Exploring the Role of Fear in Human Decision Making

This study explores the use of Convolutional Neural Networks (CNNs) to classify fear in the context of decision-making. The approach involves developing a CNN model that is trained using hyper-parameter tuning and K-fold cross-validation to accurately classify fear from video footage of participants' facial expressions during an experiment. The videos are presented along with a map to show the location of the participants along the route. The study reports an overall accuracy of 95.05% for fear classification. The results show that the model can successfully predict fear levels in different conditions. For example, the most desolate route with the lowest light levels recorded an overall fear detected at 49.15%, while the safest route with the highest light levels in a densely populated area saw an overall fear detected at 2.69%. These findings demonstrate the potential for using CNNs to classify fear and provide insight into how fear can be taken into consideration for decision-making in realistic scenarios.