1. Introduction
Our eyes are often said to be windows to our souls. Eye movements are highly sensitive representations of brain function and, therefore, of an individual’s health and wellness [
1]. Eye movements are measured noninvasively using eye-tracking technology that provides objective ways to understand neural pathways.
Some eye movements have clinical significance, such as tremors related to early-stage Parkinson’s disease [
2]. Other eye movements provide information regarding the muscle coordination of the eyes that allows individuals to see the world in three dimensions [
3]. Other eye movements may reflect expertise in a skill, such as that of a sport or diagnostic procedure (e.g., an experienced clinician examining a radiological chest X-ray) [
4].
Although eye-tracking hardware is becoming ubiquitous, and eye movement research is plentiful, limitations in setting benchmarks exist. Perhaps the most critical limitation is understanding how eye movements change across the lifespan. To date, researchers and clinicians struggle with answering fundamental questions, such as: What should be considered “functional” eye movement results across the lifespan? When do movements of the eye significantly change? How do they change? Which eye movements change? Answers to these questions will provide critical biomarkers to aid in understanding a person’s health and wellness. Furthermore, such markers can assist in rapid triage, informing treatment, and aiding in monitoring recovery.
As of the writing of this paper, twenty-one studies were found that examined eye movements as we age. In examining these studies, three main limitations were identified.
The first limitation is the small sample sizes. Normative data studies should be obtained from very large samples, typically many thousands of diverse participants [
5]. Of the twenty-one studies found, sample sizes range from groups in the single digits (e.g., Scherf et al.,
n = 9 for 10–13-year-olds,
n = 13 for 14–17-year-olds) [
6] to the largest sample size of 2993 participants in a group of 5–65-year-olds (e.g., Murray et al., 2019) [
7].
A second limitation includes the type of eye movements measured in these studies. The four major eye movements are fixations, saccades, pursuits, and vergence. Fixations are stopping points that hold an image on the fovea for detailed examination [
1]. Saccades are quick ballistic eye movements that reorient the eye to places across the field of view. Smooth pursuits eye movements follow an object through space, such as the motion of tracking a ball. Vergence eye movements are where the eyes move in oppositive directions so that images of a single object are placed or held simultaneously as the object moves closer or further away [
1].
Most studies focus solely on saccadic eye movements. There are a few exceptions, such as Kullman et al., who measured saccades and pursuits [
8]; Mokler and Fischer, who measured saccades and fixations [
9]; and Gould et al., who measured fixation alone [
10]. The limitation in not measuring all of the major eye movements leads to an incomplete picture of the visual and cognitive aspects that eye movements pose to health and wellness.
The final, third major limitation of normative data studies to date is, indeed, the lack of widespread examination of eye movements across the entire lifespan. Studies have shown differences in specific eye movements at certain ages. For example, Fukushima et al. observed that saccadic reaction time plateaued by 12 years of age [
11]. Others, like Kullman et al., have shown no age differences when comparing 18–21-year-olds (late adolescents) and 21–45-year-olds [
12]. Lenzenweger and O’Driscoll stated that they “limited the age to one group of 18–45 years to avoid potential age-related artifacts” [
13].
Murray et al. attempted to identify distinct groups by age using an unsupervised machine learning method [
7]. With a sample size of 2993, the model-based clustering used expectation maximization (EM) algorithm analysis. The results identified five distinct age group clusters: 5–8, 9–16, 17–28, 29–52, and 53–62.
Although an important step in data-driven age group determination of eye movements, Murray et al. still present two main limitations [
7]. First, there was a relatively small sample size [
7]. Second, there was a limited age range, especially regarding a lack of elderly adults [
7]. Therefore, per Campbell’s assertion that “normative data studies are typically obtained from very large, randomly selected representative samples of the whole population” [
5], the purpose of this study is multifaceted. First, this study aims to allow for eye movement data to drive the identification of age groups using a semi-supervised machine learning methodology. The second purpose is to include a large volume of age-based eye movement data from tens of thousands of participants (
n = 46,655) across the entire human lifespan to increase the generalizability of the results. The third purpose is to include the four major eye movements (fixations, saccades, pursuits, and vergence). Therefore, this study aims to identify age-based developmental biomarkers for eye movements that can be used as milestones for clinicians and researchers to inform decision-making on patients’ health and wellness and to guide future research methodologies.
4. Discussion
This study aimed to identify when and how eye movements change across the human lifespan to benchmark developmental biomarkers. The semi-supervised machine learning model stratified individuals into 12 age groups based on eye movement data with high accuracy (94.67%). Follow-up MANOVAs were used to indicate the strength of each age group; the results indicated a high level of significance (p < 0.001).
