Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# Individuals with dyslexia use a different visual sampling strategy to read text

## Introduction

One group that is well-known to struggle with developing proficient and fast reading skills are individuals with dyslexia. Dyslexia is a language-based, neurobiological specific learning disorder affecting reading, writing, and spelling10,11, which persists into adulthood10,12,13,14. Specifically, struggles with accurate and/or fluent word recognition and decoding abilities that typically result from deficits in phonological awareness characterize this disorder10,11. Slow reading and a deficit in reading comprehension can be resulting secondary consequences10,11,15. An estimated 5 to 20% of the population are affected11,12,15,16. Dyslexia’s aetiology remains the subject of a heated debate with proponents attributing the main underlying cause to deficits in a variety of systems associated with reading (i.e., phonological awareness, visuo-spatial attention17, magnocellular and cerebellar function18,19,20,21, or a lack of reading experience22,23). While deficits in phonological awareness are considered established24,25,26,27,28, other deficits in low-level sensory processing29,30,31,32,33 and visual attention remain under scrutiny (e.g.,34).

Recent evidence stresses that at least one subtype of dyslexia is affected by differences in visual processing42, which can be detected within eye movement recordings43,44. For example, Nilsson Benfatto and colleagues43 were able to reliably distinguish between 9–10 year-old children at high- and low-risk of dyslexia using a classification algorithm operating on one-minute eye movement recordings. Specifically, the duration of fixations, and the number of fixations, saccades and regressions were found to be the most predictive eye movements for differentiating between children at high and low risk of persistent reading difficulties. This finding is in line with previous research showing that readers with dyslexia exhibit longer fixation durations45,46,47, an increased number of fixations47, shorter saccades1,46,47,48, and fewer skipped words3,49,50. Conversely, the probability of revisiting a previous part of a text (i.e., expressing a regressive saccade also termed regression) has not proven to be reliably different in dyslexia3,46. It remains unknown, however, if a similar pattern classification is possible for adults with dyslexia across a range of different texts that are presented in different fonts.

Many advances in dyslexia eye movement research have been made in recent decades. Most of our current knowledge about differences in eye movements in dyslexia is provided by researchers investigating either a limited number of eye movement metrics in relation to specific linguistic aspects most often embodied by a target word (e.g.,51,52,53), or is limited by the use of a large variety of often controlled but non-standardized linguistic stimuli in several languages with varying orthographic depth ranging from one character up to one or two sentences54,55,56,57. Hence, a comprehensive profile of the eye movements of adults with dyslexia during naturalistic reading of standardized texts of multiple sentences remains surprisingly unknown. The development of a comprehensive profile would allow to uncover and quantify potential inefficiencies in visual sampling of text that have not come to light using the aforementioned focused, local approach. Therefore, we aim to devise a comprehensive eye movement account of adult dyslexia by investigating how eye movement patterns of individuals with dyslexia differ from those without dyslexia on global (text-based) and local (word-based) reading measures during an ecologically valid silent paragraph reading task in English (Fig. 1a).

Based on previous research (e.g.,13,73), we hypothesize that individuals with dyslexia, compared to an age- and education-matched control group without dyslexia, will take longer to read each text and show slower visual processing speed that is in turn linked to one’s reading duration. Reading texts in the dyslexia-friendly font OpenDyslexic is not expected to result in increased reading speed. In terms of eye movements, we hypothesize that readers with dyslexia will express more eye movements (i.e., fixations, saccades, and regressions), longer fixations and shorter saccades. Scanpaths of readers affected by dyslexia are hypothesized to be longer and to differ in their sequence and duration of eye movement events as a result of increases in reading duration.

## Results

In this study, we focused on group-level differences in behavior and eye movements between adults with and without dyslexia. Behavioral analysis included an investigation of the dependent variables reading duration, attention to the text and non-linguistic cognitive processing speed as a function of the two experimental groups (i.e., Dyslexia and Control). Eye movement analyses examined global (i.e., paragraph/trial-based) and local (i.e., word/interest area-based) metrics of eye movement events during reading.

### Behavioral results

We constructed a generalized linear mixed-effects model (GLMM) for analyzing reading duration as a function of the predictors: group (Dyslexia and Control) and font (Times New Roman and OpenDyslexic), and their font-by-group interaction on a single-trial level. The predictor font was also included as a random effect, which was allowed to vary by participant. In addition, the predictors group and font and their interaction were included as random effects, which were allowed to vary by text. This model was based on 601 experimental trials (i.e., one value for median reading duration per trial) collected from all 67 participants. Text number five had to be excluded across all participants due to a stimulus presentation issue (67 trials; 10%). Two further trials from the dyslexia group had to be excluded due to recording issues, which resulted in 601 trials being included in all analyses (for details, see “Methods”).

This GLMM demonstrates significant predictive power of the main effect of group on median reading duration with individuals with dyslexia taking longer to read each text (X2 = 13.431, df = 1, p < 0.001; Fig. 2a,b; see Table 1 for detailed model statistics). This speed difference is underlined by a lower words per minute reading rate among readers with dyslexia (MedianDyslexia = 178.09, MedianControl = 248.18; two-sided independent sample t-test: t(65) = 20.51, p < 0.0001; g = 1.67, 95% CIg = [1.486, 1.858]; Fig. 2a). However, this model yields neither a significant improvement in reading duration with font (X2 = 1.41, df = 1, p = 0.235) nor a significant font-by-group interaction (X2 = 0.446, df = 1, p = 0.504).

Secondly, the multiple-choice questions presented immediately after reading each text served as an incentive for participants to read each text for comprehension—hence, constituting an indicator of attention. Both groups demonstrate attention to the texts clearly above chance level (control: t(31) = 17.67, p < 0.0001; BF10 = 5.1457 × 1014; Min = 66.67%; dyslexia: t(34) = 15.96, p < 0.0001; BF10 = 2.2968 × 1014; Min = 55.56%; all two-sided paired t-tests; Fig. 2c). That is, two participants in the dyslexia group scoring at 55.6% and 57.1%, nine participants across both groups showing performance at 66.7%, and the remaining 56 (out of 67) participants answered more than 75% of the attention questions correctly, with 31.3% of all participants answering all questions correctly. Crucially, our data show no significant evidence for a difference between both groups (two-sided independent samples t-test: t(65) = − 0.34, p = 0.7349; BF10 = 0.2635; Fig. 2c). Altogether, this analysis demonstrates that both groups paid attention to the reading material, as their performance is better than chance across all trials.

Thirdly, we examined if there were any non-linguistic cognitive processing speed differences between our two groups using two subcomponents from the Wechsler Adult Intelligence Scale (Coding and Symbol Search74). This analysis was motivated by previous reports of links between reading speed and slower cognitive processing speed in individuals with dyslexia75. We find that individuals with dyslexia exhibit slower processing speed on the Coding test (two-sided independent samples t-test: t(65) = 5.88, p < 0.0001; g = 1.422, 95% CIg = [0.895, 1.973]; Table 2; Fig. 2d), but not on the Symbol Search test (independent samples t-test: t(65) = 0.399, p = 0.69; g = − 0.1, 95% CIg = [− 0.382, 0.577]; Table 2; Fig. 2d). Our data further show a negative correlation between coding speed and reading duration across all participants (r65 = − 0.51, p < 0.0001, 95% CIr = [− 0.680, − 0.315]; Fig. 2e) suggesting that, in the present study, participants with better coding ability (i.e., faster number related cognitive processing speed) exhibit shorter reading duration. Symbol search speed did not correlate with reading speed across participants (r65 = 0.12, p = 0.33, 95% CIr = [− 0.106, 0.35]). Although these single measures both probe visual processing speed, their separate interpretation warrants caution, since they may not provide a full representation of one’s processing capabilities as outlined in their use76.

