Age-Related Differences in Processing Unconventional Text Formats

Age-Related Differences in Processing Unconventional Text Formats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Age-Related Differences in Processing Unconventional Text Formats Natasha Alonso-Bernal, Jorge González Alonso, Pablo Gómez, Jon Andoni Duñabeitia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9093730/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Typographic formats influence reading efficiency; however, knowledge remains limited regarding how these effects change across the lifespan, especially for orthographic distortions in digital environments. This study examines how conventional formats (lowercase and uppercase) and unconventional formats (mixed-case and LEET) affect reading comprehension and reaction times. Three hundred and three adults (18–84 years) read short sentences (five words) presented in the four formats, while reading times and comprehension accuracy were recorded. The results showed a graded cost pattern: conventional formats yielded the fastest reading times, mixed-case imposed moderate costs, and LEET produced the greatest slowdown and a slight reduction in accuracy. Moreover, a significant interaction between format and age was observed: although reading slowed with age in all formats, this effect was especially pronounced for LEET. These findings suggest that extreme orthographic distortions increase perceptual and pre-lexical demands, revealing limits in reading adaptation associated with aging. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Typographic formats Reading efficiency Orthographic distortions LEET Lifespan Reading comprehension Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Studies in visual word recognition investigate how readers perceive, identify, and decode printed words, from the extraction of visual features through letter recognition to accessing meaning and pronunciation within the language system (Snowling, Hulme & Nation, 2022). This line of research aims to clarify the stepwise mechanisms underlying reading and written language comprehension, with an emphasis on how orthographic information activates linguistic representations. Among the multiple variables that modulate the reading process, recent literature has established that the physical properties of written words and their constituent letters (e.g., spacing, font width, orthographic format) influence the early stages of visual word recognition (Minakata & Liversedge, 2021; Scaltritti et al., 2019 ; Staub, 2020 ). A particularly robust demonstration of orthographic format effects is the lowercase superiority effect, whereby, compared to words in uppercase, words in lowercase yield shorter response times and shorter fixation times (Tinker and Paterson, 1928 ; see also Perea, Fernández-López & Marcet, 2020 ; Perea, Rosa & Marcet, 2017 ). Two complementary explanations have been proposed for the lowercase superiority effect. First, greater exposure to lowercase text compared to all-uppercase—used mainly for titles and acronyms—establishes it as the perceptual default (Perea et al., 2017 ). Second, lowercase letters feature distinctive ascenders ("k", "d") and descenders ("j", "g") that create unique visual shapes absent in uniform-height uppercase letters (Paterson and Tinker, 1940). From a developmental perspective, letter case sensitivity emerges gradually as children acquire alphabet knowledge and begin formal reading instruction. Early in literacy acquisition, children typically learn to recognize and name uppercase letters first, with accurate lowercase recognition lagging behind and improving as reading experience increases (e.g., van de Walle de Ghelcke et al., 2021). At these early stages, children often show a preference for one case over the other, but this preference quickly gives way to an abstract notion of letter identity that generalizes across visual forms (Grainger et al., 2008 ; Grainger, 2022 ). As this abstract letter identity becomes established, case information is relegated to a secondary role and develops the ability to identify letters efficiently regardless of whether they appear in uppercase or lowercase. In adult readers, case effects become much smaller but do not disappear entirely, with behavioural studies showing a subtle yet reliable advantage for lowercase words and word sequences in accuracy and/or speed (e.g., Perea & Rosa, 2002 ; Vergara-Martínez et al., 2020 ). Recent work shows that the lowercase advantage reflects more efficient early visuo-orthographic processing and case-independent letter access (Fournet et al., 2022 ). However, some formats disrupt letter-shape familiarity even more strongly than uniform uppercase, because they alter not only letter size but also the internal pattern of cases within a word. Among these, the alternation of uppercase and lowercase letters within a word, namely, mixed-case, e.g.,‘LOTTERY’ spelled as ‘LoTtErY’ (Mayall et al., 1997 ) represents a paradigmatic example. This alternation occurs naturally in many languages, as in proper names or sentence beginnings, and, in some languages, common nouns, but it does not occur in multiple instances within a string. Perea et al. ( 2015 ) provided a seminal analysis of mixed-case processing, demonstrating that this format disrupts word recognition by impairing letter grouping and case uniformity. Although subsequent studies remain limited, the existing evidence consistently shows that mixed-case imposes greater processing demands than conventional formats, slowing reading and hindering efficient word recognition (Mayall et al., 2001 ; McClelland, 1977 ; Poulton, 1969 ). This format particularly disrupts orthographic processing except for proper names, where the initial capital can facilitate recognition (Peressotti et al., 2003 ). This disruption has been said to arise from breaking case uniformity and impairing letter grouping due to varying heights (Arditi & Cho, 2007 ; Mayall et al., 1997 ; Mayall & Humphreys, 1996 ). While mixed-case alternations constitute a clear deviation from conventional word forms, they still preserve the original orthographic identity of each letter. Other forms of visual distortion go a step further by systematically replacing one element in the written word with another —either due to strong physical overlap between characters (e.g., o–O) or phonological similarity (e.g., φ–f, which map onto /f/)— thus altering the graphical identity of the written word more radically. A clear example is Greeklish, which can be considered a form of transliteration, although it often lacks a fixed one-to-one correspondence between source and target characters. In Greeklish, Greek words are written using Latin characters that are phonologically similar to the original letters (e.g., Greek λοταρία ‘lottery’ rendered as lotaría or lotaria ). This informal way of writing emerged in digital communication as a practical solution to software and hardware limitations on Greek-script input, and it remains frequent in online and messaging contexts (Chalamandaris et al., 2006). In this case, the alphabet changes, but phonological similarity between characters supports word recognition (Dimitropoulou et al., 2011 ). While Greeklish primarily reflects a functional adaptation to technological constraints, other formats, such as LEET, employ similar substitution principles for strategic purposes, including bypassing content moderation filters on digital platforms. LEET (or L33T) represents a more extreme case within digital environments: letters are replaced by orthographically similar digits or symbols (e.g.,’ lottery’ → ‘ L0TT3RY ’; Perea et al., 2008 ; Grabbe, 2016 ). This substitution adds another layer of complexity to reading comprehension, as LEET disrupts word recognition by altering the letters rather than their format, thereby challenging the abstract letter identity tolerance that normally facilitates efficient word processing (Fournet et al., 2022 ). The users of platforms like Twitch and online games use LEET as a strategy to bypass moderation filters and to avoid the detection of censoring systems in online platforms, exploiting the fact that human readers can still recover the intended word while early filtering systems could not (Märtens, et al., 2015). Given its peculiar orthographic characteristics, LEET has attracted attention in experimental studies exploring how altered visual word forms are processed during reading. For example, Perea et al. ( 2008 ) showed in a masked priming lexical decision task that visually similar LEET primes (e.g., L0TT3RY–LOTTERY) produce priming effects comparable to identity primes (e.g., LOTTERY–LOTTERY), relative to control primes (e.g., L6TT2R7–LOTTERY). These findings suggest that the digit-to-letter conversion process in LEET depends on significant visual resemblance between the digit and the letter it substitutes. This resemblance allows digits to function as letter instances and activate lexical representations (Carreiras, Duñabeitia & Perea, 2007 ; Kinoshita et al., 2014 ; Perea et al., 2008 ), likely because readers are used to assimilating letter identities across a variety of fonts and handwriting styles (see Manso de Zuniga et al., 1991 ). As argued by Molinaro et al. ( 2010 ), when embedded in letter strings, visually similar digits are readily regularized and encoded in a letter-like manner, whereas the reverse process (letters embedded in digit strings) does not occur to the same extent, indicating an asymmetry between letter and digit processing systems. Building on these perceptual mechanisms, it is also important to consider characteristics of the reader that may modulate how such formats are processed. Beyond these perceptual constraints, an understudied factor in the current literature on LEET and mixed-case, and even standard case formats, is the modulatory role of age. There are at least two ways in which age and age-related factors might be relevant for how readers process text presented in these alternative formats: age may influence reading either as a proxy for experience or through broader developmental and aging-related changes in reading and orthographic processing (Perea et al., 2008 ). Understanding these age-related differences requires considering how reading mechanisms evolve across the lifespan. Developmentally, young readers rely more on serial, fine-grained processing of letter identities and positions, gradually transitioning to more parallel and efficient lexical access as they gain reading experience (Acha & Perea, 2008 ). In adulthood, however, aging leads to declines in early visual stages and orthographic processing, causing older readers to depend increasingly on lexical-level information, with a reduced