用变分RNN模拟注意力与走神的自动切换机制。
Modeling Autonomous Shifts Between Focus State and Mind-Wandering Using a Predictive-Coding-Inspired Variational RNN Model
- 引入元先验参数调节预测复杂度，驱动状态切换。
- 高值元先验强化自上而下预测，低值则侧重自下而上感知。
- 结果符合神经科学发现，适合认知建模研究者参考。
本研究通过模型仿真实验探讨了注意力集中与走神之间自主切换的潜在神经机制。基于自由能原理，我们采用先前提出的变分RNN模型，对连续感官序列的感知过程进行建模，并引入一个元层级参数（元先验 𝐰），用于调节自由能中的复杂度项。仿真结果显示，当 𝐰 在低值与高值间切换时，系统会自发在专注感知与走神状态间转换；高 𝐰 值对应平均重建误差上升，优先强调自上而下的预测；低 𝐰 值则对应重建误差下降，更关注自下而上的感觉输入。本文讨论结果与现有研究的一致性，并指出其对未来认知神经科学的启示。
原文摘要 · Abstract (English)
The current study investigates possible neural mechanisms underling autonomous shifts between focus state and mind-wandering by conducting model simulation experiments. On this purpose, we modeled perception processes of continuous sensory sequences using our previous proposed variational RNN model which was developed based on the free energy principle. The current study extended this model by introducing an adaptation mechanism of a meta-level parameter, referred to as the meta-prior $\mathbf{w}$, which regulates the complexity term in the free energy. Our simulation experiments demonstrated that autonomous shifts between focused perception and mind-wandering take place when $\mathbf{w}$ switches between low and high values associated with decrease and increase of the average reconstruction error over the past window. In particular, high $\mathbf{w}$ prioritized top-down predictions while low $\mathbf{w}$ emphasized bottom-up sensations. This paper explores how our experiment results align with existing studies and highlights their potential for future research.
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