A safety risk formation model for intelligent human–machine interaction based on computational neuroscience

The widespread application of intelligent technologies in cockpits enhances the efficiency of human–machine systems, introduces new challenges for safety risk analysis. Safety risk analysis focuses on explaining the safety risk formation process from a macro perspective. However, the neurocognitive mechanisms of pilots during this process are still unclear. This paper proposes a safety risk formation model for intelligent human–machine interaction based on computational neuroscience. Specifically, the safety risk factors affecting pilot behavior during intelligent human–machine interaction are identified. The regulatory mechanisms involving dopamine, acetylcholine, and the thalamus corresponding to different safety risk factors are elucidated. The structure and function of the Cortico-Basal Ganglia-Thalamus-Cortical (CBTC) neural circuit are introduced. To quantitatively describe the dynamic characteristics of neurons, a computational model of CBTC is established. Experimental validation of the proposed model is conducted using a cockpit intelligent interaction platform. The results indicate that the established computational model of CBTC effectively simulates the impact of varying levels of safety risk factors on pilots.