Individual Differences in Sustained Attention: Effects of Age, Sex, and Time of Day Based on Psychomotor Vigilance Task Performance

Abstract

Sustained attention is a critical cognitive function, especially in contexts such as driving safety, where performance deterioration due to fatigue or drowsiness can have serious consequences. Although individual differences in sustained attention have been recognized and are known to decline with age, quantitative analyses considering sex and circadian timing are limited. In this study, we analyzed psychomotor vigilance task (PVT) data from 356 participants to investigate the effects of age, sex, and time of day on attention performance. Participants completed a 5-min PVT, and metrics including the reaction time (RT), minor lapses (MNLs, ≥5 ms), major lapses (MJLs, ≥8 ms), and false starts (FSs) were calculated. A general linear model was applied with sex and testing time (8:00–12:00, 13:00–16:00, 16:00–18:00) as fixed factors and age as a covariate. Stepwise multiple regression was also used to assess how well age, sex, and time of day explain performance. The RT showed significant differences by time and age (p < 0.001), with higher numbers of MNLs during morning sessions. Both sexes demonstrated significantly better performance in the afternoon compared to the morning. These results highlight the importance of controlling for the testing time in PVT-based experiments and underscore the measurable individual differences in sustained attention.

1. Introduction

Sustained attention—the capacity to maintain focus and respond to stimuli over extended periods—is a foundational component of human cognitive performance. It is critical in numerous real-world settings, such as driving, medical monitoring, and industrial operations, where lapses in attention can lead to errors, accidents, or even life-threatening consequences. Among the tools developed to quantify and assess sustained attention, the psychomotor vigilance task (PVT) has emerged as a gold standard due to its simplicity, reliability, and sensitivity to fatigue and circadian influences [1,2,3]. The PVT is commonly used in research on sleep deprivation, shift work, and cognitive aging, making it a key instrument in both experimental psychology and applied fields such as occupational health and transportation safety [4,5,6,7].

The ability to sustain attention varies widely across individuals. These individual differences are shaped by both intrinsic and extrinsic factors. Age is one of the well-documented determinants, with research consistently showing that older adults tend to exhibit slower reaction times and more attentional lapses compared to younger individuals [8,9,10,11]. This decline in vigilance performance with age has been linked to broader changes in cognitive processing speed, executive functioning, and arousal regulation. However, while age-related trends are relatively well understood, the effects of other demographic and situational variables, such as sex and the time of day when a task is performed, remain less thoroughly investigated in the context of sustained attention.

Biological sex may influence attentional processes through a variety of mechanisms, including hormonal cycles, stress reactivity, and differences in sleep patterns [12,13,14,15,16,17,18,19]. Some studies have reported subtle but significant differences in attentional control and vigilance performance between men and women, although the findings are often inconsistent or underpowered due to small sample sizes. Furthermore, the time of day is a crucial but frequently overlooked factor. Human cognitive performance follows circadian rhythms, which affect alertness and response capabilities throughout the day [20,21,22,23,24,25]. Morning and late afternoon may show differing patterns of attentional capacity due to natural fluctuations in sleep pressure and arousal levels. Understanding how these temporal effects interact with individual characteristics such as age and sex is essential for developing a more nuanced and generalizable model of sustained attention.

Despite the importance of individual and contextual factors in shaping cognitive performance, comprehensive datasets that allow for the concurrent evaluation of age, sex, and time-of-day effects on psychomotor vigilance task (PVT) performance remain scarce. Prior research has shown that sustained attention can decline with aging and that performance on vigilance tasks is sensitive to circadian influences and potentially sex differences. However, most existing studies examine these variables in isolation or within limited demographic samples, thereby reducing their applicability to real-world scenarios where multiple factors interact dynamically to influence attention. To address this gap, the present study analyzes PVT performance data from a diverse sample of 356 participants, incorporating metrics such as the mean reaction time (RT), minor and major lapses, and false starts. Participants were tested at different times of day (morning, early afternoon, late afternoon), and demographic variables such as age and sex were also recorded. This multidimensional dataset allows for the simultaneous investigation of how biological (age, sex) and contextual (time of day) factors contribute to sustained attention. Based on prior findings, we advance the following hypotheses. (a) Older participants will show longer reaction times and more attentional lapses compared to younger participants, reflecting age-related declines in sustained attention and processing speed. (b) Sex differences in PVT performance may exist, but due to the inconsistent findings in the literature, this investigation will be exploratory in nature, without a priori assumptions about directionality. (c) Time of day will significantly affect performance, with participants expected to perform better in the afternoon than in the morning, in line with circadian variations in arousal and alertness. By testing these hypotheses within a unified analytic framework, this study aims to clarify the combined and interactive effects of age, sex, and time of day on vigilance performance. In doing so, it contributes to a more nuanced understanding of attentional variability and provides insights relevant to operational contexts such as clinical assessment, shift work, and transportation safety, where accurate measurement of alertness is critical.

