Trust in Shared-Space Collaborative Robots: Shedding Light on the Human Brain

Abstract

Background Industry 4.0 is currently underway allowing for improved manufacturing processes that leverage the collective advantages of human and robot agents. Consideration of trust can improve the quality and safety in such shared-space human-robot collaboration environments. Objective The use of physiological response to monitor and understand trust is currently limited due to a lack of knowledge on physiological indicators of trust. This study examines neural responses to trust within a shared-workcell human-robot collaboration task as well as discusses the use of granular and multimodal perspectives to study trust. Methods Sixteen sex-balanced participants completed a surface finishing task in collaboration with a UR10 collaborative robot. All participants underwent robot reliability conditions and robot assistance level conditions. Brain activation and connectivity using functional near infrared spectroscopy, subjective responses, and performance were measured throughout the study. Results Significantly, increased neural activation was observed in response to faulty robot behavior within the medial and right dorsolateral prefrontal cortex (PFC). A similar trend was observed for the anterior PFC, primary motor cortex, and primary visual cortex. Faulty robot behavior also resulted in reduced functional connectivity strengths throughout the brain. Discussion These findings implicate regions in the prefrontal cortex along with specific connectivity patterns as signifiers of distrusting conditions. The neural response may be indicative of how trust is influenced, measured, and manifested for human-robot collaboration that requires active teaming. Application Neuroergonomic response metrics can reveal new perspectives on trust in automation that subjective responses alone are not able to provide.

Using Fnirs to Verify Trust in Highly Automated Driving

“…In contrast, the high credibility (HC) expectations provided to the other group matched with the actual vehicle performance, thus increasing trust and reducing the engagement with the driving task. Similar findings were also observed in [12] with human collaborative robots. Our participants in the HC group based their trust calibration on the heuristics (i.e., the mental model concerning the HAV reliability) generated by the vehicle capabilities provided, thus indicating an analogic process of trust calibration according to [4].…”

“…Such self-reported distrust was supported by unique variations in the right orbitofrontal (i.e., BA10-R) for the LC group. This finding within the LC group was consistent among all three chromophores in two different driving scenarios (i.e., suburbs and city centre), thus suggesting the right orbitofrontal might be involved in assessing the driving context to calibrate trust, as suggested in previous work [12], [18]. That being the case, this would indicate incremented monitoring towards the changes in the driving environment for distrusting participants.…”

“…Results indicated that the uncertainty of judging the reliability of the uncrewed vehicle's abilities under assisted manual and assisted automated increased oxygenation in the orbitofrontal cortex, specifically in Brodmann Area (BA) 10, and the right DLPC in BA 46. This finding could be associated with those from [12] who found increased activation in the DLPC and the anterior prefrontal cortex during robot malfunction. As well as to those from [6], who found the orbitofrontal and anterior cingulate cortex to play a crucial role in intentional engagement and hence in the intentional decision of trusting.…”

“…Hopko and Mehta used fNIRS to measure cortical activation and functional connectivity associated to trust during a human-robot collaboration task [12]. Their participants had to complete a surface finishing task in collaboration with a robot with varying reliability conditions.…”

“…Different trust levels can be induced through credibility expectations to naïve participants [10], [12], [31], [32], [33]. In other words, the automated system's performance credibility can be manipulated through previous information and induce trust/distrust in naïve participants.…”