Performance Monitoring Applied to System Supervision

Abstract

Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during system supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring system is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to system supervision. At a larger scale, a better understanding of system supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of systems. Finally, we provide further directions in the study of system supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL) performance problem.

Midline frontal and occipito-temporal activity during error monitoring in dyadic motor interactions

“…Besides the suggested overlap between the neural systems responding to observed and executed actions and errors (Zubarev et al, 2018) studies have documented a differential contribution of brain areas to the observation of errors performed by others (Shane et al, 2008;Abreu et al, 2012;Somon et al, 2019;Ninomiya, et al, 2018;Somon et al, 2017). In the present study we explored the neural responses associated to coordinating one's own movements with those of a virtual partner who could perform unexpected changes in its motor behaviour.…”

Learning From the Slips of Others: Neural Correlates of Trust in Automated Agents

“…Previous research has examined neural correlates of trust between people ( Adolphs, 2002 ; Delgado et al, 2005 ; King-Casas et al, 2005 ; Krueger et al, 2007 ) and while it is expected that the overall trust process for people and automation is similar, it is likely that important and specific differences will emerge between people and machines ( Madhavan and Wiegmann, 2007b ; de Visser et al, 2016 ). While contributions toward understanding trust in automated systems from a neuroscientific viewpoint are still limited, recent reviews have pointed to the potential of applying known neural correlates of performance monitoring to the monitoring of machines ( Fedota and Parasuraman, 2010 ; Drnec et al, 2016 ; Berberian et al, 2017 ; Somon et al, 2017 ). Consistent with this idea, a recent study found that false alarm-prone advice activated different brain regions for a human compared to a machine, including the precuneus, posterior cingulate cortex, and temporoparietal junction ( Goodyear et al, 2016 ).…”

Evaluation of performance monitoring ERPs through difficulty manipulation in a response-feedback paradigm

“…For reviews on performance monitoring components and their applications seeGehring et al (2011) orSomon et al (2017).…”