impact of lower-level orthographic detail (MacKay & Abrams, 1998 ; Spieler & Balota, 2000 ; Froehlich et al., 2016 ). Nevertheless, despite these age-related declines in peripheral visual and orthographic processing, central lexical functions remain relatively intact in older adults, who exhibit slower reaction times but preserved sensitivity to lexical-level information (Allen et al., 2002 , 2004 ). Morphological processing is also fully preserved in advanced age (Duñabeitia et al., 2009 ). Normative studies also suggest that older adults often show equal or even superior vocabulary knowledge relative to younger adults, as reflected in larger lexical repertoires (Keuleers et al., 2015 ; Aguasvivas et al., 2020 ). Moreover, individual differences in vocabulary and print exposure can mitigate some age-related deficits, enabling more efficient lexical processing despite the general effects of aging (Cohen-Shikora & Balota, 2016 ). Consistent with this view, older adults show greater impairments in peripheral letter encoding for visually demanding formats like mixed-case and unfamiliar fonts, particularly in terms of speed and efficiency (e.g., Allen & Madden, 1989 ; Allen et al., 1993 ; Allen et al., 2002 , 2011 ; Mayall, 2001, 2002 ). While these studies indicate that aging exacerbates difficulties under nonstandard visual conditions, how specific atypical formats are processed across the adult lifespan remains underexplored, particularly for LEET and for direct contrasts between lowercase and uppercase word recognition, as prior work has typically relied on age-homogeneous samples and limited case manipulations. The present study fills these gaps by testing adults with ages ranging from 18 to 84 years and examining how processing and comprehension of conventional (lowercase, uppercase) and unconventional (mixed-case, LEET) text formats vary across the adult lifespan. Specifically, it builds on prior research that examined these formats individually to trace how case and format effects change over the course of adult life. Our study seeks to determine whether a stable preference for one case emerges or shifts with age, examining whether age-related changes in analytic/holistic processing balance and letter encoding efficiency modulate the disruptions caused by atypical orthography. Methods Participants The sample consisted of 303 native English speakers (55% women, 44.7% men, 0.3% other), aged 18–84 years, recruited via Prolific Academic (Palan & Schitter, 2018). Figure 1 shows the age distribution of participants (mean = 50.81 years, SD = 18.67, range 18–84). Participants provided written informed consent for their involvement in accordance with the Declaration of Helsinki. The study protocols were approved by the Research Ethics Committee at Nebrija University (UNNE-2022-0017). Materials The stimuli consisted of 27 unique English sentences presented in four orthographic formats (lowercase, uppercase, mixed-case and LEET), for a total of 108 sentence stimuli. The 27 unique sentence stimuli resulted from creating all possible combinations of three colours ( green , orange and white ), three numbers ( five , eight and nine ) and three animals ( rabbit , elephant and tiger ), after which each sentence was rendered in the four orthographic formats. The LEET numbers employed were: A = 4, E = 3 and I = 1. Sentences followed the structure ‘There are [Number] [Colour] [Animal]’ (lowercase: ‘There are eight orange elephants’; uppercase: ‘THERE ARE EIGHT ORANGE ELEPHANTS’; mixed-case: ‘ThErE aRe EiGhT oRaNgE eLePhAnTs’; LEET: ‘TH3R3 4R3 31GHT 0R4NG3 3L3PH4NTS’). The presentation order was randomized for each participant. All participants read each of the 108 total sentences once. In addition to the sentences, square icons representing the three animals, three numbers and three colours were built to use in the comprehension check after each sentence (see Fig. 2 below). Task and procedure The experiment was conducted using Gorilla (Anwyl-Irvine et al., 2020 ), an online experiment builder and testing platform that allowed for accuracy and reading times to be automatically recorded. All sentences were displayed in Courier font, size 55. Participants completed the task within a maximum of 45 minutes, including a scheduled break halfway through the experiment. The procedure began with an instruction screen that clearly explained the task. This was followed by a short practice phase consisting of four trials—one for each typographic format—to familiarize participants with the procedure. Upon completing the practice, a screen informed participants that the experimental task was about to begin. A progress bar was displayed throughout the experiment to indicate how much of the task remained. Each trial followed a fixed timeline. First, a fixation cross appeared at the centre of the screen for 1500 ms to focus participants’ attention. Immediately afterward, the target sentence was presented along with the prompt "Press after reading." This prompt remained visible throughout the sentence display. Participants had a maximum of 3000 ms to read the sentence and press the spacebar. If they did not press the spacebar within this time, the sentence disappeared automatically and the experiment advanced to the next phase. Following the reading phase, a comprehension check was conducted through three successive screens. In each screen, participants had to identify one of the key elements mentioned in the sentence: a number, a colour, or an animal. All the possible options for each element were displayed on each screen, and participants were required to select the one that corresponded to the sentence they had just read by clicking on the correct option. Each response screen had a maximum duration of 5000 ms; if no response was provided within this time, the trial was marked as a timeout. Two dependent variables were analysed: reading time and comprehension accuracy. Reading times were recorded as the latency between sentence onset and the participant’s keypress. Comprehension accuracy was calculated as the percentage of correct selections across the three comprehension questions presented in each trial—i.e., a trial was considered correctly answered only if all three questions pertaining to that trial were answered correctly. Results Accuracy While our analyses focus predominantly on reading times, measuring accuracy was a necessary check to ensure that participants were reading the sentences. Sentence comprehension was considered accurate only when all three questions—regarding number, colour and animal— were answered correctly. From the original sample of 303 participants, three were excluded due to low accuracy scores, < 67% (i.e., fewer than 2/3 questions correct), which indicated a lack of attention to the task. As expected, the proportion of accurate trials in the image selection task was high: for trials with lowercase text it was 0.966 (SD = 0.055), for uppercase it was 0.960 (SD = 0.067), for mixed-case it was 0.966 (SD = 0.061), and for LEET it was 0.943 (SD = 0.084). This shows that, overall, participants were able to read and understand the simple sentences regardless of format, and that they were paying attention to the task. Given the numerical differences across formats, we conducted statistical analyses exploring the effects of presentation format and participant age through a generalized linear mixed model with a binomial family, using the function glmer in the R package lme4 (Bates et al., 2015 ), and under the philosophy of reporting maximal random-effects structure justified by the design (Barr et. al., 2013), but reporting only the models that converged and were not singular: glmer(Accuracy ~ Age * Format + (1 + Format | Participant) + (1 + Format || Item), family = binomial) Accuracy data (n = 129,600 observations) were assessed through a generalized mixed-effects model analysis. Descriptive statistics by presentation format are reported in Table 1 . The model revealed a significant main effect of Age (centred), χ²(1) = 9.73, p = .002, demonstrating that accuracy decreased with age (see Fig. 3 ). Also, a significant main effect was found for Format, χ²(3) = 15.46, p = .001. No significant interaction was found, χ²(3) = 0.60, p = .895, suggesting that differences in accuracy across formats were consistent across age groups, as illustrated in Fig. 3 . In this model, LEET served as the reference level for the Format factor. The model revealed that comprehension accuracy in sentences displayed in LEET format was significantly lower than lowercase and mixed-case, and marginally lower than uppercase (LEET vs. lowercase: β = -0.45, SE = 0.14, z = -3.31, p = 0.006 ; LEET vs. uppercase: β = -0.35, SE = 0.14, z = -2.63, p = 0.051; LEET vs. mixed-case: β = -0.47, SE = 0.14, z = -3.47, p = 0.003). Table 1 . Proportion corrects and mean Reading Times (ms) by presentation format. Format Correct SD Mean RT SD (by participant) Lowercase 0.966 0.055 1067 323 Uppercase 0.960 0.067 1088 328 Mixed-Case 0.966 0.061 1117 340 LEET 0.943 0.084 1322 343 Note. SD for proportion correct computed across participants; SD for reading times computed by participant. Reading Times Reading times associated with correct responses were analyzed. In addition, reading times shorter than 250ms (0.74% of all trials) and longer than 2500ms (2% of all trials) were eliminated. Table 1 shows the mean and standard deviation (calculated across participants) for the four presentation formats. Reading time was fastest for lowercase and slowest for LEET, with uppercase and mixed-case clustering closer to lowercase (21 and 50 ms slower than lowercase, respectively, vs. 255 ms slower in the case of LEET). These analyses were followed by exploratory data visualizations to examine the distributional features of the observed effects via delta plots. To analyze the effects of presentation format and participant age on reading times, we used a linear mixed-effects model using the lmer() function with the lmerTest package in R (Kuznetsova et al., 2017). Lmer(RT ~ Age * Format + TrialOrder + (1 | Participant) + (1 | Item) In this model, Format, Age and their interaction were specified as fixed effects, and random intercepts were included for Participant and Item. LEET served as the reference level for the Format factor. Trial Order (centered) was included in the model as a covariate to control for potential practice or fatigue effects over the course of the session. We retained a random-intercepts-only structure for subjects and items because more complex random-effects specifications led to singular fits, indicating that the data do not support estimating additional variance components reliably. Reaction Time data (n = 26,226 observations) were analysed through a linear mixed-model