During last decades, computer and smartphone technology has grossly affected the amount of time we engage in performing near tasks and has hypothetically contributed to the global rise of myopia.1,2 Generally, near work is an important factor in myopia, but it´s role is not fully explained yet – it could be due to the altered physiology during reading and studying, but it could also be just due to the prolonged time spent in this setting. It is also not clear how different psychological (or other) processes might contribute to it.

In learning, by definition, the cognitive demand is high. The extra sympathetic arousal arising from cognitive load is proven to have the capacity of altering the optical properties of the visual system – most notably accommodation and pupils. The pupillary behavior is well explored: elevated cognitive activity consistently causes mydriasis (enlargement of the pupils),3-6 whilst the effect of cognition on accommodation is less clear – some studies found that elevated cognitive effort causes induced transient myopia (i.e. retained positive accommodation),7 whereas others revealed accommodative fatigue8 and decreased accuracy.9,10

Both pupils and accommodation vary in time; small changes are referred to as microfluctuations. Pupils’ physiological restlessness (hippus) is presumably caused by dynamic equilibrium of both autonomic nerve systems, with parasympathetic input being dominant.11 In accommodation, oscillations are caused mainly by cardio-pulmonary cycles12 but depend also on the absolute value of accommodation.13 In work ergonomics, the long-term decrease of pupil size is considered a reliable measure of fatigue,14,15 whereas the alterations of accommodation are not believed to be associated with fatigue.15 An important factor of oscillations might be the modality of the presentation of stimuli: when looking into the source of light, i.e. the back-illuminated digital screen, the accommodative system is not as accurate as when observing an object which is illuminated by secondary light source.16-18

Studies show that myopic progression is closely related to the duration and level of education,19-23 to school grades as a measure of engagement24 and to seasonal variations of near work intensity.25 Some authors found also a connection to intelligence24 and proposed that the prevalence of myopia could be linked to the stressfulness of the school system.26,27 For instance, in the developed Asian region with traditionally rigorous schooling system, myopia is increasing28-30 and has now reached highest prevalence on the planet, whereas recent studies in the Scandinavian region which anecdotally has one of the most flexible and student-centered school system show that myopia there is actually staying low, despite unfavorable environmental lighting conditions.31,32 There is the genetic component to the myopia growth as well – the COMET study found that parents of myopic children themselves were also more myopic than average and had higher educational levels than average.33 Asian race seems to be more prone to short-sightedness; Asian students reach higher myopia growth rates during their studies, regardless of whether they are educated in home- or international environment.34

Since learning and studying is inevitably associated with high cognitive activity, we wanted to examine whether cognition has a measurable effect on physiological properties of the optical part of the visual system, and if yes, is the effect different with different types of cognitive activities. Causing stress to the visual system might in the short run result in a fatigue and in the long run, hypothetically, represent a factor in myopia growth.

To examine whether the selected tasks truly had different difficulty levels, we analyzed test achievements and subjective estimations of emotional and cognitive load. For each task, a principal component analysis of data on emotional valence, arousal, task difficulty, and required focus was first performed to reduce the amount of data. The results of nine principal component analyses showed that a one-component solution was acceptable for different tasks (only the first component's eigenvalue exceeded 1; the component accounted for 41% to 67% of the variance of responses about different tasks). The regression method was used for calculating the component scores, one for each task. The component scores and achievements in tasks of the same type but different difficulty were then compared using a one-way repeated-measure analysis of variance. In case of violation of the sphericity assumption, Huynh-Feldt correction was used. ANOVAs and post-hoc paired comparisons (Sidák tests) showed that for all types of tasks, subjective estimations of emotional and cognitive load increased from Level 0 to Level 2 as expected, and task achievements decreased with task difficulty. Results are shown in Table 1.

Next, we examined the values of visual system settings during different tasks. Average pupil size across tasks was 4.98 mm (SD = 0.77 mm). Average accommodative response was –1.69 D (SD = 1.24 D), representing a lag of accommodation of 0.81 D.

Descriptive statistics for physiological responses to different tasks are shown in Figs. 5–7. Since many distributions of the pairwise differences between the studied parameters found in different experimental conditions were statistically significantly different from normal (Shapiro-Wilk's test P < .05), we used two-way repeated ordinal regressions38 to analyze the effects of task type and difficulty level on the studied parameters. Inside the regression, medians (Mdn) were used to address non-normality.

Fig. 5.

Comparison of median values for pupil size (A) and relative refraction (B) in nine tasks defined by task type (numerical, textual, and Stroop) and level of difficulty (0, 1, 2). Whiskers represent 95% confidence intervals for the medians estimated by non-parametric bootstrap with 5000 samples and BCa correction.

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Fig. 6.

Comparison of standard deviations for pupil size (A) and relative refraction (B) in nine tasks. See also notes to Fig. 5.