These findings reveal age-based developmental biomarkers associated with eye movements across many participants (
n = 46,655) and a vast age range (6 to 80), adding to the current body of knowledge by enhancing the generalizability of age-based, eye-tracking biomarkers [
7,
8,
11,
12].
An interesting observation regarding the age groups identified through the eye movement variables is that young and elderly individuals have smaller age ranges when compared to other stages of life. For example, Group 1 includes 6-to-7-year-olds, a two-year age span. Group 2 includes 8-, 9-, 10- and 11-year-olds, a 4-year age span. Group 12 includes 77-, 78-, 79-, and 80-year-olds, again a 4-year age span. In the early stages of development and later stages of decline, the differences in eye movements related to age change more rapidly [
1]. In early adulthood, from 15 to 25, there is little change, according to our results. Then, in middle age, change seems to occur about every five or six years, as seen by the age groups 26 to 31 (Group 5), 32 to 38 (Group 6), and 39 to 45 (Group 7). These results are consistent with other life span development research, such as that which has found a linear reduction in processing speed as we age [
20] and that our thinking abilities appear to peak around age 30 on average and then decline subtly with age [
21].
This study shows that age affects eye movement behaviors in broad and significant ways, consistent with the current body of literature. The four major eye movements (fixations, saccades, pursuits, and vergence) were examined via the different tests (CSP, HSP, VSP, HS, VS, and FS variables). Follow-up MANOVAs revealed highly significant differences across all testing protocols.
Interestingly, a pattern is revealed when reviewing individual variables throughout
Table A8,
Table A9,
Table A10,
Table A11,
Table A12,
Table A13 and
Table A14, showing an inverted bell curve in the results. For example, latent smooth pursuit (
Table A8,
Figure 5) is higher (worse) among the young (6- and 7-year-olds with a median of 26.57%). Then, there was a consistent reduction in percentage, showing an improvement in eye movement behavior until middle adulthood (Group 6: 32–38-year-olds). This is followed by a consistent increase in latent smooth pursuit percentages from middle age to elderly (Group 12: 77–80-year-olds) showing similar results to the very young (Group 1, 6-to-7-year-olds, and Group 2, 8–11-year-olds).
This inverted bell curve reveals a change toward improvement in eye movement behavior until ones 30 s, then a gradual, consistent, and significant decline until 80 years of age. The decline is at a more gradual rate than the improvement seen in early years. Furthermore, the decline does not reach the initial, early age levels of high latent smooth pursuit percentages, indicating poor performance. This trend occurs not only in smooth pursuit eye movement metrics but also in saccades, fixations, and vergence-related variables (e.g.,
Table A12: On Target). Sensitive measures of variance and efficiency also follow the same pattern (see
Table A12). These findings support other lifespan developmental research indicating changes early and late in life, with peak abilities stabilizing in early adulthood [
20,
21,
22].
Eye movement speed is slowest in Groups 1 and 12, the young and elderly (
Table A11, saccadic amplitude, 188 and 191 degrees, respectively). However, when viewed in conjunction with saccadic targeting, we see that the elderly are slower and more accurate than the young (9.18 in Group 1 and 11.05 in Group 12). This is further validated when viewing the results of the speed–accuracy trade-off (
Table A11), whereby there is a reduction in speed in both Groups 1 and 12 but a higher accuracy even with the speed reduction for Group 12, indicating a trade-off that reveals slower speed with greater accuracy for the elderly population (3.60). In contrast, in middle age, people can be both fast and accurate with their eye movements, as demonstrated by Group 5, 26-to-31-year-olds, whose speed–accuracy trade-off was almost double that of the young and the elderly (5.67). These results are consistent with research that shows older adults are slower than younger adults in completing most tasks [
22]. Some reasons for this may include older adults being reluctant to commit errors and unwilling to adopt a “fast-and-careless” attitude.
These results may be useful in future eye movement studies where people of different ages participate. Adjustments to the design of research, specifically the methodologies and related results, should carefully consider the impact of age. Studies may consider grouping participants by age or only including specific age ranges, should they wish age, not to be a confounding variable.
This model holds many advantages over models utilized in the past. Its ability to accurately differentiate by age groups solely based on eye movement data sets it apart. This differentiation has allowed for a more thorough identification of developmental biomarkers. The work and findings are predicated on a large sample size, thereby enhancing the generalizability of the findings.
