# Modelling and Measuring Trust in Human-Robot Collaboration
## 2 Background
### Trust Measuring Using Different and Combined Psychophysiological Signals  
Studies on trust have traditionally been situated within the context of interpersonal relationships, primarily utilizing various questionnaires to evaluate levels of trust [29; 30; 31]. However, due to the arrival of automatic systems and the decreased cost of acquiring and analysing psychophysiological signals, focus has shifted towards examining these types of signals in response to specific stimuli in a bid to lower the subjectivity and potential biases associated with questionnaire-based approaches. Recent studies, like [9; 32; 33; 34], have been centred on the usage of psychophysiological measurements in the study of human trust.  
The choice of these psychophysiological sensors can differ, depending on which human biological systems (central and peripheral nervous systems) they are applied to. A common pattern has emerged from studies in which EEG is the most used signal to measure central nervous system activity, with fMRI closely behind it--the latter being more extensively used in the context of interpersonal trust [35]. EEG analysis is increasingly being utilized in human-robot interaction evaluation and brain-computer interfaces [36], as it provides the means to create real-time non-interruptive evaluation systems enabling the assessment of human mental states such as attention, workload, and fatigue during interaction [37; 38; 39]. Additionally, attempts have been made to study trust through EEG measurements which only look at event-related potentials (ERPs), but ERP has proven to be unsuitable for real-time trust level sensing during human-machine interaction due to the difficulty in identifying triggers [33; 39].  
Similarly, sensors measuring signals from the peripheral nervous system, notably ECG (electrocardiography) and GSR (galvanic skin response), have been frequently used in assessing trust [35]. GSR, a classic psychophysiological signal that captures arousal based on the conductivity of the skin's surface, not under conscious control but instead modulated by the sympathetic nervous system, has seen use in measuring stress, anxiety, and cognitive load [40]. Some research revealed that the net phasic component, as well as the maximum value of phasic activity in GSR, might play a critical role in trust detection [33].  
In contrast, the use of single signals most commonly involves only EEG, succeeded by fMRI [35]. However, some studies have proposed that combining different psychophysiological signals (like GSR, ECG, EEG, etc.) improves the depth, breadth, and temporal resolution of results [41; 42]. Interestingly, pupillometry has been recently highlighted as a viable method for detecting human trust, revealing that trust levels may be influenced by changes in partners' pupil dilation [43; 44; 45].  
In short, the current state of the art exhibits an increasing trend towards the use of psychophysiological signals emanating from both central and peripheral nervous systems. Also, it showcases an interest in combining these signals to create more robust trust detection mechanisms with an improved breadth and depth of results.