Psychophysiological markers of trust in automation: insights from ERP responses in a modified flanker task.

Abstract

This study investigated the sensitivity of event-related potentials (ERP) to factors influencing trust in machine learning (ML) automation, specifically ML reliability, bias, and transparency, with the goal of identifying an electrophysiological marker of trust in automation. Participants performed a flanker task and observed a simulated ML algorithm perform a modified flanker task, while ERP data were collected. The performance flanker task showed canonical patterns in behavioral responses, including fewer errors and shorter response times to congruent trials. We also observed the expected ERP components, including the error-related negativity (ERN) and positivity (Pe), alongside a significant late positive component (LPC) associated with error processing. Contrary to predictions, no differences in oERN amplitudes were observed across model error conditions. The oPe component was elicited by model errors, yet was insensitive to model reliability or bias. Notably, an LPC was also observed to model errors and was larger for errors from the more reliable model (90% vs. 60%). LPC amplitude was negatively correlated with subjective trust ratings in the 60% reliable biased condition, indicating that reduced LPC effects were associated with higher trust levels. These implications of these results are discussed in the context of the P3b and P600 ERP components. Additionally, there were no effects of model transparency on ERP results or subjective trust ratings, suggesting that trust is primarily developed through direct observation of model performance. Our results contribute to understanding the neural mechanisms underlying trust in automation, highlighting the potential of ERP methodologies to advance our understanding in this domain.