There exist very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make an accurate inference about the latent state, associated cognitive process, or proximal behavior of the individual. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks. We argue that domain generalization methods from the brain-computer interface community have the potential to address this challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks. Using the pretrained models, we derive estimates of the underlying latent state and associated patterns of neural activity. Importantly, as the patterns of neural activity change along the axis defined by the original training data, we find changes in behavior and task performance consistent with the observations from the original, laboratory paradigms. We argue that these results lend ecological validity to those experimental designs and provide a methodology for understanding the relationship between observed neural activity and behavior during complex tasks.

翻译：目前，在更具生态有效性的情境中分离认知过程的方法极为有限，而这些认知过程传统上是通过高度受控的实验室研究定义的。具体而言，尚不清楚在这种约束条件下观察到的神经活动模式在多大程度上实际上在实验室外显现，并可用于对个体的潜在状态、相关认知过程或近端行为做出准确推断。提高对特定神经活动模式在生态有效场景中何时以及如何显现的理解，将为实验室分离研究类似神经现象的方法提供验证，并为复杂任务期间发生的潜在状态提供有意义的见解。我们认为，来自脑机接口社区的域泛化方法有潜力解决这一挑战。我们此前曾使用该方法解码与视觉目标辨别相关的相位神经反应。在此，我们将该工作扩展到诸如内部潜在状态等更为紧张性的现象。我们使用来自两个高度受控实验室范式的数据训练两个独立的域泛化模型，并将训练后的模型应用于一个生态有效范式，在该范式中参与者同时执行多项驾驶相关任务。利用预训练模型，我们推导出潜在状态及其相关神经活动模式的估计值。重要的是，当神经活动模式沿原始训练数据定义的轴发生变化时，我们发现行为和任务表现的变化与原始实验室范式的观察结果一致。我们认为，这些结果为这些实验设计赋予了生态有效性，并提供了一种理解复杂任务中观察到的神经活动与行为之间关系的方法。