These results appear to be the consequence of the control group showing coding speed above the general population average while the dyslexia group shows performance slightly below the general population average (Mpopulation = 10, MControl = 12.84, MDyslexia = 9.40; Table 2; Fig. 2d). One reason for these results might be that the coding task encompasses working memory performance to some degree77. Memorizing digit-symbol pairs only shown at the top of the page more quickly may constitute a strategy for achieving a higher score on this test. Hence, although not explicitly testing working memory performance, these results may be indicative of working memory deficits in adults with dyslexia when compared to a similarly educated non-dyslexia group—in line with previous reports78,79,80,81,82—and their role in achieving age- and education-appropriate reading speed. However, we find no general visual processing speed deficit in dyslexia as both the Coding and Symbol Search test need to be considered in unison76.

In short, our behavioral results show a sustained level of attention to the stimulus material throughout the majority of this study by most participants. Though, readers with dyslexia exhibit generally slower reading speed in line with previous reports. One potential explanation of the observed reading speed deficit might be a difference in the skills probed by the non-linguistic Coding processing speed test but not a general visual processing speed difference.

### Eye movement profile

This study aims to devise a comprehensive characterization of the eye movement profile of individuals with dyslexia during natural paragraph reading. This profile comprises eye movement metrics traditionally examined in the field (Fig. 3), and other more recent metrics such as line-initial fixation duration, scanpath similarity, and specific saccades atypical for reading (Fig. 4). The reading-related metrics covered in this study include global (i.e., trial-/text-based) and local (single-word based) metrics. To establish the significance of a group difference in the frequentist sense, we use unbiased effect sizes83 (i.e., the 95% confidence interval of Hedges’ g not including zero; denoted as g in the text; Fig. 5a). A negative effect size indicates a longer duration or larger number exhibited by the dyslexia group and vice versa. High collinearity between some metrics included in our analyses did not allow for the use of a meaningful linear regression approach. In general, where applicable, we report group means alongside group medians to complement this robust measure of central tendency and ensure comparability to previous literature.

Global metrics, however, do not provide more detailed information on specific elements of the visual sampling strategy in relation to single words such as how many of these words get actively fixated and how often. To address these questions, we complemented the reported global metrics with metrics based on the definition of interest areas around single words. We find that individuals with dyslexia spent more time (g = − 1.57, 95% CIg = [− 1.75, − 1.39]; Fig. 3d and First Run Dwell Time in Fig. 5a,b), and skip fewer words (g = 0.40, 95% CIg = [0.235, 0.557]; Fig. 3e and Ratio First Run Words Skipped in Fig. 5a,b) during first pass reading (i.e., the sum of all first fixations on a word in reading direction excluding any revisits or skipped words). In line, readers with dyslexia fixate on more words in a given trial when all fixations are examined (g = − 1.312, 95% CIg = [− 1.488, − 1.135]; Ratio Visited Words in Fig. 5a), and stop more frequently per word on average (g = − 1.27, 95% CIg = [− 1.445, − 1.095]; Number Fixations per Word in Fig. 5a). Further, revisits of earlier parts of a text (i.e., leftward saccades to a preceding word formally called regressions) are a substantial and frequent part of natural reading. We observe that readers with dyslexia express more regressions per text (g = − 0.82, 95% CIg = [− 0.989, − 0.656]; Figs. 3f and 5a,b). However, given the increase in the number of saccades as a result of longer reading durations, this increase in the number of regressions yields no significant difference in the probability of making a regression across an entire text (g = − 0.05, 95% CIg = [− 0.213, 0.107]; Fig. 5a,b).

Furthermore, since the control group shows coding processing speed above the population average, we examined this group’s link between fast coding speed and the traditional eye movement metrics reported above. This analysis shows no correlation between coding speed and any of the reported eye movement metrics (absolute range r30 = 0.016–0.32, all ps > 0.05), which suggests that faster coding speed does not systematically affect the eye movements of readers without dyslexia. We also observe no correlation for the dyslexia group (absolute range r33 = 0.019–0.14, all ps > 0.05).

Taken together, our results on traditional eye movement metrics corroborate previous findings from investigations with readers affected by dyslexia. They demonstrate that these readers examine a given text more slowly and in smaller steps, even without accounting for any revisits of previous words (Fig. 5b). Since efficient reading was found to be characterized by skipping over many words (up to 90%3) during the first rightward scanning of a text in reading direction (termed, first-pass reading), the observed pattern strongly suggests that inefficiencies are introduced by processing less content simultaneously as well as slower information uptake and longer cognitive processing times of text. Crucially, these findings are based on data obtained from natural reading of standardized texts consisting of multiple lines.

### Further contemporary metrics of ocular movements during reading

Recently, additional metrics have been proposed to differentiate between oculomotor deficiencies and cognitive, linguistic factors underlying longer fixation times84. Line-initial fixations are one such metric. They constitute the first fixation on one of the first words of a line that is not followed by a leftwards correction within the same line. Uniquely, line-initial fixations do not allow the reader early access to a word’s coarse visual orthographic percept due to absent parafoveal preview. Hence, they have been proposed as an unconfounded indicator of linguistic processing time84,85. By contrasting groups on the duration of line-initial fixations, we find these to be longer in the dyslexia group on a single-fixation (g = − 0.33, 95% CIg = [− 0.383, − 0.276]; MedianDyslexia = 231 ms, MedianControl = 202 ms; Fig. 4a), and single-trial level (g = − 0.81, 95% CIg = [− 0.972, − 0.64]; MedianDyslexia = 256 ms, MedianControl = 216 ms; 89% of readers with dyslexia show this effect; Figs. 4b and 5b). Parker and colleagues84 reported an effect in the same direction when comparing accurate line-initial fixation durations between children and adults without dyslexia, with children showing longer fixation durations. This finding adds to the evidence indicating that readers with dyslexia take longer to process the visual and linguistic information sampled during a fixation. It further supports the notion that the visual sampling strategy of readers with dyslexia resembles the strategy of early readers without dyslexia.

To ensure that we selected line-initial fixations accurately, we compared their duration to the overall median fixation duration of a trial. Previous research shows that fixation duration decreases as readers move their eyes towards the end of a line84,86. As expected, the identified line-initial fixations are of longer duration than all fixations considered together (gDyslexia = 0.87, 95% CIg = [0.673, 1.071]; line-initialDyslexia = 256 ms; AllDyslexia = 224 ms; gControl = 0.57, 95% CIg = [0.361, 0.768]; line-initialControl = 216 ms, AllControl = 202 ms). We find this increase in line-initial fixation duration to be larger in the dyslexia group (gGroups = − 0.37, 95% CIg = [− 0.526, − 0.204]; 32 vs 14 ms). The same pattern of eye movement results reported above holds true when analyzing only trials whose attention questions were answered correctly.

### Specific divergence from a regular visual sampling strategy

Besides the presented group differences on global and local eye movement metrics, we noticed a clear divergence from a regular left-to-right visual sampling strategy among readers with dyslexia. To quantify these divergences of eye movements that we consider atypical for reading, we examined saccades with angles that would not be expected during the natural reading flow (henceforth, directional deviations).