analysis. Results showed a significant main effect of Age (centred), F (1, 262.7) = 22.48, p < .001, demonstrating that reaction times increased as a function of age. Furthermore, a significant main effect of Format was found, F (3, 102.9) = 118.95, p < .001. Reading times in sentences displayed in LEET format were significantly slower than in any other format (LEET vs. lowercase: β = -273.3, SE = 16.38, t(103.2) = -16.68, p < 0.001; LEET vs. uppercase: β = -250.6, SE = 16.38, t(103.3) = -15.30, p < 0.001; LEET vs. mixed-case: β = -225.72, SE = 16.38, t(103.3) = -13.77, p < 0.001). A significant main effect of Trial Order was also observed, F (1, 25,867.7) = 1627.16, p < .001, showing that participants identified the words faster over the course of the experiment. Notably, a significant Age × Format interaction was found, F (3, 25,840.7) = 28.77, p < .001, indicating that age-related slowing was more pronounced for the LEET format than for the other formats. In other words, while reading times increased with age across all formats, age-related slowing was particularly pronounced for the LEET format compared to the others. Figure 3 illustrates this pattern, showing a clear separation between LEET and the remaining formats, as well as a general increase in reading times with age. Follow-up analyses showed that the slopes relating Age to reading time were significantly less steep for lowercase (β = − 2.11, SE = 0.28, t (25840) = − 7.46, p < 0.001), uppercase (β = − 2.27, SE = 0.28, t (25840) = − 7.99, p < 0.001), and mixed-case (β = − 2.12, SE = 0.28, t (25840) = − 7.47, p < 0.001). To directly estimate the age slope for LEET, the model was reparameterized with Lowercase as the reference level, revealing a significant increase in reading time with age for LEET sentences (β = 2.11, SE = 0.28, t (25,840) = 7.46, p < .001). Discussion The goal of this study was to examine how variations in typographical formats (lowercase, uppercase, mixed-case, LEET) affect reading times and comprehension across the adult lifespan (18–84 years), building on established effects like lowercase superiority (Perea et al., 2017 , 2020 ) and case-mixing costs (Mayall et al., 1997 ). As noted in the introduction, it was expected that LEET would involve longer reading times, suggesting a higher cognitive load compared to conventional formats (lowercase, uppercase, mixed-case). The results confirmed these predictions. Conventional formats yielded the fastest reading times and high accuracy, mixed-case imposed moderate costs, and LEET generated substantial disruptions in reading, accompanied by slightly reduced comprehension accuracy comprehension. Although accuracy remained high across all formats, reading sentences in the LEET format resulted in reliably lower comprehension across age groups. Crucially, analyses of reading times revealed that the disruptive effect of LEET increased with age, whereas age-related slowing was less pronounced for conventional formats, yielding a significant Format × Age interaction that would not be observable in age-homogeneous samples. These results are consistent with prior research documenting the lowercase superiority effect, whereby lowercase words are processed faster and more efficiently than uppercase ones (Perea et al., 2017 ; Vergara-Martínez et al., 2020 ; Perea et al., 2020 ). They also align with evidence that orthographic distortions, such as case mixing, disrupt reading fluency by breaking familiar visual regularities and increasing processing demands (Arditi & Cho, 2007 ; Mayall et al., 1997 ; Tinker, 1963 ). Beyond mean effects, delta-plot analyses further clarified the nature of these processing costs. Vincentile-based delta plots comparing LEET with lowercase, uppercase, and mixed-case formats showed consistently positive deltas across quantiles, indicating slower reading times for LEET sentences throughout the RT distribution. Notably, the LEET–mixed-case contrast exhibited steeper slopes toward slower responses—particularly in older participants—suggesting that LEET engages processing mechanisms that place disproportionate demands on perceptual encoding and integration as reading unfolds. This pattern extends masked priming findings, in which visually similar LEET primes can activate lexical representations (Carreiras et al., 2007 ; Kinoshita et al., 2014 ), to naturalistic reading contexts where age-related constraints amplify the costs of orthographic distortion. These differences illuminate how distortions differentially tax visual word recognition mechanisms: while mixed-case preserves abstract letter identity access through standard alphabetic characters (Fournet et al., 2022 ), LEET imposes additional symbol-to-letter recoding demands, as evidenced by delta plots showing costs magnified for slower responses (Perea et al., 2008 ). Vincentile-based delta plots (0.1, 0.3, 0.5, 0.7, 0.9) of correct responses comparing LEET (baseline) with lowercase, uppercase, and mixed-case formats confirmed this pattern across all quantiles (Fig. 5 ), revealing consistently positive deltas indicating slower LEET reading times. Specifically, the LEET–lowercase contrast showed a positive slope with increasing costs at longer RTs, suggesting greater cognitive demand in resolving orthographic distortions; the LEET–uppercase comparison exhibited relatively stable deltas, indicating uniform cost; and the LEET–mixed-case comparison displayed an upward slope toward slower RTs—particularly amplified in older participants—pointing to qualitatively distinct processing mechanisms for LEET as reading unfolds. This extends masked priming findings—where visually similar LEET primes activate lexical representations (Carreiras et al., 2007 ; Kinoshita et al., 2014 ), to naturalistic reading, where contextual integration amplifies age-related cognitive demands. Age modulated these format effects in a manner consistent with prior evidence of heightened vulnerabilities in older adults' under visually demanding conditions, particularly at early perceptual and pre-lexical stages of processing (Allen et al., 2002 , 2004 ; Mayall, 2001, 2002 ) rather than reflecting age-related changes in lexical representations per se (Perea et al., 2008 ). Specifically, disruption in the LEET format amplified with age—evidenced by steeper delta plot slopes in LEET–mixed-case contrasts toward slower reading times—while conventional formats (lowercase and uppercase) showed comparatively smaller age-related costs and more stable delta patterns. This Format × Age interaction indicates that aging exacerbates the impact of orthographic irregularities that degrade visual regularities feeding into the lexical system, without implying a general decline in lexical processing. In this sense, LEET appears to place disproportionate demands on perceptual encoding and integration mechanisms that precede lexical access, thereby revealing age-related differences that are not apparent for visually canonical formats in age-homogeneous samples. These results suggest that the mechanisms underlying LEET processing differ qualitatively from those involved in case mixing. Whereas mixed-case disrupts global visual regularity, LEET requires an additional layer of symbol-to-letter recoding that particularly taxes older readers, whose analytic processing resources are more limited (Allen et al., 2011 ). Age effects were most pronounced for LEET: while younger readers tolerated LEET through flexible letter identity normalization (likely reflecting both digital exposure and preserved early visual processing), older adults showed disproportionate slowdowns and comprehension losses. This interpretation is further supported by delta plot analyses, which revealed that the cost of LEET increased disproportionately for slower responses, and aligns with lifespan shifts toward holistic, vocabulary-supported routes where compensatory lexical gains (Keuleers et al., 2015 ; Aguasvivas et al., 2020 ) preserve comprehension but cannot offset early perceptual demands (Spieler & Balota, 2000 ; Cohen-Shikora & Balota, 2016 ). Consequently, while confirming established concepts like lowercase advantage and mixed-case disruption, our findings extend the literature by demonstrating how LEET's unique processing demands interact with age-related declines in peripheral orthographic encoding. From a practical perspective, these findings underscore the challenges that unconventional formats such as LEET impose on reading efficiency and comprehension, particularly for older adults. Unlike children's gradual abstraction of case-invariant letter identities across conventional formats (Grainger, 2022 ), adult aging reveals limits to this abstraction under extreme orthographic distortion: while developmental gains reflect experience-driven parallelization of skilled reading (Acha & Perea, 2008 ; Perea et al., 2017 ), aging reverses this efficiency for visually irregular formats like LEET. Although our data did not directly measure participants’ prior exposure to LEET, the consistently slower reading times and reduced accuracy (disproportionate in older adults) suggest that familiarity alone may not be sufficient to compensate for LEET's demands on residual analytic resources beyond what compensatory lexical mechanisms can offset (Vergara-Martínez et al., 2020 ). Naturally, this study contains some limitations. First, the experimental materials consisted of short, artificial sentences and limited LEET substitutions (A = 4, E = 3, I = 1), constraining ecological validity since real LEET use involves diverse, context-specific substitutions encountered in chat messages and gaming environments. Second, participants' prior familiarity with LEET was not measured, even though previous exposure, and age as its proxy, could plausibly modulate both accuracy and reading times, limiting causal attribution between format per se and processing costs. Future work should therefore incorporate more ecological tasks, such as real chat messages or online conversations, to better approximate the contexts in which LEET is typically encountered. Moreover, combining behavioural measures with eye-tracking and EEG would provide a more precise characterization of the perceptual and lexical integration processes involved. In addition, a forthcoming study with Spanish participants residing in Morocco will explore these aspects in individuals regularly exposed to Darija, a dialect of Moroccan Arabic whose writing system incorporates the LEET format. Such a population offers a valuable opportunity to determine whether sustained exposure to LEET reduces its processing costs, thereby clarifying the extent to which familiarity and linguistic environment can mitigate the cognitive challenges posed by unconventional orthographic formats across development, adulthood, and aging. In conclusion, this study demonstrates graded orthographic processing costs (conventional formats < mixed-case < LEET) that interact with age across the adult lifespan (18–84 years), confirming established effects like lowercase superiority and case-mixing disruption while revealing LEET's unique symbol-to-letter recoding demands. By tracing format effects from young adulthood to advanced age, these findings integrate developmental, adult, and aging research, highlighting analytic processing decline as the critical vulnerability to digital orthographies. The current study not only contributes to the basic science of reading, but results advance equitable typography guidelines for education, digital design, and communication contexts that minimize age-related barriers across digital ecosystems. Declarations Acknowledgments This research has been partially funded by grant PID2024-161331NB-I00 (MCINN/AEI/10.13039/501100011033) and PRE2022-104069. Funding This research was partially funded by the State Plan for Scientific and Technical Research and Innovation of the Government of Spain with grant numbers PRE2022-104069 and PID2024-161331NB-I00, as well as the Research Council of Norway, grant number 326487-FORSKER2, and the Community of Madrid (grant number 020-T1/HUM-20037). 