# Modelling and Measuring Trust in Human-Robot Collaboration
## 2 Background
### Trust in Human-Robot Collaboration  
Within the field of Human-Robot Collaboration, trust plays a crucial role and is considered a significant determining factor. Various studies, including [11; 13], have dedicated efforts to investigate and identify the factors that influence trust in this collaborative context.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Similarity Map of Trust Dimensions According to Different Authors** & & & \\ \hline \hline
[10] & [11] & [12] & [2] \\ \hline \hline \multirow{2}{*}{Moralistic} & Dispositional & Phenomenon-based & Emotional \\  & Situational & Sentiment-based & Relational \\ \cline{1-1} \cline{2-4}  & Learned & Judgement-based & Logical \\ \hline \hline \end{tabular}
\end{table}
Table 1: A rough similarity map between trust dimensions among different authors.  
These factors have not only been structured within a single matrix but also classified based on their origins and dimensions of influence, which are instrumental in facilitating trust and designing experimental protocols.  
Authors in [14] provide a series of controllable factors with correlation to trust:  
* Robot behaviour: This factor relates to the necessity for robot companions to possess social skills and be capable of real-time adaptability, taking into account individual human preferences [15; 16]. In the manufacturing domain, trust variation has been studied in correlation to changes in robot performance based on the human operator's muscular fatigue [17].
* Reliability: An experiential correlation between subjective and objective trust measures was demonstrated through a series of system-failure diagnostic trials [18].
* Level of automation: Consistent with task difficulty and complexity and corresponding automation levels, alterations in operator trust levels were noted [19].
* Proximity: The physical or virtual presence of a robot significantly influences human perceptions and task execution efficiency [20].
* Adaptability: A robot teammate capable of emulating the behaviours and teamwork strategies observed in human teams has a positive influence on trustworthiness and performance [21].
* Anthropomorphism: Research in [22] showed that anthropomorphic interfaces are prone to greater trust resilience. However, the uncanny valley phenomenon described in [23] states that a person's reaction to a humanlike robot would suddenly transition from empathy to repulsion as it neared, but did not achieve, a lifelike appearance.
* Communication: Trust levels fluctuated based on the transparency and detail encapsulated within robot-to-human communication [24; 25].
* Task type: The task variability was recorded to influence interaction performance, preference, engagement, and satisfaction [26].  
Less controllable dimensions of trust include dispositional trust that is influenced exclusively by human traits and the organizational factors linked to the human-robot team [11; 13]. These factors exhibit limited flexibility as they depend directly on the individual or the organizational culture. On the other hand, situational trust is controllable and heavily dependent on various factors, such as the characteristics of the task being developed, making it possible to manipulate it based on the experiment's objective [11; 13].  
Moreover, trust manifests through brainwave patterns and physiological signals, making their use in assessing trust crucial [27; 28]. Biologically driven, these elements foster a more symbiotic interaction, allowing machines to adapt to human trust levels.  
Notably, trust in HRC is dynamic and influenced by a myriad of factors. Understanding the various dimensions of trust and the controllable and uncontrollable factors encompassed allows for the creation of experimental protocols and strategies to enhance trust, a hypothesis evident in studies looking into the triad of operator, robot, and environment [13]. The importance of fostering and maintaining trust in the HRC domain is clear, especially considering the complexity of trust in the ever-evolving landscape of human-robot interaction.