Readers with dyslexia express directional deviations more than twice as often per trial on average (g = − 0.48, 95% CIg = [0.316, 0.641]; MeanDyslexia = 1.003, MeanControl = 0.441, varDyslexia = 2.08, varControl = 0.61; 69% of readers with dyslexia show more directional deviations per trial than the control group’s average; Figs. 4c and 5b), which signals a more frequent loss of place at unexpected points during the reading process. Remarkably, in the dyslexia group most of the identified directional deviations were directed straight downwards, whereas this pattern was virtually reversed in readers without dyslexia (Fig. 4c). Since even a brief scanning of the area of text just below the current fixation seems rather unintuitive from a cognitive perspective, this finding raises the question whether these directional deviations are the result of occasional issues with oculomotor control previously reported in dyslexia29,31,32,33,87,88,89,90,91,92,93.

The aforementioned differences in eye movements are part of the overall visual sampling strategy of text during reading, termed a scanpath. To investigate whether readers with and without dyslexia differ only on some eye movement metrics or rather use a divergent overall visual sampling strategy, we complemented the previous analyses with a computational similarity analysis of the overall scanpath of each trial. To this end, we quantified the temporal and spatial similarity of the fixations of all scanpaths employing a version of the Scasim analysis94. The aim of this trial-based analysis was to identify clusters of trials with similar scanpath patterns, while achieving independence of the observed group differences in reading time. To identify whether trials of readers with dyslexia were more (dis)similar to those of other readers with dyslexia, we compared the number of trials associated with each group within a given cluster. Similarity scores and clusters were computed separately for each text of the IReST battery and font type, as this coordinate-based analysis is highly sensitive to differences in spacing such as those introduced by text displayed in differently spaced font types (Fig. 1b,c). In this study, trials were equally split between Times New Roman and OpenDyslexic font types. Additionally, all trials were normalized by their reading duration to avoid the introduction of trivial differences between scanpaths of different lengths.

Pairwise scanpath similarity scores showed that trials of participants with dyslexia differed from those of participants without, even though participants read identical texts (Fig. 4d–f). Upon normalizing, trial-by-trial similarity scores indicated that dyslexic participants spend a substantial amount of the time (between ~ 34 and 83%) looking at different places on the identical paragraph and/or for different durations compared to their non-dyslexic counterparts (Fig. 4e). These similarity scores were subsequently transferred to the spatial domain (for details, see “Methods”), where we find that the optimal number of group-independent clusters ranges between two and five clusters per text-font pair. Trials of each group were predominantly allocated to separate clusters, and demonstrate a significant difference of association for about 75% of text-font pairs (p < 0.05; for detailed statistics, see Table S3 in the supplementary material). Thus, we find that readers with dyslexia sample identical texts using a different sequence of fixations (i.e., fixating different locations on the text and/or for different durations) than non-dyslexics—even when differences in reading time are accounted for.

To summarize, our findings demonstrate that readers with dyslexia use a generally more laborious and inefficient visual sampling strategy during natural reading. The virtually opposite pattern of directional deviations between groups points towards the existence of occasional deficiencies in oculomotor control that result in dyslexic readers losing their place more often. Replicating previous findings, their laborious strategy is characterized by longer average and line-initial fixation duration, prolonged first run dwell time as well as shorter saccade amplitude and fewer skipped words. Contrarily, the probability of revisiting preceding words was comparable between groups. This pattern of eye movements suggests that prolonged time for cognitive, linguistic processes such as word decoding, lexical access, and/or phonological decoding underlies the behavioral difficulties associated with dyslexia such as substantially slower reading speed; but not an increased need for resolving semantic or syntactic ambiguities through reanalysis of prior text. Altogether, these results indicate that an interplay of linguistic and oculomotor factors underlies the reading struggles in adults with dyslexia.

## Discussion

In this study, we used eye-tracking to devise a comprehensive eye movement profile of the visual sampling strategy of adult readers with dyslexia during naturalistic reading of standardized multi-sentence texts in English (IReST63). Here, combining traditional and contemporary eye movement metrics, we show fundamental differences between readers with and without dyslexia on all but one of the examined metrics. These results, in combination with substantial decreases in reading speed, illustrate a laborious and more effortful reading strategy in adulthood, resembling a pattern observed in beginning2 and poorer readers56.

The idea that eye movements differ between readers with and without dyslexia is not new. Rayner1,48 was among the first to report different eye movements during reading based on anecdotal case studies with only three dyslexics. His investigations were followed by numerous cross-sectional studies using separate samples of readers with dyslexia, and largely varying stimuli in languages with different orthographic depth (for reviews, see2,95). This variety of stimuli, typically consisting of hand-picked single words or short sentences that impose artificial task demands on the reader rather than allowing for an ecologically valid natural reading scenario, constitutes an issue in the field96. The use of standardized and validated multi-sentence texts remains scarce in the literature.

In this work, we were particularly interested in reconciling many previously separate accounts of differential eye movements using the same sample of individuals with dyslexia while also examining specific indicators of oculomotor deficiencies during natural reading. Our results replicate previous findings of differential eye movements in children and adults with dyslexia such as longer fixation durations, fewer skipped words during first-pass reading and repeated fixations on the same word1,3,43,44,45,46,47,48,49,50,97. Oculomotor control has commonly been investigated using a variety of non-linguistic saccade tracking and fixation stability tasks29,33,87,88,90,91,93. Here, we show that specific saccades atypical for reading can be detected during natural reading. A twofold likelihood of expressing such a saccade, termed directional deviation, indicates signs of occasional oculomotor deficiencies in dyslexia—in line with previous reports29,31,32,33,87,88,89,90,91,92,93.

Given that the eye movement profile of children with dyslexia during paragraph reading has previously been exploited for dyslexia screening43,44, it is worth asking whether the inclusion of metrics on separate levels of granularity (i.e., the local single-word and global paragraph level) in adults improve our understanding of eye movements in dyslexia? Our approach differs crucially from these two screening studies on several points. Firstly, both studies were conducted with children around the age of 10 using recordings obtained from reading only one non-validated text with short lines. Secondly, these studies aimed to identify the most parsimonious model that classified recordings accurately as stemming from a child with or without dyslexia. This focus on reducing complexity in the data precluded devising a comprehensive profile, and may have resulted in overlooking smaller but informative differences such as directional deviations. Thirdly, this model-focused approach did not allow for addressing specific hypothesis-driven questions using targeted measures such as line-initial fixations. Hence, these child studies and our adult study complement each other by establishing an eye movement profile of dyslexia at different ages that consists of a diverse set of metrics.

In the case of individuals with dyslexia who present with longer fixation durations, as seen in the current study, this model posits that these individuals experience slower lexical access, associated with increased lexical processing demands. Where skilled readers require less time to perform the usually fast familiarity check (i.e., finding a match for the letter string making up a word), individuals with dyslexia do not seem to be able to carry out this process equally fast. The dyslexia group also expressed shorter saccades and skipped fewer words during first-pass reading—in line with previous findings1,3,48. These findings, in combination with a higher frequency of fixating on the same word repeatedly, corroborate the notion of needing to process each word or even its sub-components individually, and for longer, when reading for comprehension. Prolonged line-initial fixations of individuals with dyslexia in our study provide more evidence for delays in lexical access (stages two and three). These first fixations on a line do not benefit from any parafoveal preview benefit (stage one) resulting in the sole reliance of information sampled during this fixation for word identification purposes.