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Fig. 7.

Comparison of slope parameters for pupil size (A) and relative refraction (B) in nine tasks. See also notes to Fig. 5.

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A 3 (task type) × 3 (task difficulty) repeated-measure ordinal regression was used to test the effect of task type and difficulty on six examined physiological parameters. Post-hoc Tukey-adjusted comparisons were done to compare different conditions pairwise. Table 2 shows the results of these analyses. Pupil sizes were statistically significantly different in different types of tasks. Pupil size was largest in the Stroop task (Mdn = 5.30 mm), followed by the numerical tasks (Mdn = 5.11 mm) and textual tasks (Mdn = 4.79 mm). Different types of tasks also resulted in different pupil standard deviations––in the Stroop task (Mdn = 0.23 mm) pupil standard deviation was slightly lower than in the numerical and textual tasks (Mdn = 0.26 mm). Tasks also differed in all three refraction parameters: in the average accommodation (its median value for the textual and Stroop tasks was –1.88 D and for the numerical tasks it was –1.74 D), in accommodation standard deviation (median SD was 0.47 D for the numerical tasks, 0.48 D for the Stroop tasks, and 0.58 D for the textual tasks), and in accommodation regression slope (median slope was 0.0005 D s − 1 for the numerical tasks, –0.0004 D s − 1 for the Stroop tasks, and 0.0013 D s − 1 for the textual tasks).

Task difficulty affected mean pupil size in the numerical tasks but had no such effect in the textual and Stroop tasks (the interaction term was statistically significant; see Table 2). The decrease in pupil size over time was smaller in the difficult numerical tasks compared to the easier ones, whereas in the textual and Stroop tasks no systematic effect of task difficulty on the slope parameter could be observed. To sum up, parameters of pupil size seemed to change systematically with task difficulty only in the case of the numerical tasks.

For the accommodative response (refraction) parameters, the interaction between task difficulty and task type was statistically significant, but no systematic and interpretable effects could be noted. We can see in Fig. 5B that task T2 which required intensive logical reasoning resulted in the smallest average lag of accommodation among all experimental conditions (average refraction value was closest to –2.5 D). However, at the same time the largest increase in the lag of accommodation with time was observed in this task (see Fig. 7B; the total change in refraction – increase in lag of accommodation – was approx. 0.43 D in 90 s). Even though the accommodative response in S2 condition was not statistically significantly different from the one measured in S0 and S1 condition, we noticed that task S2 evoked a lead of accommodation (excessive accommodation for the distance given) in 15 (27%) out of 56 participants. The effect, however, was not associated with any other measure.

Inspection of the correlations between different physiological parameters within tasks showed that in many tasks (but not all) the standard deviation and slope parameters were correlated. This might indicate that the change in pupil size or refraction with time was relatively consistent (toward larger or toward lower values). Achievements in different tasks were not correlated to accommodative or pupillary changes. The correlations of the component scores and the examined physiological parameters in various tasks were also very low (the correlation value that deviated most from 0 was –0.32).

The analysis of retained accommodation revealed no significant change of refractive posture; in the pre-test stage it was on average +0.045 D (SD = 0.42 D) and in the post-test stage it was on average +0.111 D (SD = 0.46 D), t(55) = –1.84, P = .121. There was also no statistically significant difference between emmetropes (n = 46) and corrected myopes (n = 10). In emmetropes, the pre-test refraction was +0.016 D (SD = 0.45 D) and the post-test refraction was +0.114 D (SD = 0.49 D), t(45) = –2.25, P = .057. In corrected myopes, the (relative) refraction in the pre-test measurement was +0.175 D (SD = 0.29 D) and in the post-test measurement it was +0.100 D (SD = 0.24 D), t(9) = 2.00, P = .081. It is to note that the change, although not statistically significant, was towards plus in emmetropes and towards minus in corrected myopes.

The study has shown that there is a general effect of cognitive processing on accommodation and pupillary response. The effect of cognitive activity on pupils has been explored before and it is now postulated that it takes place at the level of oculomotor nucleus.39 Our findings show that accommodation, too, is influenced by cognition, supporting the hypothesis about the locus of the effect, since both accommodation and pupil size are regulated from this same part of the brain.

