## 4 Discussion
Experimental methodology, by definition, refers to the systematic approach of collecting and analyzing data through controlled observation and manipulation of variables to identify causal relationships or establish behavioral laws. Our study contributes to this field by demonstrating that time-of-day effects constitute an important yet underappreciated variable in cognitive performance studies. Including such temporal considerations in study designs enhances the experimental rigor and helps ensure that the observed effects are attributable to the variables of interest rather than to extraneous timing-related factors. Recognizing the influence of age and time of day on sustained attention can inform the optimization of work schedules in safety-critical fields such as transportation and healthcare. In educational settings, aligning instructional activities with peak alertness periods may enhance student performance and engagement. Additionally, understanding sex-related differences in attention can support the development of tailored interventions for individuals with attentional challenges, thereby broadening the real-world applicability of these findings. Despite the valuable insights gained from this study, several limitations should be acknowledged. First, although the dataset was relatively large and demographically diverse, participants self-selected their testing time based on personal convenience. This introduces the possibility of selection bias; for example, morning types may have preferentially chosen early time slots, potentially conflating circadian preference with time-of-day effects. Second, we did not collect information on participants' sleep patterns, caffeine intake, or chronotype [32; 33; 34; 35; 36]--all of which may influence attentional performance and interact with the timing of PVT administration. Future research should incorporate these factors to better isolate the contributions of the circadian phase versus sleep deprivation or lifestyle habits. Another limitation is that while the reaction time and attentional lapses were successfully modeled using age, gender, and time of day as predictors, the explained variance for some parameters--such as MNLs--was relatively low. This suggests that other unmeasured factors also play a significant role in determining sustained attention capacity. Moreover, for certain variables such as the standard deviation of the reaction time (SDRT), no robust predictive model could be established. This indicates the need for additional predictors or a more complex modeling framework in future studies. In this context, a number of additional factors--though not included in the present analysis--are known to influence sustained attention and psychomotor vigilance task (PVT) performance and may account for the unexplained variance observed in certain metrics. Notably, sleep deprivation and poor sleep quality have consistently been associated with slower reaction times and increased attentional lapses, even after modest reductions in habitual sleep duration. The chronotype, or an individual's circadian preference (morningness vs. eveningness), may also interact with the testing time to influence performance; for instance, evening-type individuals often perform suboptimally in the morning, regardless of sleep duration [36; 37; 38]. Other relevant factors include mental and physical fatigue, substance use (e.g., caffeine, alcohol, medications), and emotional state, such as acute stress or anxiety. Additionally, task motivation and engagement can influence response patterns, especially in monotonous tasks like the PVT. Environmental conditions, such as noise, lighting, or ambient temperature, may also subtly impact attention [39; 40]. Finally, the medical history, including conditions like ADHD, insomnia, or sleep apnea, has been linked to impaired vigilance [41; 42; 43; 44]. Future studies should consider incorporating these variables into predictive models to better capture the individual differences and improve the ecological validity of attention assessments.