Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model classes. In this work, we provide the first experimental validation of superstatistics and formal comparison of four non-stationary diffusion decision models in a specifically designed perceptual decision-making task. Task difficulty and speed-accuracy trade-off were systematically manipulated to induce expected changes in model parameters. To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations. To address computational challenges, we present novel deep learning techniques for amortized Bayesian estimation and comparison of models with time-varying parameters. Our findings indicate that transition models incorporating both gradual and abrupt parameter shifts provide the best fit to the empirical data. Moreover, we find that the inferred parameter trajectories closely mirror the sequence of experimental manipulations. Posterior re-simulations further underscore the ability of the models to faithfully reproduce critical data patterns. Accordingly, our results suggest that the inferred non-stationary dynamics may reflect actual changes in the targeted psychological constructs. We argue that our initial experimental validation paves the way for the widespread application of superstatistics in cognitive modeling and beyond.

翻译：认知过程在不同时间尺度上经历着各种波动与瞬态变化。超统计学作为一种灵活框架，正逐渐被用于将此类非平稳动力学纳入现有认知模型类别。本研究首次通过实验验证了超统计学方法，并在专门设计的知觉决策任务中对四种非平稳扩散决策模型进行了形式化比较。我们通过系统操纵任务难度与速度-准确率权衡来诱导模型参数的预期变化。为验证模型，我们评估了推断出的参数轨迹是否与实验操纵的模式及序列相吻合。针对计算挑战，我们提出了新颖的深度学习技术，用于实现时变参数模型的摊销贝叶斯估计与比较。研究结果表明：同时包含渐进性与突变性参数转移的过渡模型对实证数据的拟合最优。此外，我们发现推断出的参数轨迹能准确映射实验操纵的序列。后验重模拟进一步凸显了模型忠实再现关键数据模式的能力。因此，我们的研究结果表明推断出的非平稳动力学可能反映了目标心理构念的真实变化。我们认为这项开创性实验验证为超统计学在认知建模及其他领域的广泛应用铺平了道路。