Goal recognition is a crucial aspect of understanding the intentions and objectives of agents by observing some of their actions. The most prominent approaches to goal recognition can be divided into two main categories: (1) trustworthy systems, which exploit automated reasoning for computing plans compatible with the observed actions, and (2) swifter systems, which try to quickly infer goals, often overlooking complex cognitive processes, and have no formal guarantees of their results. This paper introduces a novel approach inspired by the dual process theory, which integrates these two techniques. A dual-process model is proposed, leveraging fast, experience-based recognition for immediate goal identification, and slow, deliberate analysis for deeper understanding. Machine learning techniques and classical planning techniques are employed to obtain this dual-process system. Experimental evaluations demonstrate the effectiveness of the approach, reducing the amount of resources required to compute a solution (e.g., time to find a goal), while at the same time enhancing accuracy and robustness, especially in more complex scenarios.