Modeling movement in real-world tasks is a fundamental scientific goal. However, it is unclear whether existing models and their assumptions, overwhelmingly tested in laboratory-constrained settings, generalize to the real world. For example, data-driven models of foot placement control -- a crucial action for stable locomotion -- assume linear and single timescale mappings. We develop nonlinear foot placement prediction models, finding that neural network architectures with flexible input history-dependence like GRU and Transformer perform best across multiple contexts (walking and running, treadmill and overground, varying terrains) and input modalities (multiple body states, gaze), outperforming traditional models. These models reveal context- and modality-dependent timescales: there is more reliance on fast-timescale predictions in complex terrain, gaze predictions precede body state predictions, and full-body state predictions precede center-of-mass-relevant predictions. Thus, nonlinear action prediction models provide quantifiable insights into real-world motor control and can be extended to other actions, contexts, and populations.

翻译：对现实任务中的运动进行建模是一个基础科学目标。然而，现有模型及其假设（绝大多数在实验室受限环境中测试）是否能推广到现实世界尚不清楚。例如，足部放置控制（稳定运动的关键动作）的数据驱动模型通常假设线性和单一时间尺度的映射关系。我们开发了非线性足部放置预测模型，发现具有灵活输入历史依赖性的神经网络架构（如GRU和Transformer）在多种情境（行走与跑步、跑步机与地面、不同地形）和输入模态（多种身体状态、视线）中表现最佳，优于传统模型。这些模型揭示了情境和模态依赖的时间尺度：在复杂地形中更依赖快速时间尺度的预测，视线预测先于身体状态预测，而全身状态预测又先于质心相关预测。因此，非线性动作预测模型为现实世界运动控制提供了可量化的见解，并可扩展到其他动作、情境和人群。