Measurement and Analysis of Cognitive Load Associated with Moving Object Classification in Underwater Environments

Visual analysis in field science experiments often involves classifying objects on experimental images and videos. In this context, developing a reliable and independently validated estimate of mental workload during object classification can enable cognitively responsive task allocation. The goal of this study is to quantify the cognitive load perceived by humans from electroencephalography (EEG) data during an underwater object classification task that was inspired from citizen science studies. During the task, participants were asked to identify one of three possible invasive fish species in short videos of a virtual underwater environment. The virtual environment was modeled to vary fish behavior and environmental factors that are known to be critical in classification. A contextually-relevant secondary task was designed to provide independent validation of cognitive load measures. Several established measures of cognitive load were compared across different weightings on the scalp positions, and the measure that strongly associated with reaction time and a secondary task accuracy was selected for further analysis. Our results show that cognitive load calculated using the difference in power of alpha frequencies best correlates with reaction time and secondary task accuracy. When fit to the environmental factors, cognitive load calculated using this approach was high when the environment was turbid and the fish moved at high speeds. Results from this study have applications in cognitively-responsive human-computer interaction and in developing shared control strategies in human-robot interaction.