Estimating the cognitive load in physical spatial navigation

Abstract

Navigation is an essential skill that helps one to be aware of where they are in space and ambulate from a location to others. Many cognitive processes are involved in navigation tasks, even in the simplest scenario, such as landmarks encoding, cognitive map anchoring, goal-oriented planning, and motor executing. Engaging multiple tasks simultaneously could lead to higher cognitive load and attenuated navigation performance. In this study, we investigate the cognitive load of participants while they perform a navigation task. We demonstrated the ability to extract neural features from complex physical movement tasks, such as navigation. We found that retrosplenial complex (RSC) shows a distinct features for mental workload related task. We further evaluated participant’s cognitive load with different machine learning algorithm and found that CNN is able to classify with 93% accuracy. The results provided a potential approach to study cognitive load in a more naturalistic scenario.