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Case-mixing effects on children’s word recognition: Lexical feedback and development. Q. J. Experimental Psychol. Sect. A . 55 (2), 525–542. https://doi.org/10.1080/02724980143000334 (2002). Mayall, K. & Humphreys, G. A connectionist model of alexia: Covert recognition and case mixing effects. Br. J. Psychol. 87 (3), 355–402. https://doi.org/10.1111/j.2044-8295.1996.tb02597.x (1996). Mayall, K., Humphreys, G. W., Mechelli, A., Olson, A. & Price, C. J. The effects of case mixing on word recognition: Evidence from a PET study. J. Cogn. Neurosci. 13 (6), 844–853. https://doi.org/10.1162/08989290152541494 (2001). Mayall, K., Humphreys, G. W. & Olson, A. Disruption to word or letter processing? The origins of case-mixing effects. J. Exp. Psychol. Learn. Mem. Cognit. 23 (5), 1275–1286. https://doi.org/10.1037//0278-7393.23.5.1275 (1997). McClelland, J. L. Letter and configuration information in word identification. J. Verbal Learn. Verbal Behav. 16 (1), 137–150. https://doi.org/10.1016/S0022-5371(77)80015-7 (1977). Minakata, K. & Beier, S. The effect of font width on eye movements during reading. Appl. Ergon. 97 , 103523. https://doi.org/10.1016/j.apergo.2021.103523 (2021). Molinaro, N., Duñabeitia, J. A., Marìn-Gutièrrez, A. & Carreiras, M. From numbers to letters: Feedback regularization in visual word recognition. Neuropsychologia 48 (5), 1343–1355. https://doi.org/10.1016/j.neuropsychologia.2009.12.037 (2010). Palan, S. & Schitter, C. Prolific.ac—A subject pool for online experiments. Journal of Behavioral and Experimental Finance , 17 . (2017). https://doi.org/10.1016/j.jbef.2017.12.004 Paterson, D. G. & Tinker, M. A. How to make type readable (pp. xix, 209). Harper. (1940a). https://archive.org/details/HowToMakeTypeReadable/page/n43/mode/2up Paterson, D. G. & Tinker, M. A. Influence of line width on eye movements. J. Exp. Psychol. 27 (5), 572–577. https://doi.org/10.1037/h0054498 (1940b). Perea, M., Duñabeitia, J. A. & Carreiras, M. R34D1NG W0RD5 W1TH NUMB3R5. J. Exp. Psychol. Hum. Percept. Perform. 34 (1), 237–241. https://doi.org/10.1037/0096-1523.34.1.237 (2008). Perea, M., Duñabeitia, J. A., Pollatsek, A. & Carreiras, M. Does the brain regularize digits and letters to the same extent? Q. J. Experimental Psychol. 62 (10), 1881–1888. https://doi.org/10.1080/17470210902923374 (2009). Perea, M., Fernández-López, M. & Marcet, A. Does CaSe-MiXinG disrupt the access to lexico-semantic information? Psychol. Res. 84 (4), 981–989. https://doi.org/10.1007/s00426-018-1111-7 (2020). Perea, M. & Rosa, E. Does «whole-word shape» play a role in visual word recognition? Percept. Psychophys. 64 (5), 785–794. https://doi.org/10.3758/bf03194745 (2002). Perea, M., Rosa, E. & Marcet, A. Where is the locus of the lowercase advantage during sentence reading? Acta. Psychol. 177 , 30–35. https://doi.org/10.1016/j.actpsy.2017.04.007 (2017). Perea, M., Vergara-Martínez, M. & Gomez, P. Resolving the locus of cAsE aLtErNaTiOn effects in visual word recognition: Evidence from masked priming. Cognition 142 , 39–43. https://doi.org/10.1016/j.cognition.2015.05.007 (2015). Peressotti, F., Cubelli, R. & Job, R. On recognizing proper names: The orthographic cue hypothesis. Cogn. Psychol. 47 (1), 87–116. https://doi.org/10.1016/S0010-0285(03)00004-5 (2003). Poulton, E. C. Asymmetrical transfer in reading texts produced by teleprinter and by typewriter. J. Appl. Psychol. 53 (3), 244–249. https://doi.org/10.1037/h0027417 (1969). Scaltritti, M. et al. Investigating Effects of Typographic Variables on Webpage Reading Through Eye Movements. Sci. Rep. 9 (1), 12711. https://doi.org/10.1038/s41598-019-49051-x (2019). Snowling, M. J. & Hulme, C. The science of reading: A handbook (pp. xiv, 661). Blackwell Publishing. (2005). Spieler, D. H. & Balota, D. A. Factors influencing word naming in younger and older adults. Psychol. Aging . 15 (2), 225–231. https://doi.org/10.1037/0882-7974.15.2.225 (2000). Staub, A. Do effects of visual contrast and font difficulty on readers’ eye movements interact with effects of word frequency or predictability? J. Exp. Psychol. Hum. Percept. Perform. 46 (11), 1235–1251. https://doi.org/10.1037/xhp0000853 (2020). Tinker, M. A. & with Internet Archive. Legibility of print (Iowa State University, 1963). http://archive.org/details/legibilityofprin0000tink Tinker, M. A. & Paterson, D. G. Influence of type form on speed of reading. J. Appl. Psychol. 12 (4), 359–368. https://doi.org/10.1037/h0073699 (1928). van de Walle, A., Rossion, B., Schiltz, C. & Lochy, A. Developmental changes in neural letter-selectivity: A 1-year follow-up of beginning readers. Dev. Sci. 24 (1), e12999. https://doi.org/10.1111/desc.12999 (2021). Vergara-Martínez, M., Perea, M. & Leone-Fernandez, B. The time course of the lowercase advantage in visual word recognition: An ERP investigation. Neuropsychologia 146 , 107556. https://doi.org/10.1016/j.neuropsychologia.2020.107556 (2020). Additional Declarations No competing interests reported. Supplementary Files demographicsleet.csv datarawleet.csv Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9093730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607788611,"identity":"24d90c73-1d42-4c8f-9680-077921135213","order_by":0,"name":"Natasha Alonso-Bernal","email":"","orcid":"","institution":"Universidad Nebrija","correspondingAuthor":false,"prefix":"","firstName":"Natasha","middleName":"","lastName":"Alonso-Bernal","suffix":""},{"id":607788612,"identity":"cd19bd7b-dc18-455f-8411-63c3039c0d78","order_by":1,"name":"Jorge González Alonso","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACxgYwZQHEzI0PGBgOMLABMTFaJEDMZgOitEABWEubBAMxypn7Fx97+KNGQk6+/WBbdUHFnXw+xsMPGD78weOwGc/SDSSOSRgz9iS23Z5x5pllG8MxA8aZbfi0nDGTMGCTSGxmAGrhbTtsAPSLATNvAwEtCf8k6tv4H7YV8/4DaTn+gfkPPof195hJHGyTSOCRSGwDGg7ScsaAGRhueGxhS5Ns7JMwnCHxsFma59gzkJaCg714/GLYf/iY5I9vNvLy/ckHP/PU3DGQn3F844MfeBxmOCMBXUjiAP7YkefHkOZvwKdhFIyCUTAKRiAAAOJ5VYRahd7GAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Nebrija","correspondingAuthor":true,"prefix":"","firstName":"Jorge","middleName":"González","lastName":"Alonso","suffix":""},{"id":607788613,"identity":"851f8686-dc8d-4659-b3a5-05c5e9ab9b08","order_by":2,"name":"Pablo Gómez","email":"","orcid":"","institution":"Skidmore College","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Gómez","suffix":""},{"id":607788614,"identity":"e63745dc-0cb3-4652-bfb7-3c6ec73113c9","order_by":3,"name":"Jon Andoni Duñabeitia","email":"","orcid":"","institution":"Universidad Nebrija","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"Andoni","lastName":"Duñabeitia","suffix":""}],"badges":[],"createdAt":"2026-03-11 11:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9093730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9093730/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104996503,"identity":"523d8e88-8af3-400c-8a4f-b79f92ca69dc","added_by":"auto","created_at":"2026-03-19 16:15:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141826,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of participants’ ages across quartiles (quartile boundaries indicated by dotted red lines).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/44049bc305e05766a3942d55.png"},{"id":104996508,"identity":"fb9adc00-9755-4f92-8771-1d45e640e868","added_by":"auto","created_at":"2026-03-19 16:15:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46828,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of the task interface participants were exposed to. The first two screens depict the initial fixation plus reading sequence. The remaining three screens show the multiple-choice comprehension task the participant performed based on the sentence.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/ef124501007c4ef0613d9a81.png"},{"id":105035352,"identity":"90c6408f-c92a-4fa1-8907-71f6a4aabbec","added_by":"auto","created_at":"2026-03-20 07:25:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":221339,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted accuracy and reading time as a function of age, based on a generalized linear mixed-effects model described in the main text with a Format × Age interaction. Coloured lines represent the estimated marginal means for each format, with shaded ribbons indicating 95% confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/184474ac901ef01d18c7a3fb.png"},{"id":104996505,"identity":"99714b3f-bd5c-4c9b-9855-b6b1904cce71","added_by":"auto","created_at":"2026-03-19 16:15:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e Delta plots the differences between LEET and other formats.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/2cd91bb7a288083e34148c27.png"},{"id":105036842,"identity":"6f682187-0fd4-42bf-bca2-8602fcfc1135","added_by":"auto","created_at":"2026-03-20 07:36:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1048185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/a9a6f557-7aa1-4819-b6c0-75797e940f62.pdf"},{"id":104996504,"identity":"55890a20-1fe9-43f1-b1fc-11de2cade5b5","added_by":"auto","created_at":"2026-03-19 16:15:01","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17483,"visible":true,"origin":"","legend":"","description":"","filename":"demographicsleet.csv","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/a0f305e5ecd805563aaf6622.csv"},{"id":104996509,"identity":"47bd64e3-51b0-4dd7-a043-c4b19641bf5b","added_by":"auto","created_at":"2026-03-19 16:15:02","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34114625,"visible":true,"origin":"","legend":"","description":"","filename":"datarawleet.csv","url":"https://assets-eu.researchsquare.com/files/rs-9093730/v1/f3d7be7972cb791d2ddae06b.