2. Materials and Methods

2.1. Dataset

This study analyzed data from a large-scale psychomotor vigilance task (PVT) dataset provided for research purposes by Crosswell Inc. (Yokohama, Japan), a Japanese company engaged in health sciences data monitoring and analytics. The dataset was obtained under a data-sharing agreement that ensures responsible secondary use and compliance with ethical standards. The data were fully anonymized through a process referred to as “irreversible anonymization”, or “unlinkable anonymization”, in which personally identifiable information such as names and contact details was deleted or altered to prevent any possibility of re-identification. Importantly, no correspondence tables or identifiers (e.g., ID numbers) linking the anonymized data to the original personal data were created or retained, thereby ensuring complete dissociation between the dataset and the individual participants. The PVT data were originally collected by Crosswell Inc. through their proprietary digital platforms under informed consent for general use in research and development. The dataset used in the present analysis qualifies as a form of quasi-open data. While it does not meet all the criteria for fully open data—such as unrestricted public accessibility—it is provided in a machine-readable format with clearly defined rules for secondary use. These data are thus considered appropriate for academic analysis under specific licensing and use conditions that permit secondary research. In this sense, quasi-open data offer an intermediate framework between fully open and closed datasets, promoting data-driven research while maintaining a degree of control over data sharing and interpretation. The dataset comprised PVT records from 356 participants (

Table 1. Psychomotor vigilance test (PVT) dataset (n = 356).

Participants	n	Age
All	356	48 ± 19 [18–76]
Male	214	48 ± 21 [19–76]
Female	142	46 ± 17 [18–73]

, Supplementary Materials). For each participant, the dataset included the age, the biological sex, and the time of day at which the task was performed. The PVT protocol lasted for five minutes and involved participants responding as quickly as possible to randomly appearing visual stimuli on a digital interface. Participants were instructed to respond promptly but accurately, minimizing both delayed responses and false starts. The reaction times (RTs) were recorded in milliseconds.

2.2. Psychomotor Vigilance Test (PVT)

The psychomotor vigilance task (PVT) is a standard cognitive test used to objectively evaluate sustained attention. Perception is the fastest reaction to visual stimuli that appear irregularly on a screen over a certain period of time (generally 5 min). The “RT” is recorded and the degree of insight is quantitatively evaluated.

We classified the minor lapses (MNLs, reaction time > 500 ms) and major lapses (MJLs, reaction time > 800 ms) from the RT of the dataset. In addition to the mean reaction time, we calculated the standard deviation of the reaction time (SDRT) and the number of false starts (FSs). An FS indicates a reaction that is too early before the onset of the stimulus, and we defined a reaction time of 100 ms or less as an FS.

The human reaction speed is the time from receiving a stimulus to starting a reaction, and although there are individual differences, it is known that it usually takes more than 100 ms. Although it varies depending on the situation and type of stimulus, many studies have reported that the average reaction time is about 180–200 ms for visual stimuli and 140–160 ms for auditory stimuli. Reactions are faster to predictable stimuli than to unpredictable stimuli, but still, anything shorter than 100 ms is considered a mistake.

The dataset consists of PVT tests voluntarily conducted by participants at any time between 8:00 AM and 6:00 PM, with the actual test times categorized into three distinct time periods: 8:00 AM–12:00 PM (morning), 1:00 PM–4:00 PM (afternoon), and 4:00 PM–6:00 PM (evening). The data are completely anonymized and do not contain any personally identifiable information. All the data processing, analysis, and storage procedures comply with the guidelines of the affiliated institutions regarding data protection, research ethics, and responsible data sharing.

Each index was calculated from the RT according to the following formula:

$$\mathrm{M}\mathrm{e}\mathrm{a}\mathrm{n} \mathrm{R}\mathrm{T}={\displaystyle \frac{1}{\mathrm{N}}}\sum _{i=1}^N{RT}_i$$

(1)

N: Number of valid responses.

RT_i: Reaction time for each trial.

$$\mathrm{L}\mathrm{a}\mathrm{p}\mathrm{s}\mathrm{e}\mathrm{s}=\sum _{i=1}^N{1}_{\{\mathrm{R}\mathrm{T}\mathrm{i}>\mathsf{\theta }\}}$$

(2)

θ: Threshold (500 ms and 800 ms).