While a deficit in lexical processing can explain longer fixation durations for individuals with dyslexia, a deficit in parafoveal processing could likewise explain increased fixation durations, shorter saccade amplitudes and fewer skipped words. The preview benefit takes advantage of orthographic information from parafoveal vision such as word length and word familiarity. It is linked with a reader’s perceptual span, which is defined as the number of distinct characters from which useful information can be acquired in parallel across the fovea and parafovea52,103,104. Should individuals with dyslexia present with such a deficit, removing or having a reduction in this preview benefit could also result in the need for longer processing (i.e., fixation durations) when the next word is being fixated on simply due to reduced pre-processing of its orthographic percept105. Such a smaller perceptual span has previously been associated with reading speed106,107, and reported in dyslexia2,108. This deficit can occur independently of a phonological deficit109, however, previous evidence is inconclusive110,111. In the present study, line-initial fixation duration can serve as an indicator of parafoveal processing when compared to all other fixation durations across a sentence. The former are usually longer due to absent parafoveal preview while the duration of all other fixations decreases towards the end of a line84,85,86. These line-initial fixations were longer for all readers compared to the median fixation duration across a trial. This difference was larger for readers with dyslexia, which suggests that these readers need even more processing time when no preview benefit is available. In other words, readers with dyslexia do not appear to be disadvantaged when the preview benefit for words to the right of a fixation is available. This result demonstrates that a reduction in the perceptual span is unlikely the explanation of the general increases in fixation duration, as seen in this study’s sample with dyslexia.

One conceivable concern of the present study’s design is that the observed eye movement profile could be simply a result of texts that were too difficult for readers with dyslexia, since increases in text difficulty were reported to lead to more eye movements in dyslexic children58, and a pattern similar to the one observed in this study in readers with1 and without dyslexia2. However, given that the IReST are designed at a grade six reading level and all of the participants in the present sample had previously attended or were attending higher education at the time of participation, unsuitable text difficulty is very unlikely to be the reason for the observed differences.

A second concern is that we did not observe a ceiling effect in the responses to our multiple-choice questions—with two participants performing just above chance and 31.3% of all participants answering all questions correctly. Although there are multiple reasons as to why not all participants answered all questions correctly, one is the very specific nature of some questions, which makes those somewhat more challenging for some readers. Importantly, we found no evidence for a difference between the two groups. Such a difference could have raised concerns about it exerting effects on the eye movement contrasts in this study.

In summary, the presented eye movement profile of adults with dyslexia demonstrates a laborious and effortful visual sampling strategy when reading multiline paragraphs of text. Specifically, the combination of prolonged fixation duration, shorter saccade amplitude and fewer skipped words suggests deficits in the linguistic processing components of reading such as fast and efficient access to the mental lexicon. Longer line-initial fixation durations were particularly indicative of prolonged lexical analysis. On the contrary, we did not find convincing evidence for a perceptual span deficit or increased difficulties in the semantic or syntactic post-lexical processing stage of reading. An increased number of eye movements atypical for reading shows that the eyes of readers with dyslexia occasionally move to seemingly random places on a page. Hence, occasional oculomotor deficiencies should not be categorically dismissed in dyslexia.

## Methods

### Participants

We tested 73 participants: 35 adults with an official diagnosis of dyslexia, and 38 without symptoms of dyslexia. Six individuals from the control group were excluded from all analyses due to large inaccuracies during the calibration procedure (best eye with average error > 0.5° and max error > 1.3°). Hence, the final data analysis was conducted on 67 participants: 35 with dyslexia (female = 23, Meanage = 23.54, SDage = 6.22) and 32 without (female = 32, Meanage = 22.38, SDage = 2.7).

To delineate between control participants who experience dyslexia symptoms but have not been given an official diagnosis, and to get a measure of severity of dyslexia symptoms at the time of participation, all participants completed the Adult Dyslexia Checklist113. This checklist assesses aspects of literacy, language, word finding, and organization skills on a scale of 1–4 (i.e., rarely / occasionally / often / most of the time). As specified by the original authors, a score of 45 or more points indicates mild to severe dyslexia symptoms113. We used a score of ≤ 40 points as a conservative cut-off for our control group, with all formally diagnosed dyslexics allocated to the dyslexia group.

Participants were matched on age and level of education. They were either current or former college or university students at anglophone institutions in Canada. Since participants were recruited in Montréal, a bilingual English-French city, our sample comprises both bilingual and monolingual English speakers (bilingualDyslexia = 18, monolingualDyslexia = 17; bilingualControl = 9, monolingualControl = 23). To avoid introducing a language effect, we compared bilingual to monolingual participants and found no evidence for a difference in reading duration in the dyslexia group (t(33) = 1.829, p = 0.0765; g = 0.6, 95% CIDyslexia = [− 0.064, 1.264]; BF10 = 1.16) nor the control group (t(30) = − 0.5, p = 0.6212; g = − 0.19, 95% CIControl = [− 0.94, 0.56]; BF10 = 0.4). We neither observed a language effect regarding attention to the text in the dyslexia group (t(33) = 1.375, p = 0.1784; g = 0.45, 95% CIDyslexia = [− 0.206, 1.108]; BF10 = 0.67), and insufficient evidence in the control group (t(30) = − 2.241, p = 0.0326; g = − 0.86, 95% CIControl = [− 1.634, − 0.071]; BF10 = 2.16). Based on these findings, groups were collapsed across language for all analyses. Written informed consent was obtained from all participants. Participants could choose between receiving \$10 or course credit as compensation. This study adhered to the Canadian Tri-council Policy on ethical conduct for research involving humans114, and obtained approval by the Concordia University Human Ethics Research Committee (certificate: 30003975).

### Stimuli

The Wechsler Adult Scale of Intelligence’s Symbol Search and Coding subtests were administered to assess processing speed abilities of all participants (WAIS-IV74). Importantly, both subtests use non-linguistic stimuli. In the Symbol Search task, participants are shown two target symbols and are instructed to identify both of the target symbols within the adjacent search group. This task involves no working memory as the symbols change for each trial (i.e., by horizontal search group of five symbols). Contrarily, the Coding task may involve aspects of working memory77. Participants are shown numbers 1–9 and their unique corresponding symbol at the top of the page. Here, the task is to draw the corresponding symbol associated with each number below a sequence of numbers. The WAIS has an internal consistency score of 0.87–0.98 on processing speed index tasks. The interscorer agreement ranges from 0.98 to 0.99, and intraclass correlation from 0.91 to 0.97116. Correlations between scores on tests that measure similar constructs were in the 0.8 range on criterion-related validity measures117.

### Apparatus

Stimuli were presented and data collected using an iMac (2011 27″ i7, 16 GB RAM) with an external monitor (View Sonic G225fb 21″ CRT, 1024 X 768 pixel resolution, 100 Hz refresh rate). A chin rest was used to stabilize head position at a distance of 70 cm from the screen. Eye position was acquired non-invasively using a video-based eye movement monitor (EyeLink 1000 running host software version 4.56, SR Research, Ottawa, Ontario).

