## 4 Discussion
Although some researchers suggest that physiological data stabilize after 40 s (with closed eyes, however) [5], we recommend using longer periods of time in between the glares for HRV analysis or using other indicators. When collecting psychophysiological data, environmental factors such as temperature or humidity must also be controlled for, as these could also influence the data, especially the SCR [6; 7]. Similarly, participant-related factors such as medicine or coffee consumption should be taken into account (see e.g., [25]), and regular check-ups of the equipment (such as the respiratory belt) should be performed during data collection, as extensive data loss is otherwise possible. Furthermore, when assessing drivers' physiological reactions and visual performance, the length and number of repetitions might play a role. We have observed a pronounced effect of block sequence on most of the physiological measures, with the reactions getting weaker with each block of glare, no matter from which light source, indicating a strong effect of habituation. Therefore, for further experiments, we would recommend testing a smaller number of light samples. Further, assessing visual acuity and contrast sensitivity with standard paper charts proved to be ineffective in the within-subjects experimental design, as the participants memorized the characters with repetition. In order to prevent such an effect, digital charts with variable characters for each trial could be used. In such a case, however, one should account for the light coming from the digital chart itself and adjust the brightness and color rendering of the monitor to non-disturbing levels. As a limitation, we should emphasize that the current experiment was not a clinical study, where drivers with healthy vision and different eye defects would be compared. Rather, we aimed at obtaining a diverse group of drivers and testing their reactions to different light sources. Although younger drivers were more represented in the current sample, the overall heterogeneity of the sample can be deemed satisfactory. This is especially relevant because of the high significance of the \"participant\"-factor in the statistical analyses, where inter-individual variability accounted for most of the variability explained by the linear mixed-effect models. Therefore, when testing drivers' glare perception, we recommend aiming at a heterogeneous sample of drivers (in terms of age, gender, as well as the characteristics of their visual system). Due to the within-subject nature of the experiment, we were not able to statistically separate individual demographic characteristics (such as the aforementioned gender, age, etc.) from the \"participant\" factor in the linear models; however, simple correlations indicate a link between glare perception and the drivers' age. Older drivers seem to subjectively perceive glare as more disturbing than younger drivers, although their psychophysiological responses to glare are weaker than in younger drivers. This finding might contribute to the present evidence on the influence of different driver characteristics on glare perception [22]. Finally, for future research, we recommend employing more behavioral measures and measures of visual performance in drivers' glare evaluations, as suggested by [11], such as including at least a simple visual detection task in the experimental procedure. As visual detection takes place naturally while driving (but not necessarily in the laboratory setting where participants were instructed to sit still and look straight ahead), the cognitive load induced might also affect drivers' responses to glare. This would allow better comparability of laboratory glare assessments with experiments in natural settings. Other variables, as well as the experimental setting and instruction itself [19; 22], might also influence results, so it is crucial to develop and follow a unified methodology for assessing glare perception by drivers [17].