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-Related Differences in Processing Unconventional Text Formats","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStudies in visual word recognition investigate how readers perceive, identify, and decode printed words, from the extraction of visual features through letter recognition to accessing meaning and pronunciation within the language system (Snowling, Hulme \u0026amp; Nation, 2022). This line of research aims to clarify the stepwise mechanisms underlying reading and written language comprehension, with an emphasis on how orthographic information activates linguistic representations. Among the multiple variables that modulate the reading process, recent literature has established that the physical properties of written words and their constituent letters (e.g., spacing, font width, orthographic format) influence the early stages of visual word recognition (Minakata \u0026amp; Liversedge, 2021; Scaltritti et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Staub, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA particularly robust demonstration of orthographic format effects is the lowercase superiority effect, whereby, compared to words in uppercase, words in lowercase yield shorter response times and shorter fixation times (Tinker and Paterson, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1928\u003c/span\u003e; see also Perea, Fern\u0026aacute;ndez-L\u0026oacute;pez \u0026amp; Marcet, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perea, Rosa \u0026amp; Marcet, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Two complementary explanations have been proposed for the lowercase superiority effect. First, greater exposure to lowercase text compared to all-uppercase\u0026mdash;used mainly for titles and acronyms\u0026mdash;establishes it as the perceptual default (Perea et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, lowercase letters feature distinctive ascenders (\"k\", \"d\") and descenders (\"j\", \"g\") that create unique visual shapes absent in uniform-height uppercase letters (Paterson and Tinker, 1940).\u003c/p\u003e \u003cp\u003eFrom a developmental perspective, letter case sensitivity emerges gradually as children acquire alphabet knowledge and begin formal reading instruction. Early in literacy acquisition, children typically learn to recognize and name uppercase letters first, with accurate lowercase recognition lagging behind and improving as reading experience increases (e.g., van de Walle de Ghelcke et al., 2021). At these early stages, children often show a preference for one case over the other, but this preference quickly gives way to an abstract notion of letter identity that generalizes across visual forms (Grainger et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Grainger, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As this abstract letter identity becomes established, case information is relegated to a secondary role and develops the ability to identify letters efficiently regardless of whether they appear in uppercase or lowercase.\u003c/p\u003e \u003cp\u003eIn adult readers, case effects become much smaller but do not disappear entirely, with behavioural studies showing a subtle yet reliable advantage for lowercase words and word sequences in accuracy and/or speed (e.g., Perea \u0026amp; Rosa, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vergara-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent work shows that the lowercase advantage reflects more efficient early visuo-orthographic processing and case-independent letter access (Fournet et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, some formats disrupt letter-shape familiarity even more strongly than uniform uppercase, because they alter not only letter size but also the internal pattern of cases within a word. Among these, the alternation of uppercase and lowercase letters within a word, namely, mixed-case, e.g.,\u0026lsquo;LOTTERY\u0026rsquo; spelled as \u0026lsquo;LoTtErY\u0026rsquo; (Mayall et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) represents a paradigmatic example. This alternation occurs naturally in many languages, as in proper names or sentence beginnings, and, in some languages, common nouns, but it does not occur in multiple instances within a string.\u003c/p\u003e \u003cp\u003ePerea et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) provided a seminal analysis of mixed-case processing, demonstrating that this format disrupts word recognition by impairing letter grouping and case uniformity. Although subsequent studies remain limited, the existing evidence consistently shows that mixed-case imposes greater processing demands than conventional formats, slowing reading and hindering efficient word recognition (Mayall et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; McClelland, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Poulton, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). This format particularly disrupts orthographic processing except for proper names, where the initial capital can facilitate recognition (Peressotti et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This disruption has been said to arise from breaking case uniformity and impairing letter grouping due to varying heights (Arditi \u0026amp; Cho, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mayall et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Mayall \u0026amp; Humphreys, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile mixed-case alternations constitute a clear deviation from conventional word forms, they still preserve the original orthographic identity of each letter. Other forms of visual distortion go a step further by systematically replacing one element in the written word with another \u0026mdash;either due to strong physical overlap between characters (e.g., o\u0026ndash;O) or phonological similarity (e.g., φ\u0026ndash;f, which map onto /f/)\u0026mdash; thus altering the graphical identity of the written word more radically. A clear example is Greeklish, which can be considered a form of transliteration, although it often lacks a fixed one-to-one correspondence between source and target characters. In Greeklish, Greek words are written using Latin characters that are phonologically similar to the original letters (e.g., Greek λοταρία \u0026lsquo;lottery\u0026rsquo; rendered as \u003cem\u003elotar\u0026iacute;a\u003c/em\u003e or \u003cem\u003elotaria\u003c/em\u003e). This informal way of writing emerged in digital communication as a practical solution to software and hardware limitations on Greek-script input, and it remains frequent in online and messaging contexts (Chalamandaris et al., 2006). In this case, the alphabet changes, but phonological similarity between characters supports word recognition (Dimitropoulou et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While Greeklish primarily reflects a functional adaptation to technological constraints, other formats, such as LEET, employ similar substitution principles for strategic purposes, including bypassing content moderation filters on digital platforms.\u003c/p\u003e \u003cp\u003eLEET (or L33T) represents a more extreme case within digital environments: letters are replaced by orthographically similar digits or symbols (e.g.,\u0026rsquo; lottery\u0026rsquo; \u0026rarr; \u0026lsquo;\u003cem\u003eL0TT3RY\u003c/em\u003e\u0026rsquo;; Perea et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Grabbe, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This substitution adds another layer of complexity to reading comprehension, as LEET disrupts word recognition by altering the letters rather than their format, thereby challenging the abstract letter identity tolerance that normally facilitates efficient word processing (Fournet et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The users of platforms like Twitch and online games use LEET as a strategy to bypass moderation filters and to avoid the detection of censoring systems in online platforms, exploiting the fact that human readers can still recover the intended word while early filtering systems could not (M\u0026auml;rtens, et al., 2015).\u003c/p\u003e \u003cp\u003eGiven its peculiar orthographic characteristics, LEET has attracted attention in experimental studies exploring how altered visual word forms are processed during reading. For example, Perea et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) showed in a masked priming lexical decision task that visually similar LEET primes (e.g., L0TT3RY\u0026ndash;LOTTERY) produce priming effects comparable to identity primes (e.g., LOTTERY\u0026ndash;LOTTERY), relative to control primes (e.g., L6TT2R7\u0026ndash;LOTTERY). These findings suggest that the digit-to-letter conversion process in LEET depends on significant visual resemblance between the digit and the letter it substitutes. This resemblance allows digits to function as letter instances and activate lexical representations (Carreiras, Du\u0026ntilde;abeitia \u0026amp; Perea, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kinoshita et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Perea et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), likely because readers are used to assimilating letter identities across a variety of fonts and handwriting styles (see Manso de Zuniga et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). As argued by Molinaro et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), when embedded in letter strings, visually similar digits are readily regularized and encoded in a letter-like manner, whereas the reverse process (letters embedded in digit strings) does not occur to the same extent, indicating an asymmetry between letter and digit processing systems.