While this study marks the potential for updating our understanding of age-related changes in eye movement patterns, certain limitations exist. Specifically, there are limitations in its use of cross-sectional cohorts [
23]. Although this study attempts to mitigate this limitation by having a large sample size, future research should consider longitudinal tracking to assess possible generational differences that could serve as confounders (e.g., technology usage). An examination of the data may further stratify persons into age–gender cohorts. On a related note, a limitation does, indeed, exist, in that we had more female participants than male participants. This introduces the potential for bias predicated on gender, a limitation future work should aim to address. Finally, charting typical developmental trajectories in each type of eye movement (fixations, saccades, pursuits, and vergence) may further stratify groups and, in turn, may more precisely assist clinicians and researchers in defining eye movements across the human lifespan.
Eye movements, measured using eye trackers, provide quantifiable reflections of eye movement behavior that may, in turn, be used to group persons based on age. Such information may be used as a digital biomarker. Digital biomarkers are objective measures that capture the state of a cell or, in this case, the eye movements of a human being [
24]. Digital biomarkers are collected via computing systems such as digital services, wearable technology, or computer technologies that can explain, influence, or predict health-related outcomes [
25].
Digital biomarkers can play an important role in uncovering a person’s health and wellness for early disease detection, enabling healthcare providers to administer early and targeted treatments that may slow, reduce, or even cure disease in patients [
26]. As such, it is vital to be able to compare expected (normative) controls with potential disease states [
27,
28]. Similarly, the biomarkers identified have implications for clinicians in practice, as these biomarkers could allow for detecting various disorders that would be otherwise difficult or delayed without quantifiable metrics. In this regard, these metrics allow for establishing benchmarks that are fluid with age instead of uniform across the lifespan. For example, the inverted bell curve illuminated in this work subsequently enables professionals to adjust patient expectations, thereby enhancing individualized care. As it pertains to assistive technology, clinicians can and should consider facets such as the speed–accuracy trade-off to inform both its development and adjustment to meet the needs of populations for which these devices are meant to support. These data can be used to establish baseline measures and monitor the patient over time. As eye movements significantly change over the lifespan, these normative comparisons for biomarker conclusions must be accurately determined using the appropriate age-related considerations. When biomarkers are evaluated within the context of demographic factors, the results may provide clinicians with important, previously unknown information that can aid clinical decision-making [
29].
The advancement of medical science has brought remarkable improvements in the diagnosis and treatment of diseases. However, many diagnostic techniques remain invasive, often causing patients discomfort, risk, and potential complications. This study has identified eye movements as noninvasive biomarkers of human function correlated with age that are easy to obtain and quantify. There are high correlations between pathologies of eye movement and a host of neurological disorders [
1]. Eye movement biomarkers have great promise to revolutionize disease detection, monitoring, and treatment, enhancing patient care and outcomes. Developing noninvasive disease biomarkers with untold potential benefits, current advancements, and future implications is essential.
Many diseases, particularly early-stage diseases, might not be easily accessible or identifiable. Noninvasive biomarkers such as eye movements present an exciting purpose to mitigate these issues as further clinical research develops a greater understanding of human function in health and disease. The statistically different performance of eye movements throughout the life span provides baseline measurements of function that might be compared to each individual. Deviations from expected baseline performance promote a deeper investigation of other human functions and systems integrity to identify disease or functional pathology before it becomes clinically evident.
Early detection of pathology can frequently result in an improved prognosis. Eye movement deviation from the baselines we have quantified can facilitate the early detection of diseases crucial for conditions like cancer, cardiovascular diseases, and neurodegenerative disorders. An early diagnosis often leads to better treatment outcomes and improved survival rates. Patients can be expected to be more comfortable with biomarker identification through noninvasive technology instead of invasive testing, as described in our investigation. This reduction in discomfort can lead to higher compliance rates for regular monitoring and follow-up, which is essential for managing chronic diseases and monitoring treatment efficacy.
A cost-effective consequence of utilizing eye tracking as a noninvasive diagnostic method can be realized by requiring fewer resources and infrastructure than invasive techniques. This cost reduction can make healthcare more accessible, particularly in resource-limited settings. Eye tracking technology allows for frequent and real-time monitoring of disease progression and treatment response monitoring, enabling better control and management of various medical conditions correlated with brain function and volitional eye movements.
Significant progress has been made in identifying and validating correlations between eye movement, brain function, and various clinical syndromes. This investigation quantifies eye movement by age groups with sample size and power necessary to establish biomarkers across the life span, increasing the value of eye movement as a noninvasive biomarker. The continued development of noninvasive biomarkers, such as those identified in this investigation, holds immense promise for the future of medicine. As research progresses, identifying novel biomarkers and refining detection technologies will likely lead to more accurate, efficient, and patient-friendly diagnostic methods. Moreover, noninvasive biomarkers will significantly benefit the desire to establish personalized medicine. These biomarkers can provide detailed insights into an individual’s unique health profile, enabling tailored treatment strategies that improve outcomes and reduce adverse effects.