1: Indicator function that is 1 if the condition is met and 0 otherwise.

$$\mathrm{F}\mathrm{S}=\sum _{i=1}^N{1}_{\{\mathrm{R}\mathrm{T}\mathrm{i}<100\mathrm{m}\mathrm{s}\}}$$

(3)

$$\mathrm{S}\mathrm{D}\mathrm{R}\mathrm{T}=\sqrt{{\displaystyle \frac{1}{N-1}}\sum _{i=1}^N{\left({RT}_i-\overline{RT}\right)}^2}$$

(4)

Equation (1) shows the average reaction time. However, since the average value is affected by major lapses and mistakes, in this study, following previous studies, we calculated the average value excluding major lapses and FSs. Equation (2) shows the minor and major lapses, respectively. Equation (3) shows the false starts. Following the definition of the reaction speed, it is defined as 100 ms. Equation (4) shows the SDRT. The standard deviation shows the variation in the reaction speed. Data with reaction times < 100 ms (FS) or >800 ms (MJL) were excluded from the analysis.

2.3. Statistical Analysis

The purpose of this study is to evaluate the effects of three major variables—age, gender, and time of day—on attention span. To achieve this objective, first, we verified the significance of the changes in the PVT parameters using a general linear model with the gender and time of day as fixed factors and the age as a covariate. Next, we used multiple regression analysis to verify whether the PVT parameters could be explained by the gender, time of day, and age. The parameter input method used was the stepwise method. This allowed us to verify the extent to which the predictor variables could statistically explain the variation in the PVT performance indicators. The statistical analysis was performed using IBM SPSS (v28), with a significance level set at p = 0.05 and partial eta squared (η²) values. η² indicates the proportion of the variance of the dependent variable that each independent variable (gender, time of day) explains. Since it is difficult to understand whether the difference is due to chance, due to the large sample size or is truly meaningful when the p-value is small, calculating η² makes it possible to judge the effect. The η² values were calculated using the following formulas.

$${\eta }^2={\displaystyle \frac{{ss}_{effect }}{{ss}_{effect }+{ss}_{error }}}$$

SS_effect indicates the sum of the squares of the factors (gender, time of day, etc.), and SS_error indicates the error sum of the squares.

The normality of the main PVT indices was confirmed using the Shapiro–Wilk test and Q–Q plot, and the homogeneity of variance was examined using the Levene test.

To examine the effects of age, sex, and time of day on sustained attention performance, we employed both a general linear model (GLM) and stepwise multiple regression analysis. These complementary statistical approaches were selected based on their suitability for both hypothesis testing and exploratory modeling, respectively. The GLM was used to test our a priori hypotheses regarding the main effects and potential interactions among the independent variables. This framework allows for the inclusion of categorical variables (sex, time of day) and continuous covariates (age) within the same model, enabling us to assess both the between-group differences and the continuous trends in performance. A GLM also facilitates the calculation of effect size estimates such as the partial eta squared (η²), which enhances the interpretability of statistical findings beyond simple p-values. In contrast, the stepwise multiple regression approach was used to explore the relative predictive power of each variable (age, sex, time of day) on various PVT metrics, such as the reaction time (RT), minor lapses (MNLs), major lapses (MJLs), and false starts (FSs).

3. Results

In this study, we analyzed PVT data from 356 participants to examine the differences in sustained attention based on age, gender, and time of day. The general linear model revealed that the reaction time (RT) was significantly affected by both age and time of day (p < 0.001), with longer RTs observed among older participants and those tested in the morning (

Table 2. Results of the statistical analysis of the PVT and partial eta squared (η²).

Sex	Time Class			Fixed Factor
	8:00–12:00	13:00–16:00	16:00–18:00	Sex			Time Class			Age			Sex × Time Class
	M (n = 93), F (n = 88)	M (n = 75), F (n = 38)	M (n = 46), F (n = 16)	F	p	η2	F	p	η2	F	p	η2	F	p	η2
Male	428 ± 8	297 ± 4	293 ± 4	5.656	0.018	0.016	153.1	<0.001	0.467	15.46	<0.001	0.042	1.400	0.248	0.008
Female	465 ± 9	312 ± 7	291 ± 7
Male	77 ± 7	82 ± 14	63 ± 3	1.435	0.232	0.004	0.318	0.728	0.002	2.193	0.140	0.006	0.378	0.685	0.002
Female	68 ± 3	62 ± 3	59 ± 4
Male	5.70 ± 0.41	2.20 ± 0.14	1.99 ± 0.16	3.826	0.05	0.011	69.10	<0.001	0.284	1.309	0.253	0.004	5.734	0.004	0.032
Female	8.18 ± 0.53	2.14 ± 0.21	1.93 ± 0.25
Male	1.54 ± 0.10	1.41 ± 0.09	1.26 ± 0.09	4.056	0.045	0.011	6.940	0.001	0.038	5.623	0.018	0.016	0.561	0.571	0.003
Female	1.45 ± 0.09	1.13 ± 0.08	1.09 ± 0.09
Male	2.50 ± 0.72	2.36 ± 0.66	0.80 ± 0.28	0.936	0.334	0.003	0.562	0.571	0.005	3.433	0.065	0.008	0.452	0.637	0.002
Female	1.68 ± 0.33	1.13 ± 0.28	0.80 ± 0.27

