Performance Monitoring Applied to System Supervision

Identifying conflict monitoring as a specific executive component that contributes to impaired self-awareness in patients with acquired brain injury

OBJECTIVE

Impaired self-awareness (SA) after acquired brain injury (ABI) has traditionally been linked to deficits in executive functions. However, conflicting findings about this relationship have been reported in the literature. This inconsistency is probably due to the multicomponent nature of both constructs, as not all aspects of executive functions may be equally relevant to all components of self-awareness. This study explored whether offline SA (i.e. metacognitive knowledge) and online SA (i.e. error detection) relate to a less studied executive component, conflict monitoring/resolution.

METHOD

Twenty-six patients with ABI performed the Three-Conflict Cognitive Control Task (3CCT), an experimental task that allowed to measure the ability to monitor and solve three different types of conflicts (Distractors-filtering, Spatial Stroop and Simon). Measures of SA were collected: offline SA was based on self-informant discrepancy about patient's everyday functional difficulties, and online SA was based on error detection abilities during a performance-based naturalistic task (The Breakfast Conflict Task).

RESULTS

After controlling for global cognition, the conflict monitoring measure of 3CCT demonstarted incremental validity in predicting offline and online SA measured in naturalistic tasks.

CONCLUSIONS

Conflict monitoring/resolution seems to be an important component of SA. This finding contributes to further understand the relationship between executive functions and SA. In addition, conflict monitoring/resolution is an executive component that should be considered when designing assessment and intervention strategies to deal with ISA.

Brain mechanisms of automated conflict avoidance simulator supervision

Supervision of automated systems is an ubiquitous aspect of most of our everyday life activities which is even more necessary in high risk industries (aeronautics, power plants, etc.). Performance monitoring related to our own error making has been widely studied. Here we propose to assess the neurofunctional correlates of system error detection. We used an aviation-based conflict avoidance simulator with a 40% error-rate and recorded the electroencephalographic activity of participants while they were supervising it. Neural dynamics related to the supervision of system's correct and erroneous responses were assessed in the time and time-frequency domains to address the dynamics of the error detection process in this environment. Two levels of perceptual difficulty were introduced to assess their effect on system's error detection-related evoked activity. Using a robust cluster-based permutation test, we observed a lower widespread evoked activity in the time domain for errors compared to correct responses detection, as well as a higher theta-band activity in the time-frequency domain dissociating the detection of erroneous from that of correct system responses. We also showed a significant effect of difficulty on time-domain evoked activity, and of the phase of the experiment on spectral activity: a decrease in early theta and alpha at the end of the experiment, as well as interaction effects in theta and alpha frequency bands. These results improve our understanding of the brain dynamics of performance monitoring activity in closer-to-real-life settings and are a promising avenue for the detection of error-related components in ecological and dynamic tasks.

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

Discrepancies between sensory predictions and action outcome are at the base of error coding. However, these phenomena have mainly been studied focussing on individual performance. Here, we explored EEG responses to motor prediction errors during a human-avatar interaction and show that Theta/Alpha activity of the frontal error-monitoring system works in phase with activity of the occipito-temporal node of the action observation network. Our motor interaction paradigm required healthy individuals to synchronize their reach-to-grasp movements with those of a virtual partner in conditions that did (Interactive) or did not require (Cued) movement prediction and adaptation to the partner's actions. Crucially, in 30% of the trials the virtual partner suddenly and unpredictably changed its movement trajectory thereby violating the human participant's expectation. These changes elicited error-related neuromarkers (ERN/Pe - Theta/Alpha modulations) over fronto-central electrodes during the Interactive condition. Source localization and connectivity analyses showed that the frontal Theta/Alpha activity induced by violations of the expected interactive movements was in phase with occipito-temporal Theta/Alpha activity. These results expand current knowledge about the neural correlates of on-line interpersonal motor interactions linking the frontal error-monitoring system to visual, body motion-related, responses.

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

With the rise of increasingly complex artificial intelligence (AI), there is a need to design new methods to monitor AI in a transparent, human-aware manner. Decades of research have demonstrated that people, who are not aware of the exact performance levels of automated algorithms, often experience a mismatch in expectations. Consequently, they will often provide either too little or too much trust in an algorithm. Detecting such a mismatch in expectations, or trust calibration, remains a fundamental challenge in research investigating the use of automation. Due to the context-dependent nature of trust, universal measures of trust have not been established. Trust is a difficult construct to investigate because even the act of reflecting on how much a person trusts a certain agent can change the perception of that agent. We hypothesized that electroencephalograms (EEGs) would be able to provide such a universal index of trust without the need of self-report. In this work, EEGs were recorded for 21 participants (mean age = 22.1; 13 females) while they observed a series of algorithms perform a modified version of a flanker task. Each algorithm's degree of credibility and reliability were manipulated. We hypothesized that neural markers of action monitoring, such as the observational error-related negativity (oERN) and observational error positivity (oPe), are potential candidates for monitoring computer algorithm performance. Our findings demonstrate that (1) it is possible to reliably elicit both the oERN and oPe while participants monitored these computer algorithms, (2) the oPe, as opposed to the oERN, significantly distinguished between high and low reliability algorithms, and (3) the oPe significantly correlated with subjective measures of trust. This work provides the first evidence for the utility of neural correlates of error monitoring for examining trust in computer algorithms.