Developing noninvasive biomarkers is a crucial advancement in modern medicine, offering significant benefits over traditional invasive diagnostic methods. Eye movements as noninvasive biomarkers can transform disease diagnosis and management by facilitating early detection, improving patient comfort, reducing costs, and enabling real-time monitoring. Continued research and innovation in this field will undoubtedly lead to improved health outcomes and a higher quality of life for patients worldwide. As we move forward, integrating these biomarkers into clinical practice will be essential in realizing their full potential and advancing healthcare for all.
This investigation of eye movements across the human lifespan has generated significant new knowledge, advancing our understanding of how eye movements change with age. We have identified developmental biomarkers that can be used in clinical and research settings. The primary breakthrough of this research lies in its ability to accurately categorize individuals into distinct age groups based solely on eye movement data. This was achieved with remarkable accuracy by studying a vast age range with a large sample size, ensuring the robustness and generalizability of our findings.
One of the key findings in our work is the identification of age-specific changes in eye movement patterns. This investigation revealed that the youngest and oldest age groups exhibit more rapid changes in eye movements, resulting in narrower age spans for these groups. This suggests that early development and late-life decline are periods of heightened sensitivity to changes in eye movement behavior.
Our study uncovered complex patterns in eye movement behaviors that vary significantly with age. The analysis demonstrated a unique pattern: eye movement efficiency improves until the early 30s and then gradually declines. This pattern was consistent across various types of eye movements, including fixations, saccades, pursuits, and vergence. For instance, latent smooth pursuit metrics showed higher (worse) values in young children, a consistent reduction (improvement) in early adulthood, and then a gradual increase (worsening) in older age.
Additionally, the speed–accuracy trade-off revealed further complexity in eye movement patterns. While the youngest and oldest groups exhibited slower eye movement speeds, elderly individuals demonstrated higher accuracy, suggesting a prioritization of accuracy over speed. This finding indicates that older adults may adopt more cautious eye movement strategies and are likely to avoid errors, which aligns with broader research on age-related changes in cognitive and motor functions.
The new stratification of eye movement function by age developed from this research has the potential to significantly upgrade assistive technologies and healthcare stakeholders’ decision-making. Accurately differentiating age groups based on eye movement data can enhance the development of personalized assistive technologies. For example, eye-tracking devices can be tailored for different age groups’ specific needs and capabilities, improving usability and effectiveness for young and elderly individuals.
In healthcare, identifying age-related eye movement biomarkers can aid in early detection and monitoring of neurological and cognitive disorders. Clinicians can use these biomarkers to establish patient baseline measures and track changes, allowing for timely interventions. For instance, deviations from normative eye movement patterns could signal early stages of conditions such as Parkinson’s disease or dementia, enabling healthcare providers to administer targeted treatments that may slow or mitigate disease progression.
Moreover, understanding the nuances of eye movement patterns across the lifespan can inform the design of clinical studies and trials. Researchers can group participants more accurately based on age-related eye movement data, reducing the potential for age to confound study results. This precision can lead to more reliable and valid findings, ultimately enhancing the quality of research on eye movements and cognitive health.
Our investigation has produced substantial new knowledge by elucidating how eye movements change across the human lifespan while identifying developmental biomarkers associated with these changes. The discovery of complex patterns in eye movement behavior, such as the inverted bell curve and the speed–accuracy trade-off, adds depth to our understanding of the factors affecting eye movements. The functional performance identified from this research holds significant promise for upgrading assistive technologies and informing healthcare decision-making. Eye movement biomarkers can improve patient outcomes and advance developmental and clinical research by enabling early detection and personalized interventions.
One of this research’s primary breakthroughs is its remarkable ability to accurately categorize individuals into distinct age groups based solely on eye movement data. This innovative approach leverages the power of semi-supervised machine learning to analyze and interpret complex patterns in eye movements, yielding a high accuracy rate of 94.67%. Such precision enhances our understanding of developmental biomarkers and opens new avenues for practical applications in healthcare and technology.
Eye movements are a window into the brain’s functioning and reflect many cognitive processes. The four major types of eye movements—fixations, saccades, pursuits, and vergence—are influenced by age-related changes in neural and ocular systems. By capturing and analyzing these movements, researchers and clinicians can identify subtle variations correlating with different stages of human development and aging. This study collected eye movement data from a large and diverse sample of 46,655 participants ranging from 6 to 80 years old. This extensive dataset enabled the machine learning model to detect and learn the unique characteristics of eye movements associated with each age group. This granular analysis revealed patterns that are not easily discernible through traditional observational methods, thus highlighting the sophistication and capability of machine learning in biomedical research.
