解码神经活动以评估生态有效情境中的个体潜在状态
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts
AI总结：
本研究利用脑机接口领域的领域泛化方法，将实验室训练的模型应用于生态有效的驾驶任务，验证了神经活动模式可准确推断个体潜在状态，并为实验室研究提供生态有效性支持。
摘要：
在更具生态有效的情境中，隔离认知过程的方法非常少，这些认知过程历来是通过高度受控的实验室研究来定义的。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出现在实验室之外，并能够用于对个体的潜在状态、相关认知过程或近期行为进行准确推断。提高我们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，将为研究类似神经现象隔离的实验室方法提供验证，并为我们对复杂任务中出现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领域泛化方法有潜力解决这一挑战。我们之前曾使用这种方法来解码与视觉目标辨别相关的阶段性神经反应。在此，我们将该工作扩展到更持续的现象，如内部潜在状态。我们使用来自两个高度受控的实验室范式的数据来训练两个独立的领域泛化模型。我们将训练好的模型应用于一个生态有效的范式，在该范式中，参与者执行了多项并行的驾驶相关任务。使用预训练模型，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，随着神经活动模式沿着原始训练数据定义的轴变化，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提供了生态有效性，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。
英文摘要：
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.
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