This study investigated efficient methods for decoding cognitive load using multimodal physiological signals and advanced causal spatiotemporal modeling. Cognitive load refers to the mental effort required to perform a task, and monitoring it accurately is essential in contexts where performance and safety are critical. The authors combined physiological data from multiple modalities to capture different aspects of the body’s response to cognitive stress: electroencephalography (EEG), electrocardiography (ECG), and skin conductance. They then applied causal spatiotemporal analysis to identify patterns that are not only predictive but also interpretable, revealing how physiological systems interact dynamically under varying levels of mental demand. This means they can accurately predict whether someone is under high or low cognitive load and the causal spatiotemporal patterns why and how the prediction is made. Additionally, compared to traditional machine learning approaches, their method improved classification efficiency, reduced computational requirements, and maintained high accuracy, making it feasible for real-time applications.