). Missed responses—minor lapses (MNLs) and major lapses (MJLs)—also occurred more frequently during the morning hours (p < 0.001), indicating decreased attentional performance. Gender differences were evident as well, with significant effects found in the RTs, MNLs, and MJLs (p = 0.018, 0.05, and 0.045, respectively).

Across both genders, the RTs, MNLs, and MJLs were significantly lower in the 13:00–18:00 time window compared to 8:00–12:00. Furthermore, the RTs and MNLs were particularly lower in males during the early morning hours (8:00–12:59), suggesting higher concentration and better performance in males during that period (Figure 1). Nevertheless, attention performance improved in the afternoon for both genders.

In the regression model for the RT, the age, gender, and time of day were significant explanatory variables. The standardized coefficients (β) indicated that younger women who performed the PVT in the early morning had longer RTs, with the time of day showing a particularly strong effect. In the model for MNLs, the gender and time of day significantly predicted performance, with higher MNL frequencies observed among women tested in the morning.

The RT model had a correlation coefficient of 0.7188 and explained 51.26% of the variance (

Table 3. HRV index of the seated rest control group.

Model	R (Multiple Regression Coefficient)	R2 (Coefficient of Determination)	Adjusted R2	Estimated SD	F	p
RT	0.718	0.5167	0.5126	69.0213	125.45	<0.0001
SDRT
MNL	0.4784	0.2289	0.2245	14.1334	52.38	<0.0001
MJL	0.1342	0.018	0.0152	0.8684	6.4	1
FS	0.1179	0.0139	0.0111	2.8737	4.9	1

). The MNL model showed a correlation coefficient of 0.4784 and explained 22.45% of the variance, indicating lower model accuracy (

Table 4. Coefficients of the RT model.

RT	Standardized Coefficients			T	p
	B	SD	β
Constant	511.8912
Age	−1.0232	0.2001	−0.263	−5.114	<0.0001
Gender	27.0655	7.604	0.1864	3.559	0.0004
Time period	−76.5889	5.245	−0.6142	−14.602	<0.0001

and

Table 5. Coefficients of the MNL model.

MNL	Standardized Coefficients			T	p
	B	SD	β
Constant	14.7669
Gender	6.1711	1.557	0.2064	3.963	0.0001
Time period	−8.6109	1.0091	−0.4135	−8.533	<0.0001

). For the SDRT, a valid predictive model could not be established.

The blue line indicates males and the red line indicates females. The horizontal axis indicates time. The vertical axis indicates the units of the RT and SDRT in ms, and the MNLs, MJLs, and FSs in the number of attempts. For both males and females, the RTs, MNLs, and MJLs are significantly lower from 13:00–18:00 compared to 8:00–12:00. RT and the NMLs are significantly lower in men from 8:00–12:59. Males have better concentration and perform better in the 8:00–12:59 time period, but when observed by time period, the performance is higher in both males and females in the afternoon.

Significant model equations were established for the RT and MNLs, and the RT could be explained with 51.26% accuracy with a multiple correlation coefficient of 0.7188. The MNLs could be explained with 22.45% accuracy with a multiple correlation coefficient of 0.4784, but the accuracy of the model was low. No model equation could be established for the SDRT.

Gender (male: 1, female: 2). The RT can be significantly explained by the variables of age, gender, and time of day. The standardized coefficient (β) shows that when the PVT is administered to young female at an earlier time of day, the RT value is larger (reaction time is longer), and the variable of time of day has a particularly strong influence.

Gender (male: 1, female: 2). The MNL variable was significantly explained by gender and time of day. The standardized coefficients (β) showed that the frequency of MNLs was higher in female when the PVT was performed earlier in the day.