This study’s semi-supervised machine learning model is designed to handle vast numbers of data, identify patterns, and make accurate predictions. It combines elements of supervised learning, where the model is trained on a labeled dataset, with unsupervised learning, where it identifies hidden patterns in unlabeled data. This hybrid approach enhances the model’s ability to generalize from the data and improves accuracy.
By inputting eye movement data into the model, we stratified individuals into 12 distinct age groups based on intricate details of eye movement behavior, such as the frequency and duration of fixations, the amplitude and speed of saccades, the smoothness of pursuit movements, and the coordination of vergence. These parameters vary systematically with age, reflecting developmental and degenerative changes in the brain and visual system.
The ability to categorize individuals into age groups based on eye movement data has profound implications for identifying developmental biomarkers. Developmental biomarkers are measurable indicators that reflect the human body’s average growth and aging processes. In this context, eye movement patterns serve as biomarkers that can track cognitive and neural development progression across the lifespan. The breakthrough in accurately categorizing age groups based on eye movement data has significant practical applications. In healthcare, this capability can enhance the early detection and monitoring of neurological and cognitive disorders. Clinicians can use age-specific eye movement biomarkers to identify deviations from typical development, enabling timely interventions. For example, abnormalities in eye movement patterns could indicate early stages of neurodegenerative diseases like Parkinson’s or Alzheimer’s, allowing for earlier diagnosis and targeted treatment.
Using assistive technologies, this research can inform the development of personalized eye-tracking devices. These devices can be tailored to accommodate different age group’s specific needs and capabilities, improving usability and effectiveness for young children and elderly individuals. By incorporating age-related variations in eye movement behavior, these technologies can provide users with more accurate and reliable support. Furthermore, the insights gained from this study can improve the design of clinical trials and research studies. By grouping participants based on precise age-related eye movement data, researchers can reduce the potential for age to confound study results. This precision enhances the validity and reliability of research findings, ultimately advancing the field of developmental and clinical neuroscience.
Accurately categorizing individuals into distinct age groups based solely on eye movement data represents a significant breakthrough in biomedical research. This innovative approach leverages the power of machine learning to uncover intricate patterns in eye movements, providing valuable insights into developmental biomarkers. The practical applications of this research are vast, ranging from early detection and monitoring of neurological disorders to the development of personalized assistive technologies. This study paves the way for improved healthcare outcomes and technological innovations by advancing our understanding of age-related changes in eye movement behavior.
Age-related diseases manifest at different stages of life, each with unique challenges and implications. Understanding the progression and characteristics of these diseases from childhood to old age is crucial for effective diagnosis, treatment, and management. This investigation explores the spectrum of age-related eye movement performance. It promotes applications to health conditions that affect individuals at various life stages while allowing for discussion of their impact on health and quality of life.
During childhood, age-related diseases often stem from congenital and developmental origins. These conditions can significantly impact physical, cognitive, and emotional development, requiring early intervention to improve long-term outcomes. Eye movement biomarkers can document whether a child has obtained a similar performance capability as others of the same age. These biomarkers also serve to assist in the assessment of the success or failure of a variety of treatment interventions.
Age-related diseases span the entire human lifespan, from congenital and developmental disorders in childhood to chronic and neurodegenerative conditions in old age. Each stage of life presents unique challenges and opportunities for intervention. Understanding the progression and impact of these diseases is crucial for developing effective prevention, diagnosis, and treatment strategies. Identifying and quantifying eye movement performance by age has been central to this investigation. By addressing the specific needs of individuals at different life stages, healthcare providers can improve outcomes and enhance the quality of life for patients across the lifespan. Continued research and innovation are essential to tackle the evolving landscape of age-related diseases and to ensure optimal care for all individuals.
The continued development of eye movements as noninvasive biomarkers holds immense promise for the future of medicine. Identifying novel biomarkers and refining detection technologies will likely lead to even more accurate, efficient, and patient-centered diagnostic methods as research progresses. Developing noninvasive biomarkers is a crucial advancement in modern medicine, offering significant benefits over traditional invasive diagnostic methods. Eye movement quantification fits this developmental challenge well. Noninvasive eye movement biomarkers can potentially transform disease diagnosis and management by facilitating early detection, improving patient comfort, reducing costs, and enabling real-time monitoring. Continued research and innovation in this field will undoubtedly lead to improved health outcomes and a higher quality of life for patients worldwide. As we move forward, integrating these eye movement biomarkers into clinical practice will be essential in realizing their full potential and advancing healthcare for all.