灵活感知推理的神经机制
John Schwarcz1, Jan Bauer1,2, Haneen Rajabi1
1Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel.
PLoS computational biology
|December 1, 2025
概括
小鼠通过推断基本情况而迅速适应不断变化的环境,而不仅仅是从奖励中推断. 这种灵活的感知推断依赖于更新内部信念,这种机制也在人工神经网络中见到.
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:
- 情境依赖推理对于理解感官信息至关重要.
- 大脑推断上下文的能力,尤其是在动态转移期间,仍然是一个关键的研究问题.
研究的目的:
- 调查灵活的感知推理,大脑快速适应隐性上下文变化的能力.
- 了解大脑如何同时推断感官意义和动态背景.
主要方法:
- 为小鼠在动态环境中开发了一种新的变化检测任务.
- 模拟了贝叶斯-最佳政策,使用部分可观察的马尔科夫决策过程.
- 训练有素的人工神经网络 (ANN) 使用强化学习来模仿自适应推理.
主要成果:
- 小鼠表现出第一次试验的行为适应潜伏的上下文转移,由推理驱动而不是奖励反.
- 贝叶斯推理,通过连续更新内部信念状态,是快速适应的基础.
- 在它们的反复动态中,ANN实现了近乎最佳的性能,利用贝叶斯推理类机制.
结论:
- 灵活的感知推断是认知灵活性的一个基本方面.
- 连续的信念状态更新是不确定的环境中实时适应性推理的关键.
- 这些发现提供了对大脑和ANN如何适应不断变化的环境的计算和神经洞察力.
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