## 2 Materials and Methods
### Procedure
#### 2.2.1 Behavioral Data Acquisition  
To explore individual tendencies to rely on information of increasing complexity in a decision-making process, the experimental task was administered via a web-based experiment-management platform (Quatrics XM, Qualtrics LLC, Provo, UT, USA).  
Specifically, the task was the same developed and adopted in the research by [PERSON] [1] and required participants to face real-life decision-making situations, during which an unexpected event prompted them to appraise the situation and rely on different strategies to make the best decision and manage the problem. Indeed, participants were presented with a series of realistic decision-making scenarios of increasing complexity, simulating unpredictable but real-life everyday possible scenarios (e.g., _\"The high-speed train is delayed, and the regional train would make you late for the meeting, albeit by a little. What do you do?\"_) to facilitate their understanding and self-identification, aspects not always ensured by protocols used in previous studies. After immersing themselves in the scenario, participants were required to express the level of agreement with each proposed option (e.g., _\"The high-speed is faster than the regional train. The repair of the malfunction may be resolved quickly. If I remain on board, there is still a reasonable chance of arriving on time\"_) for managing the unexpected event on a five-point scale (1 = total disagreement; 5 = complete agreement). By involving participants in these realistic decision-making scenarios, the study aimed to analyze the complexity of the decision-making process in depth, assessing not only the explicit choice of strategies but also the cognitive effort required to deal with them. Unlike previous research, which considered decision-making as a unified process without distinguishing between strategies [32; 33], this approach enabled a more detailed analysis of the factors influencing strategy choice. Furthermore, this study integrated behavioral data with electrophysiological measures, in particular EEG recordings, to investigate the neural dynamics underlying different decision-making strategies. This methodological integration allows a more comprehensive understanding of the cognitive processes involved, providing both explicit responses and implicit neural activity associated with decision complexity and cognitive load.  
For the behavioral data, both response scores (the degree of agreement expressed with each phrase) and response time (RTs) were collected for each decision-making scenario and then transcribed offline to create three behavioral indices, the Information-strategy Index (I-i), the Situation-strategy Index (S-i), and the Task-strategy Index (T-i), respectively. Specifically, RTs were collected as behavioral metrics of performance, mirroring the efficacy in responding to the task and information processing, as well as an indirect measure of the workload and cognitive effort required by the decision-making process [34; 35].  
To calculate the behavioral indices, the following formulae were adopted:  
\[I-i=\frac{Information\;score\;in\;deciles}{11-\;RTs\;Information\;in\;deciles}\]  
\[S-i=\frac{Situation\;score\;in\;deciles}{11-\;RTs\;Situation\;in\;deciles}\]  
\[T-i=\frac{Task\;score\;in\;deciles}{11-\;RTs\;Task\;in\;deciles}\]  
where the score in deciles was represented by the average of the scores obtained from the Likert scales for each of the three scenarios converted into deciles, and the RTs in deciles were obtained by averaging the RTs of each response for each scenario, setting 2 s as the lower limit and 20 s as the upper limit. The decision to use deciles facilitated the achievement of comparable measurements, given that scores and times were measured on different scales.