Proceedings of the Annual Meeting of the Cognitive Science Society

To inform Explainable AI (XAI) design for updating users’ beliefs about AI based on their mental models, we examined the similarities and differences between humans and AI in object detection in driving scenarios. In humans, individuals differed in adopting focused or explorative attention strategies, with better performance associated with the focused strategy. AI (Yolo-v5s) had higher similarity in attended features to the focused than the explorative strategy in humans, and achieved human-expert-level performance in vehicle detection even in difficult cases such as occlusion and degradation. In contrast, it performed much poorer than humans in detecting humans with low attended feature similarity due to humans’ attention bias for stimuli with evolutionary significance. Also, higher similarity to humans’ attended features was associated with better AI performance, suggesting that human attention may be used for guiding AI design. These findings have significant implications for both AI and XAI designs.