\u003c/p\u003e \u003cp\u003eBuilding on these perceptual mechanisms, it is also important to consider characteristics of the reader that may modulate how such formats are processed. Beyond these perceptual constraints, an understudied factor in the current literature on LEET and mixed-case, and even standard case formats, is the modulatory role of age. There are at least two ways in which age and age-related factors might be relevant for how readers process text presented in these alternative formats: age may influence reading either as a proxy for experience or through broader developmental and aging-related changes in reading and orthographic processing (Perea et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Understanding these age-related differences requires considering how reading mechanisms evolve across the lifespan. Developmentally, young readers rely more on serial, fine-grained processing of letter identities and positions, gradually transitioning to more parallel and efficient lexical access as they gain reading experience (Acha \u0026amp; Perea, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In adulthood, however, aging leads to declines in early visual stages and orthographic processing, causing older readers to depend increasingly on lexical-level information, with a reduced impact of lower-level orthographic detail (MacKay \u0026amp; Abrams, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Spieler \u0026amp; Balota, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Froehlich et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, despite these age-related declines in peripheral visual and orthographic processing, central lexical functions remain relatively intact in older adults, who exhibit slower reaction times but preserved sensitivity to lexical-level information (Allen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Morphological processing is also fully preserved in advanced age (Du\u0026ntilde;abeitia et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Normative studies also suggest that older adults often show equal or even superior vocabulary knowledge relative to younger adults, as reflected in larger lexical repertoires (Keuleers et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aguasvivas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, individual differences in vocabulary and print exposure can mitigate some age-related deficits, enabling more efficient lexical processing despite the general effects of aging (Cohen-Shikora \u0026amp; Balota, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consistent with this view, older adults show greater impairments in peripheral letter encoding for visually demanding formats like mixed-case and unfamiliar fonts, particularly in terms of speed and efficiency (e.g., Allen \u0026amp; Madden, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Allen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mayall, 2001, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). While these studies indicate that aging exacerbates difficulties under nonstandard visual conditions, how specific atypical formats are processed across the adult lifespan remains underexplored, particularly for LEET and for direct contrasts between lowercase and uppercase word recognition, as prior work has typically relied on age-homogeneous samples and limited case manipulations.\u003c/p\u003e \u003cp\u003eThe present study fills these gaps by testing adults with ages ranging from 18 to 84 years and examining how processing and comprehension of conventional (lowercase, uppercase) and unconventional (mixed-case, LEET) text formats vary across the adult lifespan. Specifically, it builds on prior research that examined these formats individually to trace how case and format effects change over the course of adult life. Our study seeks to determine whether a stable preference for one case emerges or shifts with age, examining whether age-related changes in analytic/holistic processing balance and letter encoding efficiency modulate the disruptions caused by atypical orthography.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample consisted of 303 native English speakers (55% women, 44.7% men, 0.3% other), aged 18\u0026ndash;84 years, recruited via Prolific Academic (Palan \u0026amp; Schitter, 2018). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the age distribution of participants (mean\u0026thinsp;=\u0026thinsp;50.81 years, SD\u0026thinsp;=\u0026thinsp;18.67, range 18\u0026ndash;84). Participants provided written informed consent for their involvement in accordance with the Declaration of Helsinki. The study protocols were approved by the Research Ethics Committee at Nebrija University (UNNE-2022-0017).\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaterials\u003c/h3\u003e\n\u003cp\u003eThe stimuli consisted of 27 unique English sentences presented in four orthographic formats (lowercase, uppercase, mixed-case and LEET), for a total of 108 sentence stimuli. The 27 unique sentence stimuli resulted from creating all possible combinations of three colours (\u003cem\u003egreen\u003c/em\u003e, \u003cem\u003eorange\u003c/em\u003e and \u003cem\u003ewhite\u003c/em\u003e), three numbers (\u003cem\u003efive\u003c/em\u003e, \u003cem\u003eeight\u003c/em\u003e and \u003cem\u003enine\u003c/em\u003e) and three animals (\u003cem\u003erabbit\u003c/em\u003e, \u003cem\u003eelephant\u003c/em\u003e and \u003cem\u003etiger\u003c/em\u003e), after which each sentence was rendered in the four orthographic formats. The LEET numbers employed were: A\u0026thinsp;=\u0026thinsp;4, E\u0026thinsp;=\u0026thinsp;3 and I\u0026thinsp;=\u0026thinsp;1. Sentences followed the structure \u0026lsquo;There are [Number] [Colour] [Animal]\u0026rsquo; (lowercase: \u0026lsquo;There are eight orange elephants\u0026rsquo;; uppercase: \u0026lsquo;THERE ARE EIGHT ORANGE ELEPHANTS\u0026rsquo;; mixed-case: \u0026lsquo;ThErE aRe EiGhT oRaNgE eLePhAnTs\u0026rsquo;; LEET: \u0026lsquo;TH3R3 4R3 31GHT 0R4NG3 3L3PH4NTS\u0026rsquo;). The presentation order was randomized for each participant. All participants read each of the 108 total sentences once. In addition to the sentences, square icons representing the three animals, three numbers and three colours were built to use in the comprehension check after each sentence (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below).\u003c/p\u003e\n\u003ch3\u003eTask and procedure\u003c/h3\u003e\n\u003cp\u003eThe experiment was conducted using Gorilla (Anwyl-Irvine et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), an online experiment builder and testing platform that allowed for accuracy and reading times to be automatically recorded. All sentences were displayed in Courier font, size 55. Participants completed the task within a maximum of 45 minutes, including a scheduled break halfway through the experiment.\u003c/p\u003e \u003cp\u003eThe procedure began with an instruction screen that clearly explained the task. This was followed by a short practice phase consisting of four trials\u0026mdash;one for each typographic format\u0026mdash;to familiarize participants with the procedure. Upon completing the practice, a screen informed participants that the experimental task was about to begin. A progress bar was displayed throughout the experiment to indicate how much of the task remained.\u003c/p\u003e \u003cp\u003eEach trial followed a fixed timeline. First, a fixation cross appeared at the centre of the screen for 1500 ms to focus participants\u0026rsquo; attention. Immediately afterward, the target sentence was presented along with the prompt \"Press after reading.\" This prompt remained visible throughout the sentence display. Participants had a maximum of 3000 ms to read the sentence and press the spacebar. If they did not press the spacebar within this time, the sentence disappeared automatically and the experiment advanced to the next phase.\u003c/p\u003e \u003cp\u003eFollowing the reading phase, a comprehension check was conducted through three successive screens. In each screen, participants had to identify one of the key elements mentioned in the sentence: a number, a colour, or an animal. All the possible options for each element were displayed on each screen, and participants were required to select the one that corresponded to the sentence they had just read by clicking on the correct option. Each response screen had a maximum duration of 5000 ms; if no response was provided within this time, the trial was marked as a timeout. Two dependent variables were analysed: reading time and comprehension accuracy. Reading times were recorded as the latency between sentence onset and the participant\u0026rsquo;s keypress. Comprehension accuracy was calculated as the percentage of correct selections across the three comprehension questions presented in each trial\u0026mdash;i.e., a trial was considered correctly answered only if all three questions pertaining to that trial were answered correctly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAccuracy\u003c/h2\u003e \u003cp\u003eWhile our analyses focus predominantly on reading times, measuring accuracy was a necessary check to ensure that participants were reading the sentences. Sentence comprehension was considered accurate only when all three questions\u0026mdash;regarding number, colour and animal\u0026mdash; were answered correctly. From the original sample of 303 participants, three were excluded due to low accuracy scores, \u0026lt; 67% (i.e., fewer than 2/3 questions correct), which indicated a lack of attention to the task.\u003c/p\u003e \u003cp\u003eAs expected, the proportion of accurate trials in the image selection task was high: for trials with lowercase text it was 0.966 (SD\u0026thinsp;=\u0026thinsp;0.055), for uppercase it was 0.960 (SD\u0026thinsp;=\u0026thinsp;0.067), for mixed-case it was 0.966 (SD\u0026thinsp;=\u0026thinsp;0.061), and for LEET it was 0.943 (SD\u0026thinsp;=\u0026thinsp;0.084). This shows that, overall, participants were able to read and understand the simple sentences regardless of format, and that they were paying attention to the task.\u003c/p\u003e \u003cp\u003eGiven the numerical differences across formats, we conducted statistical analyses exploring the effects of presentation format and participant age through a generalized linear mixed model with a binomial family, using the function glmer in the \u003cem\u003eR\u003c/em\u003e package \u003cem\u003elme4\u003c/em\u003e (Bates et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and under the philosophy of reporting maximal random-effects structure justified by the design (Barr et. al., 2013), but reporting only the models that converged and were not singular:\u003c/p\u003e \u003cp\u003eglmer(Accuracy\u0026thinsp;~\u0026thinsp;Age * Format + (1\u0026thinsp;+\u0026thinsp;Format | Participant) + (1\u0026thinsp;+\u0026thinsp;Format || Item), family\u0026thinsp;=\u0026thinsp;binomial)\u003c/p\u003e \u003cp\u003eAccuracy data (n\u0026thinsp;=\u0026thinsp;129,600 observations) were assessed through a generalized mixed-effects model analysis. Descriptive statistics by presentation format are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The model revealed a significant main effect of Age (centred), χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;9.73, \u003cem\u003ep\u003c/em\u003e = .002, demonstrating that accuracy decreased with age (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Also, a significant main effect was found for Format, χ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;15.46, \u003cem\u003ep\u003c/em\u003e = .001. No significant interaction was found, χ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;0.60, \u003cem\u003ep\u003c/em\u003e = .895, suggesting that differences in accuracy across formats were consistent across age groups, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn this model, LEET served as the reference level for the Format factor. The model revealed that comprehension accuracy in sentences displayed in LEET format was significantly lower than lowercase and mixed-case, and marginally lower than uppercase (LEET vs. lowercase: β = -0.45, SE = 0.14, z = -3.31, \u003cem\u003ep\u003c/em\u003e = 0.006 ; LEET vs. uppercase: β = -0.35, SE = 0.14, z = -2.63, \u003cem\u003ep\u003c/em\u003e = 0.051; LEET vs. mixed-case: β = -0.47, SE = 0.14, z = -3.47, \u003cem\u003ep\u003c/em\u003e = 0.003).