4. Discussion

The present study investigated the individual differences in sustained attention as measured by the psychomotor vigilance task (PVT), focusing specifically on the influence of age, gender, and time of day. While it has been well documented in prior research that aging is associated with slower reaction times, the impacts of the measurement time and gender differences on attentional performance have not been sufficiently examined in large-scale datasets [1,3]. Our findings contribute new insights into the variability of attention, including its human physiology and ergonomics [26,27,28,29,30,31], providing new perspectives on the methodological considerations required when using the PVT in experimental and applied settings.

One of the most notable findings of this study was the significant effect of time of day on the reaction time (RT), minor lapses (MNLs), and major lapses (MJLs). Participants tested in the early morning (8:00–12:00) demonstrated significantly slower reaction times and more attentional lapses compared to those tested later in the day (13:00–18:00). This temporal pattern suggests that attentional performance is not uniform throughout the day and is likely influenced by circadian rhythms, which regulate alertness and cognitive performance across time. Although circadian effects have been widely recognized in sleep science, their explicit influence on sustained attention tasks like the PVT has not been adequately integrated into standard experimental protocols. Our findings suggest that time-of-day effects should be taken into account when designing studies or interpreting PVT-based outcomes, particularly in studies seeking to assess fatigue, sleep deprivation, or the impact of long working hours.

Additionally, gender differences were evident in our analyses. Female participants tended to exhibit longer reaction times and a higher frequency of minor lapses, especially during the early morning period. These differences may be partly explained by hormonal or neurobiological variations, but further studies are needed to understand the underlying mechanisms. The interaction between gender and time of day also merits further investigation, as it may indicate differential sensitivity to circadian- or fatigue-related factors between males and females.

Importantly, our findings highlight a key methodological issue that has been largely overlooked in experimental design: the timing of cognitive performance assessments. The PVT is often used as an objective measure to evaluate declines in attention associated with fatigue, disrupted sleep, or prolonged work. However, if the baseline attentional performance varies systematically depending on the time of day, this introduces a potential confounder that could affect the validity and interpretation of the findings. For example, a participant tested at 9:00 AM may appear more fatigued than one tested at 3:00 PM, even if both are equally well-rested, simply due to natural variations in alertness. As such, researchers using the PVT should either control for or statistically adjust for the measurement time, or ideally standardize the testing times across participants. This insight is particularly valuable from an experimental methodology standpoint, as it addresses how specific design choices—such as the measurement timing—can significantly influence the data outcomes.

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. While this study contributes to understanding the individual differences in sustained attention and diurnal variation, we recognize that our approach is not entirely novel. Prior studies have already examined the time-of-day effects on cognitive performance. Our findings, based on a relatively large and diverse sample, serve to reinforce and clarify these known effects.

In conclusion, this study emphasizes the importance of accounting for the time-of-day effects and demographic differences when using the PVT as a measure of sustained attention. The significant impact of the measurement timing on attentional performance, especially in interaction with age and gender, underscores the need for refined methodological approaches in cognitive and behavioral experiments. These findings contribute to the evolving framework of experimental methodology by identifying a novel variable—measurement timing—that can influence experimental outcomes and should be systematically controlled in future research. Furthermore, incorporating lifestyle and environmental factors could substantially enhance our understanding of attentional performance. Variables such as sleep quality, physical activity, nutrition, and stress management likely interact with demographic traits to shape attention. Future studies should also consider situational elements like the ambient noise, lighting conditions, and levels of distraction to ensure ecologically valid assessments of attention in real-world settings.

5. Conclusions

This study aimed to examine how sustained attention, as measured by the psychomotor vigilance task (PVT), varies across age groups, genders, and different times of day.

Our findings revealed several important patterns. Age and time of day were both significant predictors of reaction time, with older individuals and those tested in the morning showing longer RTs. Additionally, attentional lapses—including both minor lapses (MNLs) and major lapses (MJLs)—were more frequent in the early morning hours. Gender differences were also observed: women, particularly younger women tested in the morning, exhibited slower RTs and more frequent MNLs. The regression analysis supported these findings.

The key contribution of this research lies in its demonstration that the PVT outcomes are significantly influenced by the time of testing—a methodological factor that has often been overlooked. Since the PVT is widely used in research and clinical contexts to assess fatigue, sleep deprivation, and cognitive decline, the timing of the measurement should be carefully considered to avoid confounding effects. This insight has important implications for experimental design and contributes to the advancement of experimental methodology by emphasizing the need to control or adjust for time-of-day effects when assessing sustained attention. Future research should consider a wider range of factors influencing sustained attention. While the time of day proved important, attention is shaped by both internal variables, like circadian rhythms and lifestyles, and external factors, such as noise or lighting. Investigating their dynamic interplay may offer more comprehensive and ecologically valid insights into attentional performance.