# Modelling and Measuring Trust in Human-Robot Collaboration
## 5 Discussion  
Throughout this article, we have presented the different dimensions of trust (dispositional, situational, and learned) and outlined the experimental process undertaken to determine the factors influencing them. The literature consulted indicates a marked influence of personal factors on dispositional trust. Nevertheless, our empirical results contradicted conventional expectations by unveiling the absence of statistically significant correlations between these customary demographic indicators and the participants' dispositional trust in automated systems. Since our analysis focused on gender, age, and occupational role, we suggest further analysis of the potential influence of unexplored demographic aspects. Variables such as social status, educational background, and nationality may yield significant results. Their potential significance in shaping dispositional trust cannot be disregarded, and further studies are needed to analyze their impact. To analyze the evolution and dynamics of learned trust, we designed two experimental models with similar iterations arranged in different orders, thus allowing for the detection of variations in trust evolution dynamics. As indicated by statistical tests, the behavior of both groups is disparate. Specifically, participants who start with a series of iterations exhibiting random behavior (participants interacting with Model-1) experience a higher dynamic variation compared to their counterparts (participants interacting with Model-0), thus exhibiting higher trust levels when the system functions properly and lower trust levels when the system's performance is erratic. In this context, it could be argued that the initial iterations with the system significantly influence the trajectory of future iterations. Drawing an analogy with classical mechanics, it can be asserted that the initial iterations are critical in determining the level of \"trust inertia\" individuals have towards automated systems and their future interactions. Regarding the third component of trust, situational trust, its influence is not directly covered by the experimentation and analysis conducted in this research. However, a small portion of it is reflected in the link between post-trust perception and the interaction model to which each participant has been subjected. This is justified by considering that this combination incorporates elements that are external to the individual into a static value  
Figure 7: Summary of signal contribution to general and individual models. of trust and, therefore, cannot correspond to dispositional (individual) trust or learned (dynamic) trust. The analyses performed highlight a low significance in the link between these elements. This is mainly because this factor is not the main focus of the study, and thus, the experimental process is not designed to emphasize its influence. Regarding mathematical models aimed at trust detection, we designed a common model for all participants using the generalist approach. The Leave-One-Out approach is also suitable, in general terms, for any individual and, in this regard, allows the same universalization as the general model. These models offer a moderate performance, achieving an F1 score close to 0.61-0.62. On the other hand, individualized models show much higher performance than these approaches, achieving F1 values that surpass those of the previous models in all cases, reaching an average F1 score of 0.76 and even reaching occasional values of 0.92 on this score. This indicates that, compared to generalist models, individualized models offer better performance in detecting trust violation and recovery situations. However, the implementation of this approach faces other challenges, such as the need to train independent algorithms for each subject. The influence of the different psychophysiological signals in the proposed prediction models must also be considered.