## 4 Discussion
### Conclusion and Future Perspectives  
To conclude, to the best of our knowledge, this research explored the neurophysiological correlates of individual decisional strategies in a complex real-life scenario with unexpected event prompts by adopting, for the first time, a combined behavioral and neurophysiological approach. By integrating EEG data with behavioral analysis, this research provided novel insights into the neural correlates of different decision-making strategies, offering a more comprehensive understanding of both explicit and implicit cognitive processes underlying real-world decision-making. The results showed that people have a predisposition to adopt the T-ds, a decisional strategy focused on the consideration of the duty that has to be accomplished and the objectives to be pursued. Indeed, this decisional strategy permits making an effective decision in real-life and unpredictable scenarios in which people are required to act efficiently and in a short time. Moreover, adopting a decisional strategy, specifically the T-ds, is characterized by a frontal activation of theta and gamma bands, indices of the promotion of goal selection, working memory, mental fatigue, and cognitive flexibility. So, the integration of EEG data reinforces the significance of theta and gamma frequency bands for efficient decision-making processes. Based on these findings, this study's novelty lies in the integration of EEG correlates of decision-making strategies with behavioral data, providing a novel opportunity to explore brain activity underlying the decision-making process in complex environmental contexts. This approach enabled a more in-depth exploration of decision-making strategies, overcoming the limitations of previous studies that focused mostly on only behavioral aspects. Compared to existing research, which primarily highlighted the impact of individual variables or decision-making styles on the strategy adopted [37,48], the present study provided a more comprehensive and integrated perspective on the decision-making process, highlighting not only the behavioral aspect but also and especially the electrophysiological activity as an index of effort and cognitive load. From these results, future research could extend the investigation of decision-making strategies to different domains beyond real-life unpredictable scenarios. For instance, exploring how these strategies operate in professional settings such as emergency response, financial trading, or medical decision-making could provide valuable insights into their applicability in high-stakes environments. Additionally, examining the role of decision-making strategies in group dynamics and collaborative problem-solving may reveal how individuals adapt their approaches when working within teams. Furthermore, the influence of digital technologies, such as artificial intelligence-assisted decision-making, could be explored to understand how human decision-making strategies interact with automated systems. This could be particularly relevant in fields where technology plays a critical role in shaping choices, such as autonomous driving and personalized healthcare. Despite the fact that the study provided interesting data, it is important to consider some possible limitations. The first limitation regards the sample size, which although moderate, may limit the generalizability of the results to larger populations. Additionally, future studies should recruit samples from different environments, such as multiple organizations, to highlight possible differences in individual decisional strategy tendencies due to different work contexts and operate a comparison between professional and non-professional samples. Furthermore, the integration of complementary neuroimaging techniques could facilitate a more comprehensive investigation of brain activity underlying the process under analysis.