Košir Andrej, Svetina Matija, Perkovič Marko, Dimc Franc, Brcko Tanja, Žagar Dejan
Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.
Faculty of Arts, Department of Psychology, University of Ljubljana, Ljubljana, Slovenia.
Front Psychol. 2024 Sep 3;15:1417215. doi: 10.3389/fpsyg.2024.1417215. eCollection 2024.
Maritime studies, encompassing a range of disciplines, increasingly rely on advanced data analytics, particularly in the context of navigation. As technology advances, the statistical averaging of large datasets has become a critical component of these analyses. However, recent studies have highlighted discrepancies between statistical predictions and observable realities, especially in high-stress environments like port approach procedures conducted by marine pilots. This study analyzed physiological responses recorded during simulation exercises involving experienced marine pilots. The focus was not on the specific outcomes of the simulations but on the potential faults arising from conventional statistical signal processing, particularly mean-centered approaches. A large dataset of signals was generated, including one signal with a dominant characteristic intentionally designed to introduce imbalance, mimicking the uneven distribution of real-world data. Initial analysis suggested that the average physiological response of the pilots followed an S-shaped curve, indicative of a psycho-physiological reaction to stress. However, further post hoc analysis revealed that this pattern was primarily influenced by a single participant's data. This finding raises concerns about the generalizability of the S-curve as a typical stress response in maritime pilots. The results underscore the limitations of relying solely on conventional statistical methods, such as mean-centered approaches, in interpreting complex datasets. The study calls into question the validity of standardizing data interpretations based on dominant characteristic curves, particularly in environments as intricate as maritime navigation. The research highlights the need for a re-evaluation of these methods to ensure more reliable and nuanced conclusions in maritime studies. This study contributes to the ongoing discourse on data interpretation in maritime research, emphasizing the critical need to re-assess conventional statistical signal processing techniques. By recognizing the potential pitfalls in data generalization, the study advocates for more robust analytical approaches to better capture the complexities of real-world maritime challenges.
海洋研究涵盖一系列学科，越来越依赖先进的数据分析，尤其是在航海领域。随着技术的进步，大型数据集的统计平均已成为这些分析的关键组成部分。然而，最近的研究突出了统计预测与可观察到的现实之间的差异，特别是在像引航员进行港口进港程序这样的高压力环境中。本研究分析了在涉及经验丰富的引航员的模拟演习中记录的生理反应。重点不是模拟的具体结果，而是传统统计信号处理，特别是均值中心化方法所产生的潜在错误。生成了一个大型信号数据集，包括一个故意设计以引入不平衡的具有主导特征的信号，模仿现实世界数据的不均匀分布。初步分析表明，引航员的平均生理反应遵循S形曲线，表明对应激的心理生理反应。然而，进一步的事后分析表明，这种模式主要受单个参与者数据的影响。这一发现引发了对S曲线作为引航员典型应激反应的普遍性的担忧。结果强调了仅依靠传统统计方法（如均值中心化方法）来解释复杂数据集的局限性。该研究质疑基于主导特征曲线标准化数据解释的有效性，特别是在像海上航行这样复杂的环境中。该研究强调需要重新评估这些方法，以确保在海洋研究中得出更可靠、更细致入微的结论。本研究为正在进行的海洋研究数据解释讨论做出了贡献，强调了重新评估传统统计信号处理技术的迫切需要。通过认识到数据泛化中的潜在陷阱，该研究提倡采用更稳健的分析方法，以更好地捕捉现实世界海洋挑战的复杂性。