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"char\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eProportion corrects and mean Reading Times (ms) by presentation format.\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFormat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean RT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD (by participant)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLowercase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUppercase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed-Case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote.\u003c/strong\u003e SD for proportion correct computed across participants; SD for reading times computed by participant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eReading Times\u003c/h2\u003e\n \u003cp\u003eReading times associated with correct responses were analyzed. In addition, reading times shorter than 250ms (0.74% of all trials) and longer than 2500ms (2% of all trials) were eliminated.\u003c/p\u003e\n \u003cp\u003eTable 1 shows the mean and standard deviation (calculated across participants) for the four presentation formats. Reading time was fastest for lowercase and slowest for LEET, with uppercase and mixed-case clustering closer to lowercase (21 and 50 ms slower than lowercase, respectively, vs. 255 ms slower in the case of LEET). These analyses were followed by exploratory data visualizations to examine the distributional features of the observed effects via delta plots.\u003c/p\u003e\n \u003cp\u003eTo analyze the effects of presentation format and participant age on reading times, we used a linear mixed-effects model using the lmer() function with the \u003cem\u003elmerTest\u003c/em\u003e package in \u003cem\u003eR\u003c/em\u003e (Kuznetsova et al., 2017).\u003c/p\u003e\n \u003cp\u003eLmer(RT ~ Age * Format + TrialOrder + (1 | Participant) + (1 | Item)\u003c/p\u003e\n \u003cp\u003eIn this model, Format, Age and their interaction were specified as fixed effects, and random intercepts were included for Participant and Item. LEET served as the reference level for the Format factor. Trial Order (centered) was included in the model as a covariate to control for potential practice or fatigue effects over the course of the session. We retained a random-intercepts-only structure for subjects and items because more complex random-effects specifications led to singular fits, indicating that the data do not support estimating additional variance components reliably.\u003c/p\u003e\n \u003cp\u003eReaction Time data (n = 26,226 observations) were analysed through a linear mixed-model analysis. Results showed a significant main effect of Age (centred), \u003cem\u003eF\u003c/em\u003e(1, 262.7) = 22.48, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, demonstrating that reaction times increased as a function of age. Furthermore, a significant main effect of Format was found, \u003cem\u003eF\u003c/em\u003e(3, 102.9) = 118.95, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Reading times in sentences displayed in LEET format were significantly slower than in any other format (LEET vs. lowercase: β = -273.3, SE = 16.38, t(103.2) = -16.68, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; LEET vs. uppercase: β = -250.6, SE = 16.38, t(103.3) = -15.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; LEET vs. mixed-case: β = -225.72, SE = 16.38, t(103.3) = -13.77, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). A significant main effect of Trial Order was also observed, \u003cem\u003eF\u003c/em\u003e(1, 25,867.7) = 1627.16, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, showing that participants identified the words faster over the course of the experiment. Notably, a significant Age × Format interaction was found, \u003cem\u003eF\u003c/em\u003e(3, 25,840.7) = 28.77, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, indicating that age-related slowing was more pronounced for the LEET format than for the other formats. In other words, while reading times increased with age across all formats, age-related slowing was particularly pronounced for the LEET format compared to the others. Figure 3 illustrates this pattern, showing a clear separation between LEET and the remaining formats, as well as a general increase in reading times with age. Follow-up analyses showed that the slopes relating Age to reading time were significantly less steep for lowercase (β = − 2.11, SE = 0.28, \u003cem\u003et\u003c/em\u003e (25840) = − 7.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), uppercase (β = − 2.27, SE = 0.28, \u003cem\u003et\u003c/em\u003e (25840) = − 7.99, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and mixed-case (β = − 2.12, SE = 0.28, \u003cem\u003et\u003c/em\u003e (25840) = − 7.47, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). To directly estimate the age slope for LEET, the model was reparameterized with Lowercase as the reference level, revealing a significant increase in reading time with age for LEET sentences (β = 2.11, SE = 0.28, \u003cem\u003et\u003c/em\u003e(25,840) = 7.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe goal of this study was to examine how variations in typographical formats (lowercase, uppercase, mixed-case, LEET) affect reading times and comprehension across the adult lifespan (18\u0026ndash;84 years), building on established effects like lowercase superiority (Perea et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and case-mixing costs (Mayall et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). As noted in the introduction, it was expected that LEET would involve longer reading times, suggesting a higher cognitive load compared to conventional formats (lowercase, uppercase, mixed-case).\u003c/p\u003e \u003cp\u003eThe results confirmed these predictions. Conventional formats yielded the fastest reading times and high accuracy, mixed-case imposed moderate costs, and LEET generated substantial disruptions in reading, accompanied by slightly reduced comprehension accuracy comprehension. Although accuracy remained high across all formats, reading sentences in the LEET format resulted in reliably lower comprehension across age groups.\u003c/p\u003e \u003cp\u003eCrucially, analyses of reading times revealed that the disruptive effect of LEET increased with age, whereas age-related slowing was less pronounced for conventional formats, yielding a significant Format \u0026times; Age interaction that would not be observable in age-homogeneous samples. These results are consistent with prior research documenting the lowercase superiority effect, whereby lowercase words are processed faster and more efficiently than uppercase ones (Perea et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vergara-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perea et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). They also align with evidence that orthographic distortions, such as case mixing, disrupt reading fluency by breaking familiar visual regularities and increasing processing demands (Arditi \u0026amp; Cho, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mayall et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Tinker, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1963\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond mean effects, delta-plot analyses further clarified the nature of these processing costs. Vincentile-based delta plots comparing LEET with lowercase, uppercase, and mixed-case formats showed consistently positive deltas across quantiles, indicating slower reading times for LEET sentences throughout the RT distribution. Notably, the LEET\u0026ndash;mixed-case contrast exhibited steeper slopes toward slower responses\u0026mdash;particularly in older participants\u0026mdash;suggesting that LEET engages processing mechanisms that place disproportionate demands on perceptual encoding and integration as reading unfolds. This pattern extends masked priming findings, in which visually similar LEET primes can activate lexical representations (Carreiras et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kinoshita et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), to naturalistic reading contexts where age-related constraints amplify the costs of orthographic distortion.\u003c/p\u003e \u003cp\u003eThese differences illuminate how distortions differentially tax visual word recognition mechanisms: while mixed-case preserves abstract letter identity access through standard alphabetic characters (Fournet et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), LEET imposes additional symbol-to-letter recoding demands, as evidenced by delta plots showing costs magnified for slower responses (Perea et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Vincentile-based delta plots (0.1, 0.3, 0.5, 0.7, 0.9) of correct responses comparing LEET (baseline) with lowercase, uppercase, and mixed-case formats confirmed this pattern across all quantiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), revealing consistently positive deltas indicating slower LEET reading times. Specifically, the LEET\u0026ndash;lowercase contrast showed a positive slope with increasing costs at longer RTs, suggesting greater cognitive demand in resolving orthographic distortions; the LEET\u0026ndash;uppercase comparison exhibited relatively stable deltas, indicating uniform cost; and the LEET\u0026ndash;mixed-case comparison displayed an upward slope toward slower RTs\u0026mdash;particularly amplified in older participants\u0026mdash;pointing to qualitatively distinct processing mechanisms for LEET as reading unfolds. This extends masked priming findings\u0026mdash;where visually similar LEET primes activate lexical representations (Carreiras et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kinoshita et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), to naturalistic reading, where contextual integration amplifies age-related cognitive demands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAge modulated these format effects in a manner consistent with prior evidence of heightened vulnerabilities in older adults' under visually demanding conditions, particularly at early perceptual and pre-lexical stages of processing (Allen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mayall, 2001, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) rather than reflecting age-related changes in lexical representations per se (Perea et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Specifically, disruption in the LEET format amplified with age\u0026mdash;evidenced by steeper delta plot slopes in LEET\u0026ndash;mixed-case contrasts toward slower reading times\u0026mdash;while conventional formats (lowercase and uppercase) showed comparatively smaller age-related costs and more stable delta patterns.