Predicting Secondary Task Performance: A Directly Actionable Metric for Cognitive Overload Detection
Predicting Secondary Task Performance: A Directly Actionable Metric for Cognitive Overload Detection
复制标题
DOI：
10.1109/tcds.2021.3114162
复制
发表时间：
2022-12
影响因子：
5
通讯作者：
P. Amadori;Tobias Fischer;Ruohan Wang;Y. Demiris
中科院分区：
文献类型：
--
作者：
P. Amadori;Tobias Fischer;Ruohan Wang;Y. Demiris
在这篇文章中，我们讨论了在双重任务场景下，从不显眼的生理信号中检测用户的认知过载。预测认知超载是交互式认知系统中的一个关键挑战，可能会导致用户和辅助系统之间更安全的共享控制。我们的框架建立在这样的假设之上，即双任务用户在认知次级任务上的决策错误对应于认知过载事件，其中执行任务所需的认知资源超过了用户可用的认知资源。我们提出了一种端到端的序列到序列深度学习模型DecNet，它实时地从眼睛凝视和头部姿势数据中推断用户在次要任务上出错的可能性，即认知过载的实际影响。我们在模拟驾驶环境中收集的数据集上对DecNet进行了训练和测试，该数据集来自两个双任务决策场景下的20名用户，无论是视觉还是听觉决策刺激。DecNet在这两种情况下都可以预测认知超载事件，并可以在时间有限的情况下执行，在认知超载事件发生之前预测到2 S。我们发现，DecNet在视听场景之间的性能差距与用户感知的难度是一致的。这表明，单一通道刺激会给用户带来更高的认知负荷，阻碍他们的决策能力。
In this article, we address cognitive overload detection from unobtrusive physiological signals for users in dual-tasking scenarios. Anticipating cognitive overload is a pivotal challenge in interactive cognitive systems and could lead to safer shared-control between users and assistance systems. Our framework builds on the assumption that decision mistakes on the cognitive secondary task of dual-tasking users correspond to cognitive overload events, wherein the cognitive resources required to perform the task exceed the ones available to the users. We propose DecNet, an end-to-end sequence-to-sequence deep learning model that infers in real time the likelihood of user mistakes on the secondary task, i.e., the practical impact of cognitive overload, from eye-gaze and head-pose data. We train and test DecNet on a data set collected in a simulated driving setup from a cohort of 20 users on two dual-tasking decision-making scenarios, with either visual or auditory decision stimuli. DecNet anticipates cognitive overload events in both scenarios and can perform in time-constrained scenarios, anticipating cognitive overload events up to 2 s before they occur. We show that DecNet’s performance gap between audio and visual scenarios is consistent with user-perceived difficulty. This suggests that single modality stimulation induces higher cognitive load on users, hindering their decision-making abilities.