It was previously reported that non-congruent tasks (i.e. tasks not related to the target observed), evoked less accurate accommodative response.40 In our study, however, different types of cognitive load were employed. Data processing in reading words and numbers and naming colors follows different paths.41 The fMRI technology revealed that the left parietal cortex is involved in the coding of the position of letters and their identity and it thus contributes to the early part of the process of reading.42,43 Its role might be in controlling the robustness of processing information. Carreiras et al.31 have shown that the robustness for changes within a set of characters is higher for letters than for numbers, which makes sense, since the rationale for reading is understanding of the whole words (and sentences, paragraphs, etc.). In numbers, on the other hand, the accurate information is defined by non-interchangeable order of numeric characters, so, contrary to the robustness in letters, high processing accuracy is required. In the Stroop task the situation is different again: in the interference condition where the naming of text color is required, the visual system must use the color channel only and resist the temptation to read the text, so a high level of robustness of letter processing is needed. For good performance in the Stroop task, high flexibility of choosing the correct processing channel in different conditions and strong inhibition of irrelevant visual information is important. The three different types of tasks in our study evoked essentially different ocular responses. It is interesting that the T2 task requiring logical reasoning evoked more accurate accommodation (smaller lag of accommodation) and smaller pupils than the S2 and N2 tasks, which is in favor of the robustness of text processing hypothesis, however, there were more oscillations of the refractive value in this condition. It is also interesting to note that in the Stroop interference task (S2) and the continuous calculations task (N2), the changes in the visual parameters across task difficulty were most congruent of all measures.

Pupils were significantly larger when performing the Stroop task than in performing the textual tasks. The reason for this might be the presence of incongruent stimuli in task S2 and the use of color naming in task S1 which increases cognitive load. However, compared to the T0, T1 and T2 tasks, pupils were also larger in the S0 task containing no incongruent stimuli, only reading of color words. The S0 task could therefore be regarded as a textual task as well. The difference between the textual tasks T0, T1 and T2 and the S0 task is that the first ones always included sentences containing a subject and predicate and some redundancies. In such sentences, some information may be removed but the meaning is still uniquely defined; the redundant information makes the text more readable. In contrast, the S0 task only included adjectives with no redundancies present, so the text was less readable, thus presumably requiring more attention for processing. Our results could lead to the conclusion that reading the content with little redundancies or the text which we do not understand or has no meaning for us affects pupil diameter.44 The increased pupil size is not in favor of physiology in reading, as the depth of focus decreases and allows the aberrations of higher order to worsen the image quality. At the same time, low readability, poor understanding of the text, or making lots of effort to understand the text could hypothetically be risk factors for myopia.

Many studies exploring cognition in near work used numbers or calculations to vary the cognitive effort.4,7,40 With the selected numerical tasks we found effects on pupils and accommodation that were different from the effects of other tasks – the pupillary oscillations in the numerical tasks decreased with increasing difficulty, whereas in the textual tasks the trend was opposite. Similarly, the accommodative oscillations decreased with highest cognitive load in the numerical tasks, and an opposite tendency was found in the textual tasks. With this in mind, it is interesting to note that myopia growth was found to be greater in students who were more linguistics oriented than in those who had better mathematical skills.24 In accordance with Langaas et al.36 and Harb et al.,45 we assume that, just like larger pupils mentioned before, also the oscillating refraction might be a factor associated with the axial elongation of the eye due to higher cognitive demand during reading. According to the results of our study, this association may be more pronounced in reading than in performing mathematical tasks.

The experiment, using short duration of experimental tasks (90 s), revealed no significant retained accommodation, neither in emmetropes, nor in myopes. However, the tendency of myopes shifting towards short-sightedness was noted, which is in accordance with other research findings that found a shift in tonic accommodation due to near work in young myopes.46

In the study, we found important influences of cognitive effort on pupils and accommodation. We discovered that pupil size was a good measure of cognitive effort in numerical tasks. However, in other domains, such as textual tasks or the Stroop task, pupils did not reliably represent increased mental activity. Accommodative responses varied more in textual tasks than in tasks in the other two domains. In the most difficult condition with textual information that required logical reasoning, the accommodative response decreased significantly over time (the accommodation lag increased). In the numerical task with the highest level of difficulty, the oscillations of accommodation were the smallest. Thus, the results of our study show that the effects of cognitive load vary across domains and are therefore not generalizable. Although our results are not fully conclusive, we can hypothesize that when performing demanding near tasks, at least one physiological parameter is altered toward less favorable values. Pupils are larger in conflicting tasks and when reading texts with low readability (as in the Stroop task) or when performing continuous calculations and the change in accommodative response over time is larger in tasks requiring logical reasoning and inference. These findings are consistent with current views on the effects of education on myopia growth and point to some potential risk factors.

In clinical practice, good myopia management includes optical and pharmacological interventions, but also advising about environmental factors. Patients are counseled on how to perform near work and how to adopt good ergonomics. We hypothesize that when reading, the content presented to young progressing myopes should be well matched to their comprehension ability, as excessive cognitive load appears to be a disturbing factor in the physiology of near vision. In addition, we would encourage school systems to favor clear, readable, and comprehensible texts and to encourage students to releasing strain while reasoning, if possible, for example, by fixating distant objects. Alternative ways of conducting high cognitive load learning activities, such as outdoor content discussions, might work as preventive strategies. Further studies should be conducted on the optical properties of the visual system in different learning situations to better understand potential educational risk factors for myopia growth.