### Eye tracking analysis

Eye movement data were recorded at a sampling rate of 1000 Hz and stored for offline analysis. DataViewer’s inbuilt algorithms (version 4.1.1, SR Research, 2019, Ottawa, Ontario) were used for the pre-processing of fixations, saccades, and blinks, forming reading-related interest areas and trial-based aggregate measures. An interest period that excluded the first and last 300 ms of each trial was defined in DataViewer to avoid contaminating this analysis with reading unrelated events at the very beginning and end of each trial. The duration of fixations that spanned any of these two cut-off time points was trimmed. Further, the minimum saccade amplitude was set to 0.5°, the fixation merging amplitude to 1°, and the minimum fixation duration to 50 ms. Fixations separated by a blink were not merged. Instead we removed fixations immediately before and after a blink. Fixations beyond display bounds (i.e., the entire screen) were excluded. In general, we analyzed only data of one eye per participant and excluded all samples that were identified by any of the aforementioned criteria from all further analyses. These analysis parameters help to remove outliers caused by random eye movements that are unrelated to reading.

The interest area analysis was word based in that one interest area was associated with each word including five pixels of padding around all sides of a word. A background RGB threshold of less than 350 was chosen to fill gaps between interest areas. Although all fixations were drift corrected by the drift value obtained at the start of each trial, we manually adjusted all fixations of a trial vertically (13.9% of all analyzed trials) if visual inspection showed that fixations exhibited an obvious vertical offset across all lines of a text resulting in them lying on interest area boundaries. Importantly, we neither moved single fixations separately nor adjusted fixations horizontally.

Results of the offline analysis with DataViewer (version 4.1.1, SR Research, 2019, Ottawa, Ontario) were exported for use with custom scripts in MATLAB (version 2020a, The MathWorks Inc., 2020, Natick, Massachusetts). There, we calculated all measures split by experimental conditions (i.e., group and font). We excluded all trials presenting text number five in either font due to a stimulus presentation issue and to avoid any bias (10% of all trials). A further two trials had to be excluded due to recording issues. To quantify and compare the effect of group (i.e., Dyslexia vs Control) in detail, we computed unbiased signed between-group effect sizes (g) and their respective 95% confidence intervals separately for each eye movement metric (using the mes function of the Measures of Effect Size Toolbox83 and its exact analytical method for determining confidence intervals). Following frequentist logic, a significant effect of group was presumed when the 95% confidence interval of an effect size did not include zero. In our design, positive effect sizes represent a higher number or ratio of the respective eye-tracking metric for the control group compared to the dyslexia group and vice versa (Fig. 5a). As well, we estimated the probability density function corresponding to selected eye movement metrics whose between-group comparison yielded a significant effect size employing kernel density estimation in MATLAB (using the raincloud_plot function118). In doing so, we created a probability density heat map for all selected eye movement metrics.

In addition to traditional reading eye movement metrics, we also examined line-initial fixations. These fixations have been proposed to be able to dissociate binocular coordination from linguistic analysis of text/words85. We identified the first fixation on any of the first two words of a line that was not followed by an undersweep corrective saccade to the left of this fixation as accurate line-initial fixation. Group differences within these fixations were then quantified using Hedges’ g.

As an overall measure of scanpath similarity, we computed the Scasim metric94. This trial-based metric compares the location (x-, y-coordinates) and duration of all fixations that make up a scanpath and can be computed using the scasim function provided by the first author’s GitHub repository119. The resulting score represents a value of dissimilarity. We normalized all scores by their respective reading duration to avoid confounds due to large differences in reading duration. Scasim scores are computed on a pairwise trial-by-trial basis. Since this measure uses the x-,y-coordinates of a fixation’s location, we computed Scasim scores per IReST and font a text was displayed in. This was necessary as differences in font led to words of the same text being displayed in slightly different locations. Trials were analyzed across groups to begin with. However, to quantify scanpath similarity between readers with and without dyslexia, spatial maps of scanpaths were fit on similarity scores using Euclidean distances (dist function) and non-metric multidimensional scaling (isoMDS function from the MASS package120). Subsequently, we used the optimal number of clusters, a result of a calculation of Gaussian mixture models paired with the Bayesian Information Criterion121 (mclustBIC function of the mclust package122), as the number of clusters in a k-means clustering procedure (kmeans function). Since the determined clusters were still comprised of group-independent trials, we employed chi-square and Fisher exact tests of association analyzing whether trials in each cluster belonged to the same or a different experimental group. The entire Scasim analysis was conducted in RStudio123.

### Statistical analysis

To investigate potential differences in reading duration as a function of dyslexia (i.e., group factor) and font, we used a generalized linear mixed-effects model (GLMM). Eye movement data was primarily analyzed using unbiased effect sizes (i.e., Hedges’ g; denoted as g in the text) and their exact analytical 95% confidence interval83. For selected eye movement metrics, we also computed the proportion of participants in the dyslexia group whose trial-based average showed performance in line with their group when compared to the average of all participants in the control group (i.e., above or below the control group’s mean).

The GLMM analysis was performed by means of the lme4 package124 and the bobyca optimizer in RStudio123. Reading duration was specified as a continuous dependent variable, and examined, using a gamma model in the family argument of the glmer function, as a function of the two categorical predictors: group (i.e., Dyslexia and Control), and font (i.e., Times New Roman and OpenDyslexic), and their interaction on a single-trial level. The GLMM included the maximal random effects structure justified by the experimental design125. They included all main effects and interactions of our two predictors, group and font, as well as by-subject and by-item random intercepts and random slopes for all relevant main effects. We excluded random correlations for this model. The 95% confidence intervals were calculated for all $$\upbeta$$ estimates (using the broom package and Wald method in RStudio123). We accounted for small imbalances in trial numbers of the predictors’ levels by entering all predictors in mean-centred form (deviation coding). All entered predictors were checked for collinearity (using the cor function and model output). Lastly, we used post-hoc likelihood-ratio (X2) model comparisons to quantify the predictive power and exact significance level of all initially significant or trending effects (i.e., p < 0.1) revealed by the GLMM.

To examine potential effects of non-linguistic visual cognitive processing speed on reading duration (i.e., the WAIS subscale scores), particularly in light of a difference in reading duration between experimental groups, we correlated the standardized scores for both processing speed measures with reading duration across participants. This and all other correlations were computed employing robust bend correlations and the default of 20% bending in each direction126.

To investigate each groups’ attention to the reading material separately, we first compared their results on our multiple-choice attention questions against chance (i.e., 50%) using two separate two-sided one-sample t-tests as well as a Bayes Factor analysis. We also contrasted the two experimental groups against each other by means of a two-sided independent samples t-test, under the assumption that both groups would pay equal attention to the reading material. In determining significance, all t-tests were Bonferroni-corrected by the number of t-tests evaluating this dependent variable (n = 3). The Bayes Factor analysis allowed us to quantify the strength of the evidence in support of the null hypothesis of no difference when compared against 0 or between groups. Due to a lack of previous research using Bayes Factors in this area, we used an uninformed prior for the Bayesian analyses with a Cauchy width of 0.7.

## Data availability

Data supporting this work is available from the project’s Open Science Framework repository [https://osf.io/3r8gx/].

## Code availability

Code to reproduce selected figures and analyses is available from the project’s Open Science Framework repository [https://osf.io/3r8gx/].

## References

1. Rayner, K. The role of eye movements in learning to read and reading disability. Remedial Spec. Educ. 6, 53–60 (1985).

2. Rayner, K. Eye movements in reading and information processing: 20 years of research. Psychol. Bull. 124, 372–422 (1998).

3. Hawelka, S., Gagl, B. & Wimmer, H. A dual-route perspective on eye movements of dyslexic readers. Cognition 115, 367–379 (2010).