\u003c/p\u003e \u003cp\u003eThis Format \u0026times; Age interaction indicates that aging exacerbates the impact of orthographic irregularities that degrade visual regularities feeding into the lexical system, without implying a general decline in lexical processing. In this sense, LEET appears to place disproportionate demands on perceptual encoding and integration mechanisms that precede lexical access, thereby revealing age-related differences that are not apparent for visually canonical formats in age-homogeneous samples.\u003c/p\u003e \u003cp\u003eThese results suggest that the mechanisms underlying LEET processing differ qualitatively from those involved in case mixing. Whereas mixed-case disrupts global visual regularity, LEET requires an additional layer of symbol-to-letter recoding that particularly taxes older readers, whose analytic processing resources are more limited (Allen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Age effects were most pronounced for LEET: while younger readers tolerated LEET through flexible letter identity normalization (likely reflecting both digital exposure and preserved early visual processing), older adults showed disproportionate slowdowns and comprehension losses. This interpretation is further supported by delta plot analyses, which revealed that the cost of LEET increased disproportionately for slower responses, and aligns with lifespan shifts toward holistic, vocabulary-supported routes where compensatory lexical gains (Keuleers et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aguasvivas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) preserve comprehension but cannot offset early perceptual demands (Spieler \u0026amp; Balota, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Cohen-Shikora \u0026amp; Balota, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Consequently, while confirming established concepts like lowercase advantage and mixed-case disruption, our findings extend the literature by demonstrating how LEET's unique processing demands interact with age-related declines in peripheral orthographic encoding.\u003c/p\u003e \u003cp\u003eFrom a practical perspective, these findings underscore the challenges that unconventional formats such as LEET impose on reading efficiency and comprehension, particularly for older adults. Unlike children's gradual abstraction of case-invariant letter identities across conventional formats (Grainger, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), adult aging reveals limits to this abstraction under extreme orthographic distortion: while developmental gains reflect experience-driven parallelization of skilled reading (Acha \u0026amp; Perea, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Perea et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), aging reverses this efficiency for visually irregular formats like LEET. Although our data did not directly measure participants\u0026rsquo; prior exposure to LEET, the consistently slower reading times and reduced accuracy (disproportionate in older adults) suggest that familiarity alone may not be sufficient to compensate for LEET's demands on residual analytic resources beyond what compensatory lexical mechanisms can offset (Vergara-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNaturally, this study contains some limitations. First, the experimental materials consisted of short, artificial sentences and limited LEET substitutions (A\u0026thinsp;=\u0026thinsp;4, E\u0026thinsp;=\u0026thinsp;3, I\u0026thinsp;=\u0026thinsp;1), constraining ecological validity since real LEET use involves diverse, context-specific substitutions encountered in chat messages and gaming environments. Second, participants' prior familiarity with LEET was not measured, even though previous exposure, and age as its proxy, could plausibly modulate both accuracy and reading times, limiting causal attribution between format per se and processing costs.\u003c/p\u003e \u003cp\u003eFuture work should therefore incorporate more ecological tasks, such as real chat messages or online conversations, to better approximate the contexts in which LEET is typically encountered. Moreover, combining behavioural measures with eye-tracking and EEG would provide a more precise characterization of the perceptual and lexical integration processes involved. In addition, a forthcoming study with Spanish participants residing in Morocco will explore these aspects in individuals regularly exposed to Darija, a dialect of Moroccan Arabic whose writing system incorporates the LEET format. Such a population offers a valuable opportunity to determine whether sustained exposure to LEET reduces its processing costs, thereby clarifying the extent to which familiarity and linguistic environment can mitigate the cognitive challenges posed by unconventional orthographic formats across development, adulthood, and aging.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates graded orthographic processing costs (conventional formats\u0026thinsp;\u0026lt;\u0026thinsp;mixed-case\u0026thinsp;\u0026lt;\u0026thinsp;LEET) that interact with age across the adult lifespan (18\u0026ndash;84 years), confirming established effects like lowercase superiority and case-mixing disruption while revealing LEET's unique symbol-to-letter recoding demands. By tracing format effects from young adulthood to advanced age, these findings integrate developmental, adult, and aging research, highlighting analytic processing decline as the critical vulnerability to digital orthographies. The current study not only contributes to the basic science of reading, but results advance equitable typography guidelines for education, digital design, and communication contexts that minimize age-related barriers across digital ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis research has been partially funded by grant\u0026nbsp;PID2024-161331NB-I00 (MCINN/AEI/10.13039/501100011033) and PRE2022-104069.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was partially funded by the State Plan for Scientific and Technical Research and Innovation of the Government of Spain with grant numbers PRE2022-104069 and PID2024-161331NB-I00, as well as the Research Council of Norway, grant number 326487-FORSKER2, and the Community of Madrid (grant number 020-T1/HUM-20037).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcha, J. \u0026amp; Perea, M. 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The time course of the lowercase advantage in visual word recognition: An ERP investigation. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e146\u003c/b\u003e, 107556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuropsychologia.2020.107556\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropsychologia.2020.107556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Typographic formats, Reading efficiency, Orthographic distortions, LEET, Lifespan, Reading comprehension","lastPublishedDoi":"10.21203/rs.3.rs-9093730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9093730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTypographic formats influence reading efficiency; however, knowledge remains limited regarding how these effects change across the lifespan, especially for orthographic distortions in digital environments. This study examines how conventional formats (lowercase and uppercase) and unconventional formats (mixed-case and LEET) affect reading comprehension and reaction times. Three hundred and three adults (18\u0026ndash;84 years) read short sentences (five words) presented in the four formats, while reading times and comprehension accuracy were recorded. The results showed a graded cost pattern: conventional formats yielded the fastest reading times, mixed-case imposed moderate costs, and LEET produced the greatest slowdown and a slight reduction in accuracy. Moreover, a significant interaction between format and age was observed: although reading slowed with age in all formats, this effect was especially pronounced for LEET. These findings suggest that extreme orthographic distortions increase perceptual and pre-lexical demands, revealing limits in reading adaptation associated with aging.\u003c/p\u003e","manuscriptTitle":"Age-Related Differences in Processing Unconventional Text Formats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:14:56","doi":"10.21203/rs.3.rs-9093730/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T08:55:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T03:44:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T15:44:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86716916417169459282964699901466791408","date":"2026-03-18T22:14:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29299569322364489222242675994364236030","date":"2026-03-17T17:43:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T22:33:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T10:27:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T11:22:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T11:21:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-11T11:10:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3aa8527-4b99-45c6-abad-fd0f954b77d5","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64674830,"name":"Biological sciences/Neuroscience"},{"id":64674831,"name":"Biological sciences/Psychology"},{"id":64674832,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-18T23:23:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:14:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9093730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9093730","identity":"rs-9093730","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}