The absent P3a. Performance monitoring ERPs differentiate trust in humans and autonomous systems.

To address suggestions that human brain responses to autonomous system errors may be used as brain-based measures of trust in automation, the present study asked participants to monitor the performance of either a virtual human or an autonomous system partner performing a novel, complex, real-world image classification task. We predicted visual feedback of partner errors would elicit the feedback-related negativity and P3 ERP components, and that these components would differ between the human and system groups. Behavioral results showed that while participants calibrated their trust in their partner according to our intended manipulation of error rates, no group differences were found. The ERP data, however, revealed FRN and P3 effects for both groups, modulated by accuracy and error rate. An unexpected finding was that the P3 topography differed between groups, as while the P3 for the human group was widespread, the P3 for the system group was limited to posterior electrodes with the P3a being completely absent. These results demonstrate the potential for EEG-based measures of real-time trust in automation to be used in applied scenarios with benefits beyond traditional methods. Further, we found that a distinct neural processing of autonomous system errors compared to human errors may exist, necessitating further research in this emerging field.