4. Rayner, K. & Clifton, C. Language processing in reading and speech perception is fast and incremental: Implications for event-related potential research. Biol. Psychol. 80, 4–9 (2009).

5. Sereno, S. C. & Rayner, K. Measuring word recognition in reading: Eye movements and event-related potentials. Trends Cogn. Sci. 7, 489–493 (2003).

6. Snell, J., van Leipsig, S., Grainger, J. & Meeter, M. OB1-reader: A model of word recognition and eye movements in text reading. Psychol. Rev. 125, 969–984 (2018).

7. Snell, J. & Grainger, J. Readers are parallel processors. Trends Cogn. Sci. 23, 537–546 (2019).

8. Dien, J. The neurocognitive basis of reading single words as seen through early latency ERPs: A model of converging pathways. Biol. Psychol. 80, 10–22 (2009).

9. Hauk, O., Davis, M. H., Ford, M., Pulvermüller, F. & Marslen-Wilson, W. D. The time course of visual word recognition as revealed by linear regression analysis of ERP data. Neuroimage 30, 1383–1400 (2006).

10. Lyon, G. R., Shaywitz, S. E. & Shaywitz, B. A. A definition of dyslexia. Ann Dyslexia 53, 1–14 (2003).

11. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. (American Psychiatric Association, 2013).

12. Shaywitz, S. E. et al. Neural systems for compensation and persistence: Young adult outcome of childhood reading disability. Biol. Psychiatry 54, 25–33 (2003).

13. Swanson, H. L. & Hsieh, C.-J.C.-J. Reading disabilities in adults: A selective meta-analysis of the literature. Rev. Educ. Res. 79, 1362–1390 (2009).

14. Vellutino, F. R., Fletcher, J. M., Snowling, M. J. & Scanlon, D. M. Specific reading disability (dyslexia): What have we learned in the past four decades?. J. Child Psychol. Psychiatry 45, 2–40 (2004).

15. Shaywitz, S. E. Dyslexia. N. Engl. J. Med. 338, 307–312 (1998).

16. Wagner, R. K. et al. The prevalence of dyslexia: A new approach to its estimation. J. Learn. Disabil. 53, 354–365 (2020).

17. Lobier, M., Zoubrinetzky, R. & Valdois, S. The visual attention span deficit in dyslexia is visual and not verbal. Cortex 48, 768–773 (2012).

18. Stein, J. F. The magnocellular theory of developmental dyslexia. Dyslexia 7, 12–36 (2001).

19. Stein, J. F. The current status of the magnocellular theory of developmental dyslexia. Neuropsychologia 7, 12–36 (2018).

20. Stein, J. F. & Walsh, V. To see but not to read: The magnocellular theory of dyslexia. Trends Neurosci. 20, 147–152 (1997).

21. Stoodley, C. J. & Stein, J. F. The cerebellum and dyslexia. Cortex 47, 101–116 (2011).

22. Huettig, F., Lachmann, T., Reis, A. & Petersson, K. M. Distinguishing cause from effect–many deficits associated with developmental dyslexia may be a consequence of reduced and suboptimal reading experience. Lang. Cogn. Neurosci. 33, 333–350 (2018).

23. Goswami, U. Sensory theories of developmental dyslexia: Three challenges for research. Nat. Rev. Neurosci. 16, 43–54 (2015).

24. Ramus, F. Developmental dyslexia: Specific phonological deficit or general sensorimotor dysfunction?. Curr. Opin. Neurobiol. 13, 212–218 (2003).

25. Ramus, F. Neuroimaging sheds new light on the phonological deficit in dyslexia. Trends Cogn. Sci. 18, 274–275 (2014).

26. Saksida, A. et al. Phonological skills, visual attention span, and visual stress in developmental dyslexia. Dev. Psychol. 52, 1503–1516 (2016).

27. Snowling, M. J. The development of grapheme-phoneme correspondence in normal and dyslexic readers. J. Exp. Child Psychol. 29, 294–305 (1980).

28. Snowling, M. J. Phonemic deficits in developmental dyslexia. Psychol. Res. 43, 219–234 (1981).

29. Raghuram, A., Gowrisankaran, S. & Swanson, E. Frequency of visual deficits in children with developmental dyslexia. JAMA Ophthalmol. 136, 1089–1095 (2018).

30. Raghuram, A., Hunter, D. G., Gowrisankaran, S. & Waber, D. P. Self-reported visual symptoms in children with developmental dyslexia. Vis. Res. 155, 11–16 (2019).

31. Pavlidis, G. T. Do eye movements hold the key to dyslexia?. Neuropsychologia 19, 57–64 (1981).

32. Fischer, B. & Hartnegg, K. Stability of gaze control in dyslexia. Strabismus 8, 119–122 (2000).

33. Biscaldi, M., Fischer, B. & Hartnegg, K. Voluntary saccadic control in dyslexia. Perception 29, 509–521 (2000).

34. Blythe, H. I., Kirkby, J. A. & Liversedge, S. P. Comments on: “What is developmental dyslexia?” brain Sci 2018, 8, 26. The relationship between eye movements and reading difficulties. Brain Sci. 8, 100 (2018).

35. O’Brien, B. A., Mansfield, J. S. & Legge, G. E. The effect of print size on reading speed in dyslexia. J. Res. Read. 28, 332–349 (2005).

36. Marinus, E. et al. A special font for people with dyslexia: Does it work and if so, why?. Dyslexia 22, 233–244 (2016).

37. O’Brien, B. A., Mansfield, J. S. & Legge, G. E. The effect of contrast on reading speed in dyslexia. Vis. Res. 40, 1921–1935 (2000).

38. Re, A. M., Tressoldi, P. E., Cornoldi, C. & Lucangeli, D. Which tasks best discriminate between dyslexic university students and controls in a transparent language?. Dyslexia 17, 227–241 (2011).

39. Callens, M., Tops, W. & Brysbaert, M. Cognitive profile of students who enter higher education with an indication of dyslexia. PLoS ONE 7, e38081 (2012).

40. Suarez-Coalla, P. & Cuetos, F. Reading difficulties in Spanish adults with dyslexia. Educ. Technol. Res. Dev. 65, 33–51 (2015).

41. Adler-Grinberg, D. & Stark, L. Eye movements, scanpaths, and dyslexia. Am. J. Optom. Physiol. Opt. 55, 557–570 (1978).

42. Joo, S. J., White, A. L., Strodtman, D. J. & Yeatman, J. D. Optimizing text for an individual’s visual system: The contribution of visual crowding to reading difficulties. Cortex 103, 291–301 (2018).

43. Nilsson Benfatto, M. et al. Screening for dyslexia using eye tracking during reading. PLoS ONE 11, e0165508 (2016).

44. Smyrnakis, I. et al. RADAR: A novel fast-screening method for reading difficulties with special focus on dyslexia. PLoS ONE 12, e0182597 (2017).

45. Razuk, M., Barela, J. A., Peyre, H., Gerard, C. L. & Bucci, M. P. Eye movements and postural control in dyslexic children performing different visual tasks. PLoS ONE 13, e0198001 (2018).

46. De Luca, M., Di Pace, E., Judica, A., Spinelli, D. & Zoccolotti, P. Eye movement patterns in linguistic and non-linguistic tasks in developmental surface dyslexia. Neuropsychologia 37, 1407–1420 (1999).

