模拟焦点状态和思维漫游之间的自主转移,使用预测编码启发的变量循环神经网络
Henrique Oyama1, Takazumi Matsumoto1, Jun Tani1
1Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Onna, Japan.
Frontiers in computational neuroscience
|July 17, 2025
概括
这项研究模拟了集中注意力和思维游荡 (MW) 之间的自主转移. 一个新的meta-prior参数通过平衡自上而下的预测和自下而上的感觉来调节转变,提供对认知状态的见解.
科学领域:
- 认知神经科学 认知神经科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 心灵流浪 (MW) 涉及集中注意力和非任务状态之间的动态转移.
- 了解焦点状态 (FS) 和MW之间的自主转移的神经机制对于认知科学至关重要.
- 现有的模型缺乏对这些神经支的全面解释.
研究的目的:
- 研究驱动FS和MW之间自主转换的神经机制.
- 提出基于预测编码和自由能量的最小化计算模型.
- 探索元前参数在调节这些认知状态过渡中的作用.
主要方法:
- 利用基于预测编码原则的变异性循环神经网络 (RNN) 模型.
- 在自由能量最小化下对连续感官序列的模拟感知.
- 引入并操纵在线自适应元前参数 (w) 来调节模型复杂性.
主要成果:
- 由元前参数 (w) 驱动的FS和MW之间的自主转移.
- 显示低"w"优先考虑自下而上的感官输入,而高"w"强调自上而下的预测.
- 观察到,随着时间的推移,转移与平均重建误差的变化相关.
结论:
- 拟议的模型提供了一个可信的神经机制,用于在集中注意力和思维漫游之间进行自主过渡.
- 超前参数 (w) 作为一个关键的调节器,影响感官证据和内部预测之间的平衡.
- 推测,累积的预测错误超过一个值可能是精神游荡的自我意识的基础.
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