While most of the computational trust models devote to truthfully detecting trustworthy individuals, much less attention is paid to how these models are perceived by users, who are the core of the trust machinery. Understanding the relation between trust models and users’ perception of those models may contribute for reducing their complexity, while improving the user-experience and the system performance. Our work recognizes reputation, recommendation and rating systems as online trust representatives and explores the biased behavior resulting from users’ perception of those systems. Moreover, we investigate the relation and inter-dependencies between trust mechanisms and user behavior with respect to context, risk, dynamics and privacy. We perform experimental study and identify few types of cognitive biases that users exhibit. Based on the identified factors and the findings of the study, we propose a framework for addressing some of the issues attributed to users’ biased behavior.