次试验推断,相应地调整选择和反应时间.
- 神经表征显示了跨背景的系统几何变化,主要沿着决策变量轴.
- 学习加速了这些表示过渡的速度,与更快的行为概括相关.
结论:
- LPFC 电路通过在表示几何学中的结构化转移来编码奖励上下文来支持等级推断.
- 这种几何编码允许依赖上下文的偏见和灵活控制决策政策.
- 结果提供了一个快速概括和适应在不断变化的环境下决策的机制.
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