47. Hutzler, F. & Wimmer, H. Eye movements of dyslexic children when reading in a regular orthography. Brain Lang. 89, 235–242 (2004).

48. Rayner, K. Eye movements, perceptual span, and reading disability. Ann. Dyslexia 33, 163–173 (1983).

49. Bucci, M. P., Brémond-Gignac, D. & Kapoula, Z. Poor binocular coordination of saccades in dyslexic children. Graefe’s Arch. Clin. Exp. Ophthalmol. 246, 417–428 (2008).

50. Jainta, S. & Kapoula, Z. Dyslexic children are confronted with unstable binocular fixation while reading. PLoS ONE 6, e18694 (2011).

51. Hawelka, S., Huber, C. & Wimmer, H. Impaired visual processing of letter and digit strings in adult dyslexic readers. Vis. Res. 46, 718–723 (2006).

52. Rayner, K., Slattery, T. J. & Bélanger, N. N. Eye movements, the perceptual span, and reading speed. Psychon. Bull. Rev. 17, 834–839 (2010).

53. Slattery, T. J. & Yates, M. Word skipping: Effects of word length, predictability, spelling and reading skill. Q. J. Exp. Psychol. 71, 250–259 (2018).

54. Drieghe, D., Veldre, A., Fitzsimmons, G., Ashby, J. & Andrews, S. The influence of number of syllables on word skipping during reading revisited. Psychon. Bull. Rev. 26, 616–621 (2019).

55. Degno, F. et al. Parafoveal previews and lexical frequency in natural reading: Evidence from eye movements and fixation-related potentials. J. Exp. Psychol. Gen. 148, 453–474 (2019).

56. Ashby, J., Rayner, K. & Clifton, C. Eye movements of highly skilled and average readers: Differential effects of frequency and predictability. Q. J. Exp. Psychol. Sect. A 58, 1065–1086 (2005).

57. Engelmann, F., Vasishth, S., Engbert, R. & Kliegl, R. A framework for modeling the interaction of syntactic processing and eye movement control. Top. Cogn. Sci. 5, 452–474 (2013).

58. Trauzettel-Klosinski, S. et al. Eye movements in German-speaking children with and without dyslexia when reading aloud. Acta Ophthalmol. 88, 681–691 (2010).

59. Rayner, K. & Duffy, S. A. Lexical complexity and fixation times in reading: Effects of word frequency, verb complexity, and lexical ambiguity. Mem. Cognit. 14, 191–201 (1986).

60. von der Malsburg, T. & Vasishth, S. Scanpaths reveal syntactic underspecification and reanalysis strategies. Lang. Cogn. Process. 28, 1545–1578 (2013).

61. von der Malsburg, T., Kliegl, R. & Vasishth, S. Determinants of scanpath regularity in reading. Cogn. Sci. 39, 1675–1703 (2015).

62. Reichle, E. D., Pollatsek, A., Fisher, D. L. & Rayner, K. Toward a model of eye movement control in reading. Psychol. Rev. 105, 125–157 (1998).

63. Trauzettel-Klosinski, S. & Dietz, K. Standardized assessment of reading performance: The new international reading speed texts IReST. Investig. Ophthalmol. Vis. Sci. 53, 5452–5461 (2012).

64. Morrice, E., Hughes, J., Stark, Z., Wittich, W. & Johnson, A. Validation of the international reading speed texts in a Canadian sample. Optom. Vis. Sci. 97, 509–517 (2020).

65. Zorzi, M. et al. Extra-large letter spacing improves reading in dyslexia. Proc. Natl. Acad. Sci. USA 109, 11455–11459 (2012).

66. Dotan, S. & Katzir, T. Mind the gap: Increased inter-letter spacing as a means of improving reading performance. J. Exp. Child Psychol. 174, 13–28 (2018).

67. Hakvoort, B., van den Boer, M., Leenaars, T., Bos, P. & Tijms, J. Improvements in reading accuracy as a result of increased interletter spacing are not specific to children with dyslexia. J. Exp. Child Psychol. 164, 101–116 (2017).

68. Sjoblom, A. M., Eaton, E. & Stagg, S. D. The effects of letter spacing and coloured overlays on reading speed and accuracy in adult dyslexia. Br. J. Educ. Psychol. 86, 630–639 (2016).

69. Gonzalez, A. OpenDyslexic. https://opendyslexic.org (2011).

70. Boer, C. T. Dyslexie Font. https://www.dyslexiefont.com/en/typeface/ (2008).

71. Kuster, S. M., van Weerdenburg, M., Gompel, M. & Bosman, A. M. T. Dyslexie font does not benefit reading in children with or without dyslexia. Ann. Dyslexia 68, 25–42 (2018).

72. Wery, J. J. & Diliberto, J. A. The effect of a specialized dyslexia font, OpenDyslexic, on reading rate and accuracy. Ann. Dyslexia 67, 114–127 (2017).

73. Spinelli, D., De Luca, M., Judica, A. & Zoccolotti, P. Crowding effects on word identification in developmental dyslexia. Cortex 38, 179–200 (2002).

74. Wechsler, D. Wechsler Abbreviated Scale of Intelligence: WASI-IV. (NCS Pearson Inc., 2008).

75. Breznitz, Z. & Misra, M. Speed of processing of the visual-orthographic and auditory-phonological systems in adult dyslexics: The contribution of ‘asynchrony’ to word recognition deficits. Brain Lang. 85, 486–502 (2003).

76. Lichtenberger, E. O. & Kaufman, A. S. Essentials of WAIS-IV Assessment (Wiley, New York, 2012).

77. Joy, S., Kaplan, E. & Fein, D. Speed and memory in the WAIS-III Digit Symbol: Coding subtest across the adult lifespan. Arch. Clin. Neuropsychol. 19, 759–767 (2004).

78. De Jong, P. F. Working memory deficits of reading disabled children. J. Exp. Child Psychol. 70, 75–96 (1998).

79. Savage, R., Lavers, N. & Pillay, V. Working memory and reading difficulties: What we know and what we don’t know about the relationship. Educ. Psychol. Rev. 19, 185–221 (2007).

80. Beidas, H., Khateb, A. & Breznitz, Z. The cognitive profile of adult dyslexics and its relation to their reading abilities. Read. Writ. 26, 1487–1515 (2013).

81. Moll, K., Kunze, S., Neuhoff, N., Bruder, J. & Schulte-Körne, G. Specific learning disorder: Prevalence and gender differences. PLoS ONE 9, e103537 (2014).

82. Fostick, L. & Revah, H. Dyslexia as a multi-deficit disorder: Working memory and auditory temporal processing. Acta Psychol. (Amst) 183, 19–28 (2018).

83. Hentschke, H. & Stüttgen, M. C. Computation of measures of effect size for neuroscience data sets. Eur. J. Neurosci. 34, 1887–1894 (2011).

84. Parker, A. J., Kirkby, J. A. & Slattery, T. J. Predictability effects during reading in the absence of parafoveal preview. J. Cogn. Psychol. 29, 902–911 (2017).

85. Parker, A. J., Nikolova, M., Slattery, T. J., Liversedge, S. P. & Kirkby, J. A. Binocular coordination and return-sweep saccades among skilled adult readers. J. Vis. 19, 1–19 (2019).

86. Parker, A. J., Slattery, T. J. & Kirkby, J. A. Return-sweep saccades during reading in adults and children. Vis. Res. 155, 35–43 (2019).

