# Modelling and Measuring Trust in Human-Robot Collaboration
## 6 Conclusions  
To conclude our study, we need to review the core research questions that guided our research regarding the nature of trust in Human-Robot Collaboration. First, we aimed to identify the fundamental factors affecting trust in HRC environments. We addressed this issue with an extensive literature review that revealed three distinct dimensions of trust: dispositional, situational, and learned. This review also stressed several demographic aspects that influence the dispositional dimension of trust. The second question aimed to identify specific variables that could influence the previously disclosed dimensions of trust. To answer this issue, we created a specific experimental process that allowed us to identify potential factors affecting dispositional and learned trust. The literature suggested a marked influence of personal factors such as gender, age, and occupational roles on dispositional trust. However, contrary to conventional expectations, our empirical findings did not reveal any statistically significant correlations between these traditional demographic markers and participants' dispositional trust in automated systems. This unexpected result prompted us to delve further into broader demographic factors--social status, educational background, and nationality--which were not explicitly considered in our initial study. On the other hand, the study proved that the first iterations between humans and robots play a crucial role the evolution of trust dynamics. Presumably, this points out that the learned dimension of trust is more sensible and susceptible to change than the other dimensions. The third and final research question aimed to identify crucial human signals for measuring trust in HRC and understand their contributions to the development of trust detection models. Following previous works detailed in the literature review, our exploration into psychophysiological signals encompassed brain (EEG), electrodermal (GSR), respiratory (RSP), and ocular (PLP) activities. We focused on three different approaches, varying from generic to individualized trust models. Results revealed the difficulty of extrapolating a general model of trust. On the one hand, the individualized models worked better than the general model, and, on the other hand, several individually significant psychophysiological signals showed very particular responses and, thus, proved irrelevant in the general model. This issue emphasizes the very complex and personal nature of trust. Our study effectively addresses the research questions, shedding light on the intricate interplay of factors influencing trust, the temporal dynamics of trust evolution, and the optimal human signals for trust measurement in HRC. However, it is worth mentioning that while our research provides valuable insights into the complex dynamics of trust in Human-Robot Collaboration, there are areas that remain inconclusive. For example, our discussion does not address the potential implications of feedback mechanisms on robot behaviour based on the trust levels perceived by human inspectors. Incorporating such feedback into our trust detection models could enhance the adaptability of robots in response to changing trust dynamics, thus optimizing collaboration and performance in human-robot interaction scenarios. Nevertheless, we are confident that this research will empower the design of future reliable, robust, and trusted human-robot collaborative environments. Conceptualization, E.L., L.B., A.M., S.S. and N.T. ; methodology, E.L.; software, E.L.; validation, L.B., A.M. and S.S.; formal analysis, E.L.; investigation, L.B., A.M. and S.S.; resources, S.S.; data curation, E.L.; writing--original draft preparation, E.L., L.B., A.M. and S.S.; writing--review and editing, L.B., S.S and N.T. ; visualization, A.M.; supervision, L.B. and S.S.; project administration, S.S.; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript.