利用贝叶斯信息增益建模认知情绪的唤醒潜力：由自由能波动驱动的探究循环
Modeling arousal potential of epistemic emotions using Bayesian information gain: Inquiry cycle driven by free energy fluctuations
AI总结：
本研究基于自由能最小化原理的两类信息增益构建认知情绪的数学模型，发现其构成类似冯特曲线的上凸函数，揭示认知情绪驱动的理想探究循环机制，统一大脑自由能原理与唤醒潜力理论。
摘要：
认知情绪（如好奇心和兴趣）驱动着探究过程。本研究提出了一种利用自由能最小化原理产生的两类信息增益来构建好奇心、兴趣等认知情绪的新公式：从贝叶斯后验到先验的库尔贝克-莱布勒散度（Kullback-Leibler divergence, KLD），代表识别过程中的自由能减少；以及贝叶斯惊奇（Bayesian surprise, BS），代表通过贝叶斯先验更新获得的预期信息增益。通过应用带有附加均匀似然的高斯生成模型，我们发现KLD和BS构成了惊奇（最小化自由能与预测误差）的上凸函数，类似于贝里内（Berlyne）的唤醒潜力函数，即冯特曲线（Wundt curve）。我们认为，BS和KLD的交替最大化会生成一个理想的探究循环，通过惊奇的波动趋近最优唤醒水平，而好奇心和兴趣则起到推动该循环进程的作用。我们详尽分析了预测不确定性（先验方差）和观测不确定性（似然方差）对作为最优惊奇的信息增益函数峰值的影响。结果表明，更高的预测不确定性（即开放的心态）和更低的观测不确定性（即集中注意力的精确观测），有望通过更大范围的探索带来更高的信息增益。所提出的数学框架统一了大脑的自由能原理与唤醒潜力理论，将冯特曲线解释为一种信息增益函数，并提出了一种由认知情绪驱动的理想探究过程。
英文摘要：
Epistemic emotions, such as curiosity and interest, drive the inquiry process. This study proposes a novel formulation of epistemic emotions such as curiosity and interest using two types of information gain generated by the principle of free energy minimization: Kullback-Leibler divergence(KLD) from Bayesian posterior to prior, which represents free energy reduction in recognition, and Bayesian surprise (BS), which represents the expected information gain by Bayesian prior update. By applying a Gaussian generative model with an additional uniform likelihood, we found that KLD and BS form an upward-convex function of surprise (minimized free energy and prediction error), similar to Berlyne's arousal potential functions, or the Wundt curve. We consider that the alternate maximization of BS and KLD generates an ideal inquiry cycle to approach the optimal arousal level with fluctuations in surprise, and that curiosity and interest drive to facilitate the cyclic process. We exhaustively analyzed the effects of prediction uncertainty (prior variance) and observation uncertainty (likelihood variance) on the peaks of the information gain function as optimal surprises. The results show that greater prediction uncertainty, meaning an open-minded attitude, and less observational uncertainty, meaning precise observation with attention, are expected to provide greater information gains through a greater range of exploration. The proposed mathematical framework unifies the free energy principle of the brain and the arousal potential theory to explain the Wundt curve as an information gain function and suggests an ideal inquiry process driven by epistemic emotions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
