As social beings, humans make decisions partly based on social interaction. Observing the behavior of others can lead to learning from and about them, potentially increasing trust and prompting trust-based behavioral changes. Observation-based decision making involves different neural structures. The orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) are known as neural structures mainly involved in processing emotional and cognitive decision values, respectively, while the anterior cingulate cortex (ACC) plays a pivotal role as a social hub, integrating the afferent expectancy signals from the OFC and LPFC. This paper presents a neurocomputational model of the interplay between observational learning and trust, as well as their role in individual decision making. Hence, our model provides a framework for investigating how emotional and rational responses may change when individuals observe the action–outcome associations of an alleged expert. We have modeled the neurodynamics of three cortical structures (OFC, LPFC, and ACC) and their interactions, where the neural oscillatory properties, modeled with Dynamic Bayesian Probability, represent the observer’s attitude towards the expert and the decision options. As an example of an everyday behavioral situation related to climate change, we use the choice of transportation between home and work. The model generates EEG-like signals that show how patterns of neural activity change during observation-based decision making. The simulations suggest that higher levels of trust influence both emotional and rational evaluations when individuals observe the actions and outcomes of an expert. Overall, the proposed framework provides insight into how observational learning and trust work together to shape decision making. It highlights the dynamic interplay between emotional and cognitive processes and offers a mechanistic understanding of how social information can influence behavior.