## 4 Discussion
Finally, no studies have evaluated how cognitive processing varies with lighting conditions during VA measurement. Furthermore, our study did not observe a trend indicating that better test results correlate with faster response times. In Test 2, no illumination condition resulted in significantly better overall performance, neither when analyzed according to spatial frequency nor contrast. Generally, poorer performance was observed in terms of accuracy at lower spatial frequencies and faster response times at medium frequencies. According to the CS curve for normal healthy subjects, it would be expected that as the spatial frequency increases, the accuracy decreases, reaching a peak at medium spatial frequencies. However, this pattern was not observed in our results, possibly because the stimulus subtending angle requires a much higher spatial frequency than that measured, which is limited by the screen resolution. Moreover, as contrast decreased, there was a progressive increase in RT across all lighting conditions, except at the maximum contrast (C1), where slower response times were recorded compared to lower contrast levels (C2). This could be influenced by the learning effect for each spatial frequency. Additionally, [PERSON] et al. [40] reported an improvement in CS when both LCA and TCA were corrected simultaneously. However, no studies have measured CS under lighting conditions such as those in our study, indicating that this is a novel finding that could serve as a starting point for future research. Additionally, our test measures both contrast sensitivity and tilt angle sensitivity, offering a comprehensive assessment of CS-related visual performance similar to traditional clinical tests. For Test 3, when the stimulus appeared centrally, the average RT was faster for white illumination and slower for blue illumination, with a mean difference of approximately 44 ms. Moreover, when the stimulus appeared peripherally, the average RT was minimal for white illumination and maximal for red illumination, with a mean difference of approximately 41 ms. Furthermore, during the test where the stimulus appeared peripherally, the subjects exhibited poorer response inhibition control with red light, resulting in an average 2% higher error rate than those of the other two types of illumination. This difference was not observed when the stimulus appeared in the center of the screen (Figure 8). Previous studies have utilized the Go/No-go test to examine whether RT varies on the basis of the color of the stimulus to which participants must respond, typically under achromatic background lighting conditions [41; 42; 43; 44]. Some studies [41; 42] have reported faster RTs when the stimulus color was blue, which contrasts with the findings of this study. In contrast, [PERSON] et al. [43] reported faster RT with a red stimulus, whereas [PERSON] et al. [44] did not find significant RT differences between blue and red stimuli. In our study, despite white illumination (blue stimulus to respond to) resulting in a faster RT, no significant differences were found between blue illumination (red stimulus to respond to) and red illumination (blue stimulus to respond to). This finding suggests that this effect may have been partially masked by the background illumination to which the subjects were exposed. Moreover, other studies comparing RT under different lighting conditions [45; 46] did not find significant differences between red and white illuminations, contrary to our findings in this study. The emission spectra of the television and ceiling light differ, with the red television spectrum overlapping the red LED minima and affecting M-cones. However, these differences are not expected to significantly impact our study's focus on cognitive performance and alertness. With respect to the aberrometric measurements, significantly more myopic values of M were obtained in the baseline measurement than in all types of illumination. Similar results were reported in a study by [PERSON] et al.