Scientists often use observational time series data to study complex natural processes, but regression analyses often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to the performance of models of complex processes, but deep learning is generally not used for scientific analysis. Here we show that deep learning can be used to analyze complex processes, providing flexible function approximation while preserving interpretability. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many natural systems and may critically affect the interpretation of data. We evaluate our model on incremental human language processing, a domain with complex continuous dynamics. We demonstrate substantial improvements on behavioral and neuroimaging data, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions that are otherwise hard to study.

翻译：科学家常利用观测时间序列数据研究复杂的自然过程，但回归分析通常假设简单的动力学机制。深度学习的近期进展显著提升了复杂过程模型的性能，然而深度学习通常不用于科学分析。本文证明，深度学习可被用于分析复杂过程，在保持可解释性的同时提供灵活的函数逼近。我们的方法放宽了标准简化假设（如线性、平稳性和同方差性），这些假设对许多自然系统而言并不成立，且可能严重影响数据解读。我们在增量式人类语言处理这一具有复杂连续动态特征的领域评估了模型。我们展示了在行为与神经影像数据上的显著改进，并证明该模型能在探索性分析中发现新范式、在验证性分析中控制多种混杂因素，并开辟了其他方法难以研究的科学问题。