87. Fukushima, J., Tanaka, S., Williams, J. D. & Fukushima, K. Voluntary control of saccadic and smooth-pursuit eye movements in children with learning disorders. Brain Dev. 27, 579–588 (2005).

88. Tiadi, A., Gérard, C. L., Peyre, H., Bui-Quoc, E. & Bucci, M. P. Immaturity of visual fixations in dyslexic children. Front. Hum. Neurosci. 10, 1–7 (2016).

89. Vagge, A., Cavanna, M., Traverso, C. E. & Lester, M. Evaluation of ocular movements in patients with dyslexia. Ann. Dyslexia 65, 24–32 (2015).

90. Freedman, E. G., Molholm, S., Gray, M. J., Belyusar, D. & Foxe, J. J. Saccade adaptation deficits in developmental dyslexia suggest disruption of cerebellar-dependent learning. J. Neurodev. Disord. 9, 1–8 (2017).

91. Fischer, B., Biscaldi, M. & Otto, P. Saccadic eye movements of dyslexic adult subjects. Neuropsychologia 31, 887–906 (1993).

92. Biscaldi, M., Gezeck, S. & Stuhr, V. Poor saccadic control correlates with dyslexia. Neuropsychologia 36, 1189–1202 (1998).

93. Fukushima, J., Hatta, T. & Fukushima, K. Development of voluntary control of saccadic eye movements I. Age-related changes in normal children. Brain Dev. 22, 173–180 (2000).

94. von der Malsburg, T. & Vasishth, S. What is the scanpath signature of syntactic reanalysis?. J. Mem. Lang. 65, 109–127 (2011).

95. Quercia, P., Feiss, L. & Michel, C. Developmental dyslexia and vision. Clin. Ophthalmol. 7, 869–881 (2013).

96. Schotter, E. R. & Payne, B. R. Eye Movements and comprehension are important to reading. Trends Cogn. Sci. 23, 811–812 (2019).

97. De Luca, M., Borrelli, M., Judica, A., Spinelli, D. & Zoccolotti, P. Reading words and pseudowords: An eye movement study of developmental dyslexia. Brain Lang. 80, 617–626 (2002).

98. Reichle, E. D., Pollatsek, A. & Rayner, K. E-Z Reader: A cognitive-control, serial-attention model of eye-movement behavior during reading. Cogn. Syst. Res. 7, 4–22 (2006).

99. Reichle, E. D., Warren, T. & McConnell, K. Using E-Z reader to model the effects of higher level language processing on eye movements during reading. Psychon. Bull. Rev. 16, 1–21 (2009).

100. Reichle, E. D. et al. Using E-Z Reader to examine the concurrent development of eye-movement control and reading skill. Dev. Rev. 33, 110–149 (2013).

101. Engbert, R., Nuthmann, A., Richter, E. M. & Kliegl, R. Swift: A dynamical model of saccade generation during reading. Psychol. Rev. 112, 777–813 (2005).

102. Mancheva, L. et al. An analysis of reading skill development using E-Z Reader. J. Cogn. Psychol. 27, 657–676 (2015).

103. McConkie, G. W. & Rayner, K. The span of the effective stimulus during a fixation in reading. Percept. Psychophys. 17, 578–586 (1975).

104. Rayner, K. & McConkie, G. W. What guides a reader’s eye movements?. Vis. Res. 16, 829–837 (1976).

105. Kliegl, R., Nuthmann, A. & Engbert, R. Tracking the mind during reading: The influence of past, present, and future words on fixation durations. J. Exp. Psychol. Gen. 135, 12–35 (2006).

106. Häikiö, T., Bertram, R., Hyönä, J. & Niemi, P. Development of the letter identity span in reading: Evidence from the eye movement moving window paradigm. J. Exp. Child Psychol. 102, 167–181 (2009).

107. Ashby, J., Yang, J., Evans, K. H. & Rayner, K. Eye movements and the perceptual span in silent and oral reading. Atten. Percept. Psychophys. 74, 634–640 (2012).

108. Rayner, K., Murphy, L. A., Henderson, J. M. & Pollatsek, A. Selective attentional dyslexia. Cogn. Neuropsychol. 6, 357–378 (1989).

109. Bosse, M. L., Tainturier, M. J. & Valdois, S. Developmental dyslexia: The visual attention span deficit hypothesis. Cognition 104, 198–230 (2007).

110. Phillips, M. H. & Edelman, J. A. The dependence of visual scanning performance on saccade, fixation, and perceptual metrics. Vis. Res. 48, 926–936 (2008).

111. Silva, S. et al. Too little or too much? Parafoveal preview benefits and parafoveal load costs in dyslexic adults. Ann. Dyslexia 66, 187–201 (2016).

112. Stein, J. F. What is developmental dyslexia?. Brain Sci. 8, 26 (2018).

114. Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada & Social Sciences and Humanities Research Council of Canada. Tri-council Policy Statement: Ethical Conduct for Research Involving Humans. (2014).

115. Morrice, E., Johnson, A. P., Marinier, J. A. & Wittich, W. Assessment of the Apple iPad as a low-vision reading aid. Eye 31, 865–871 (2017).

116. Canivez, G. L. Test review of the Wechsler Adult Intelligence Scale—Fourth Edition. In The Eighteenth Mental Measurements Yearbook (eds Spies, A. et al.) (Buros Center for Testing, Lincoln, 2010).

117. Schraw, G. Test review of the Wechsler Adult Intelligence Scale – Fourth Edition. in The eighteenth mental measurements yearbook (eds. Spies, A., Carlson, J. & Geisinger, K.) (Buros Center for Testing, 2010).

118. Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R. & Kievit, R. Raincloud plots: a multi-platform tool for robust data visualization [version 1; peer review: 2 approved]. Wellcome Open Res. 4, 63 (2019).

119. von der Malsburg, T. Scasim. https://github.com/tmalsburg/scanpath (2020).

120. Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, Berlin, 2002).

121. Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).

122. Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 205–233 (2016).

123. RStudio Team. RStudio: Integrated Development for R (RStudio, Inc., Boston, MA, 2020).

124. Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

125. Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).

126. Pernet, C. R., Wilcox, R. & Rousselet, G. A. Robust correlation analyses: False positive and power validation using a new open source matlab toolbox. Front. Psychol. 3, 606 (2013).

## Acknowledgements

This work was supported by a Concordia University Seed fund and a Concordia University Horizon Postdoctoral Fellowship awarded to L.F.

## Author information

Authors

### Contributions

Conceptualization: L.F. and A.P.J. Data Curation: L.F. and Z.S. Formal Analysis: L.F. and Z.S. Funding Acquisition: L.F. and A.P.J. Investigation: L.F., Z.S. and A.P.J. Methodology: L.F. and A.P.J. Project Administration: L.F. and A.P.J. Resources: L.F. and A.P.J. Software: L.F. and A.P.J. Supervision: A.P.J. Validation: L.F. and Z.S. Visualization: L.F. Writing—Original Draft Preparation: L.F. and Z.S. Writing—Review & Editing: L.F., Z.S. and A.P.J.

### Corresponding author

Correspondence to Léon Franzen.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

### Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Rights and permissions

Reprints and Permissions

Franzen, L., Stark, Z. & Johnson, A.P. Individuals with dyslexia use a different visual sampling strategy to read text. Sci Rep 11, 6449 (2021). https://doi.org/10.1038/s41598-021-84945-9

• Accepted:

• Published:

• DOI: https://doi.org/10.1038/s41598-021-84945-9